# Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation

Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Fabio Poiesi,
and Elisa Ricci C. Saltori, N. Sebe and E. Ricci are with the Dept. of
Information Engineering and Computer Science, University of Trento, Italy.
E-mails<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>F. Galasso is with the Dept. of Computer Science, Sapienza University of Rome,
Italy. G. Fiameni is with NVIDIA AI Technology Center, Italy. F. Poiesi and E.
Ricci are with Fondazione Bruno Kessler (FBK), Trento, Italy. E-mail:
<EMAIL_ADDRESS>
Manuscript received April 19, 2005; revised August 26, 2015.

###### Abstract

Deep-learning models for 3D point cloud semantic segmentation exhibit limited
generalization capabilities when trained and tested on data captured with
different sensors or in varying environments due to domain shift. Domain
adaptation methods can be employed to mitigate this domain shift, for
instance, by simulating sensor noise, developing domain-agnostic generators,
or training point cloud completion networks. Often, these methods are tailored
for range view maps or necessitate multi-modal input. In contrast, domain
adaptation in the image domain can be executed through sample mixing, which
emphasizes input data manipulation rather than employing distinct adaptation
modules. In this study, we introduce compositional semantic mixing for point
cloud domain adaptation, representing the first unsupervised domain adaptation
technique for point cloud segmentation based on semantic and geometric sample
mixing. We present a two-branch symmetric network architecture capable of
concurrently processing point clouds from a source domain (e.g. synthetic) and
point clouds from a target domain (e.g. real-world). Each branch operates
within one domain by integrating selected data fragments from the other domain
and utilizing semantic information derived from source labels and target
(pseudo) labels. Additionally, our method can leverage a limited number of
human point-level annotations (semi-supervised) to further enhance
performance. We assess our approach in both synthetic-to-real and real-to-real
scenarios using LiDAR datasets and demonstrate that it significantly
outperforms state-of-the-art methods in both unsupervised and semi-supervised
settings.

###### Index Terms:

Domain adaptation, unsupervised learning, semi-supervised learning, semantic
segmentation, point cloud.

##  1 Introduction

LIDAR is currently the most suitable sensor for capturing accurate 3D
measurements of an environment for autonomous driving [1] and robotic
navigation [2]. Semantic scene understanding is a crucial component for AI-
based perception systems [3]. LiDAR measurements can be analyzed in the form
of 3D point clouds, with point cloud semantic segmentation used to assign a
finite set of semantic labels to the 3D points [4]. To train accurate deep
learning models, large-scale datasets with point-level annotations are
necessary [5, 6, 7]. This involves a costly and labor-intensive data
collection process, as point clouds need to be captured in the real world and
manually annotated. An alternative is to use synthetic data, which can be
conveniently generated with simulators [8]. However, deep neural networks are
known to suffer from domain shift when trained and tested on data from
different domains [8]. Although simulators can reproduce the acquisition
sensor with high fidelity, further research is still required to address such
domain shift [9].

Data augmentation techniques based on the combination of samples and their
labels, such as Mixup [10] or CutMix [11], have been proposed to enhance deep
network generalization. The underlying concept involves mixing samples to
expand the training set and reduce overfitting. These methods were initially
applied to image classification tasks and later adapted for domain adaptation
and domain generalization in image recognition [12, 13]. Similar ideas have
also been successfully extended to 2D semantic segmentation [14, 15]. While
Unsupervised Domain Adaptation (UDA) for semantic segmentation in the image
domain has been extensively studied [16, 15, 14, 17, 18], less attention has
been devoted to developing adaptation techniques for point cloud segmentation.
Point cloud UDA can be addressed in the input space [9, 19] with dropout
rendering [19] or adversarial networks [9], or in the feature space through
feature alignment [20]. A few studies have proposed exploiting sample mixing
for point cloud data [21, 22], but they target different applications than UDA
for semantic segmentation.

In this paper, we present a novel domain adaptation framework for 3D point
cloud segmentation, named CoSMix, which extends the approach presented in [23]
to the semi-supervised settings (SSDA). CoSMix is designed to mitigate the
domain shift by mixing semantically-informed groups of points (patches) across
domains. Specifically, we design a two-branch symmetric deep neural network
pipeline that concurrently processes point clouds from a source domain (e.g.
synthetic or real) and point clouds from a target domain (e.g. real or real
but captured with a different sensor). Target point clouds can be either
unlabeled or partially labeled if one wants to use CoSMix for UDA or SSDA,
respectively. Each branch is domain specific, i.e. the source branch is in
charge of mixing a source point cloud with selected patches of a target point
cloud, and vice versa for the target branch. We formulate mixing as a
composition operation, which is similar to the concatenation operation
proposed in [21, 22], but unlike them, we leverage the semantic information to
mix domains. Patches from the source point cloud are selected based on the
semantic labels of their points. Patches from the target point cloud can be
selected based on the predicted semantic pseudo-labels in the case of UDA and
based on human annotations in the case of SSDA. We will show that only a
handful of manually annotated points are sufficient to significantly improve
the domain adaptation performance. When patches are mixed across domains we
apply data augmentation both at local and global semantic levels to boost the
efficacy of the mixing. An additional key difference between our method and
[21, 22] is the teacher-student learning scheme that we implement to improve
the accuracy of the pseudo-labels. We evaluate CoSMix on large scale point
cloud segmentation benchmarks, featuring both synthetic and real-world data,
in several directions such as synthetic to real and real to real.
Specifically, we use the following datasets: SynLiDAR [9], SemanticPOSS [6],
SemanticKITTI [5], and nuScenes [7]. Our results show that CoSMix can reduce
the domain shift, outperforming state-of-the-art methods in both UDA and SSDA
settings. We perform detailed analyses of CoSMix and an ablation study of each
component, highlighting its strengths and discussing its limitations.

This paper extends our earlier work [23] in several aspects. We extend the
original CoSMix in order to tackle the SSDA setup. The current design allows a
user to input a few annotated points to significantly improve the semantic
segmentation performance on the target domain. Then, we significantly extend
our experimental evaluation and analysis by adding new experiments, new
comparisons, and new ablation studies to evaluate this new setup. We extend
the related work by thoroughly reviewing additional state-of-the-art
approaches, and summarizing these approaches in a comprehensive table that
highlights key contributions and setups. Lastly, the code is available at
https://github.com/saltoricristiano/cosmix-uda.

##  2 Related work

TABLE I:  Overview of existing methods for unsupervised (UDA) and semi-
supervised (SSDA) adaptation in point cloud segmentation. For each approach,
we report the sensor setup (Setup), the architecture (Input data type and
Model), and the source and target datasets. Then, we classify the adaptation
strategy into mixup based, adversarial learning based, alignment based,
generative based, self-training based and auxiliary task based. Furthermore,
we report whether the implementation (Code) is publicly available.

Method | Setup | Architecture | Datasets | Settings | Adaptation | Code
---|---|---|---|---|---|---
Input data | Model | Source | Target | UDA | SSDA | Mixup | Adv. | Align. | Gen. | Self-train. | Aux. task
RayCast [24] | real-to-real | RV | RangeNet++ [25] | Sem.KITTI [5] | nuSc. [7] | ✓ |  |  |  | ✓ | ✓ |  |  |
ePointDA [19] | synth-to-real | RV | SqueezeSegV2 [20] | GTA-V [20] | KITTI [26] | ✓ |  |  | ✓ | ✓ | ✓ |  |  |
Sem.KITTI [5] |  |  |  |  |
SqueezeSegV2 [20] | synth-to-real | RV | SqueezeSegV2 [20] | GTA-V [20] | KITTI [26] | ✓ |  |  |  | ✓ |  |  |  | ✓
Gated [27] | synth-to-real | RV | SalsaNext [28] | GTA-V [20] | KITTI [26] | ✓ |  |  |  |  | ✓ |  | ✓ |
real-to-real | nuScenes [7] |  |  |  |  |  |
KITTI [26] | nuScenes [7] |  |  |  |  |  |
xMUDA [29] | real-to-real | 2D&3D | xMUDA [29] | nuSc. [7] | nuSc.[7] | ✓ |  |  |  | ✓ |  | ✓ |  | ✓
KITTI [30] | KITTI [30] |  |  |  |  |
A2D2 [31] | A2D2 [31] |  |  |  |  |
Cross-modal [32] |  | 2D&3D | xMUDA [29] | nuSc. [7] |  | ✓ | ✓ |  |  | ✓ |  | ✓ |  | ✓
real-to-real | v.KITTI [33] | nuSc. [7] |  |  |  |
synth-to-real | A2D2 [31] | Sem.KITTI [5] |  |  |  |
| Sem.KITTI [5] | Waymo [34] |  |  |  |
| Waymo [34] |  |  |  |  |
Complete&Label [35] | real-to-real | 3D | SparseConv [36] | KITTI [26] | KITTI [26] | ✓ |  |  |  |  | ✓ |  |  |
Waymo [34] | Waymo [34] |  |  |  |  |  |  |
nuSc. [7] | nuSc. [7] |  |  |  |  |  |  |
PCT [9] | synth-to-real | 3D | Minkowski [4] | SynLiDAR [9] | Sem.KITTI [5] | ✓ | ✓ |  |  |  | ✓ |  |  | ✓
Sem.POSS [6] |  |  |  |  |
CoSMix [23] | synth-to-real | 3D | Minkowski [4] | SynLiDAR [9] | Sem.KITTI [5] | ✓ |  | ✓ |  |  |  | ✓ |  | ✓
Sem.POSS [6] |  |  |  |  |
Ours | real-to-real | 3D | Minkowski [4] | SynLiDAR [9] | Sem.KITTI [5] | ✓ | ✓ | ✓ |  |  |  | ✓ |  | ✓
synth-to-real | SemKITTI [5] | Sem.POSS [6] |  |  |  |
|  | nuScenes [7] |  |  |  |

Point cloud semantic segmentation. Point cloud semantic segmentation can be
performed at point level [37], on range views [38] or on a voxelized point
clouds [39]. Point-level architectures process the input point cloud without
the need for intermediate representation processing. This architectures
include PointNet [40], which is based on a series of multilayer perceptrons.
PointNet++ [37] improves on PointNet by aggregating global and local point
features at multiple scales. RandLA-Net [41] extends PoinNet++ [37] by
embedding local spatial encoding, random sampling and attentive pooling.
KPConv [42] learns weights in the continuous space, and introduces flexible
and deformable convolutions for point cloud processing. These methods are
computationally inefficient when large-scale point clouds are processed.
Computational efficiency can be improved by projecting 3D points on 2D
representations [25] or by using 3D quantization approaches [4]. The former
includes 2D projection-based approaches that use 2D range maps and exploit
standard 2D convolution filters [38] to segment these maps prior to a re-
projection in the 3D space. RangeNet++ [25], SqueezeSeg networks [43, 20],
3D-MiniNet [44] and PolarNet [45] are approaches that belong to this category.
Although these approaches are efficient, they tend to lose information when
the input data are projected in 2D and re-projected in 3D. The latter includes
3D quantization-based approaches that transform the input point cloud into a
3D discrete representations, and that employ 3D convolutions [39] or 3D sparse
convolutions [36, 4] to predict per-point classes. VoxelNet [39] maps input
points into a voxel-grid and processes the input voxel-grid with 3D
convolutions. SparseConv [36, 46] and MinkowskiNet [4] improves voxel
processing and introduce sparse convolutions to improve efficiency. Cylinder3D
[47] further improves voxel processing for LiDAR data by using cylindrical and
asymmetrical 3D convolutions. In our work, we use MinkowskiNet [4], which
provides a trade off between accuracy and efficiency.

Sample mixing for 2D domain adaptation. Unsupervised domain adaptation (UDA)
for image-based tasks is a well-studied problem [48], and there exist several
methods using domain adversarial learning [49, 50, 51], regularization losses
[52, 50, 52], self-training [17, 53], multi-task learning [54], and curriculum
learning [55]. Mixup can also be used for domain adaptation [10, 11, 56]. For
2D semantic segmentation, several recent works have used sample mixing for
domain adaptation, including BAPA-Net [57], CAMix [58], DACS [14], DSP [15],
and DAFormer [59]. BAPA-Net employs a boundary-informed mixing strategy, while
CAMix proposes a context-aware mask generation for domain mixing. DACS extends
ClassMix [56] for domain adaptation by pasting specific source classes into
target images. DSP improves on DACS by introducing self-training, soft-labels,
and a double mixing strategy. Unlike these works, our method tackles UDA for
3D semantic segmentation. It should be noted that extending image mixing
strategies to point clouds is not as straightforward, as point clouds are
sparse in nature. Therefore, we introduce a novel mixing strategy specifically
designed for 3D point clouds.

Domain adaptation for point cloud segmentation. Unlike domain adaptation for
image-based tasks [48, 60], domain adaptation for point cloud segmentation
still lacks a unified experimental setup to compare different approaches. We
review domain adaptation approaches for point cloud segmentation by grouping
them into range-view methods, multi-modal (2D&3D) methods, and 3D-focused
methods. Tab. I provides a detailed summary of these approaches.

Range-view (RV) images are computed through a cylindrical projection of the
input point cloud onto a 2D plane. After projection, RV images can be
processed with existing 2D convolution networks. RV-based networks are
affected by domain shift, which can be mitigated by using generative
approaches [24, 19], feature alignment [24, 20, 19], and contrastive learning
[27]. RayCast [24] tackles the real-to-real UDA problem by transferring the
sensor pattern of the target domain to the source domain through ray casting.
After training the deep network on the source data, a minimal-entropy
correlation alignment loss is used to reduce domain shift [61]. SqueezeSegV2
[20] improves the SqueezeSeg [43] architecture, and reduces domain shift in
the synth-to-real setup by aligning source and target features with a geodesic
correlation alignment [61]. ePointDA [19] addresses domain shift in the synth-
to-real UDA setup at both input level and feature level. At input level, a
generative CycleGAN [62] is trained to simulate real sensor noise on synthetic
source data. At feature level, a higher-order momentum loss [63] is used to
learn domain agnostic features between source and target input data. Gated
[27] states that domain shift between source and target point clouds can be
mitigated by solving the sparsity shift and by introducing domain specific
parameters. Given an input pair of source an target RV images, they first
solve the sparsity difference thorough self-supervised completion and by
applying a dropout mask. Then, residual gated adapters are added to the
segmentation model to learn target specific parameters. None of the RV-based
methods tackle the semi-supervised scenario.

Multi-modal models are designed to process the information captured by
multiple input sensors, e.g. RGB cameras and LiDAR sensors are those typically
used. Domain shift is tackled by enforcing prediction consistency among
modalities and domains, and by using target (pseudo) labels. xMUDA [29] uses
cross-modality and cross-domain consistency to learn a domain agnostic model
in the real-to-real UDA setup. Cross-modal consistency exploits source labels
and target pseudo-labels to produce consistent multi-modal predictions in both
the domains. DeepCORAL feature alignment [64] is used to enforce feature
alignment between source and target domains. In [32], xMUDA is extended to
SSDA settings showing that cross-modal consistency is effective even in the
semi-supervised settings.

3D methods can process input point clouds with or without prior voxelization.
UDA approaches for 3D segmentation include voxel-based architectures such as
SparseConv [46] and MinkowskiNet [4]. Domain shift can be tackled by focusing
on the problem of sparsity [35, 9] or by employing mix up strategies [23].
Complete&Label [35] reduces the sparsity difference between real domains by
formulating the domain adaptation problem as a point cloud completion (or
densification) problem. A self-supervised completion network is trained to
make the sparse input point cloud denser. The pre-processed point clouds can
then be used as intermediate domains in order to lower the domain shift. PCT
[9] disentangles domain shift between synthetic and real point clouds into
appearance and sparsity. Then, PCT learns an appearance translation module and
a sparsity translation module. These modules are used for translating source
data in the target modality. Translated data are then used together with ST
[18] and APE [65] in the UDA and SSDA settings, respectively.

CoSMix [23] is a method that reduces domain shift in point cloud data by
introducing a compositional semantic mixup strategy with a teacher-student
learning scheme. The method obtains domain-invariant models/features by
creating two new intermediate domains of composite point clouds: a mixed
source and a mixed target. In the mixed target, source instances pull the
target domain closer to the source domain, preventing overfitting from noisy
pseudo-labels. In the mixed source, target instances (pseudo-labels) bring the
target modality into the source domain, pulling the source domain closer to
the target domain. The teacher-student learning scheme enables the iterative
improvement of pseudo-labels, progressively reducing the domain gap. In this
work, we extend CoSMix [23] to the SSDA settings by allowing target labels to
be mixed in the source and target (unlabeled) point clouds while improving
adaptation. We also show how a small amount of target supervision can
significantly improve the adaptation performance.

##  3 Compositional Semantic Mix (CoSMix)

###  3.1 Preliminaries and definitions

Figure 1: Block diagram of CoSMix detailing the UDA and SSDA settings. The UDA
setting uses the top and bottom branch (red line). The SSDA setting uses also
the middle branch in addition to those used in UDA (gray line). In the top
branch, the input source point cloud $\mathcal{X}^{s}$ is mixed with the
unsupervised target point cloud $\mathcal{X}^{t}_{\textsf{U}}$ obtaining
$\mathcal{X}^{t\rightarrow s}$. In the bottom branch, the input target point
cloud $\mathcal{X}^{t}_{\textsf{U}}$ is mixed with the source point cloud
$\mathcal{X}^{s}$ obtaining $\mathcal{X}^{s\rightarrow t}$. In the SSDA
setting, the labeled target data $\mathcal{X}^{t}_{\textsf{L}}$ are mixed with
the source point cloud $\mathcal{X}^{s}$ and with the unsupervised target
point cloud $\mathcal{X}^{t}_{\textsf{U}}$. A teacher-student learning
architecture is used in both the UDA and SSDA settings to improve pseudo-label
accuracy while adapting over target domain. This is achieved by updating the
teacher network through Exponential Moving Average (EMA). Semantic Selection
($f$ and $g$) selects subsets of points (patches) to be mixed based on the
source labels $\mathcal{Y}^{s}$, the target labels
$\mathcal{Y}^{t}_{\textsf{L}}$, and target pseudo-labels
$\hat{\mathcal{Y}^{t}_{\textsf{U}}}$ information. Compositional Mix applies
local $h$ and global $r$ augmentations and mixes the selected patches among
domains.

CoSMix implements a teacher-student learning scheme that exploits the
supervision from the source domain, the self-supervision from the target
domain and, if available, the supervision from a few labeled target samples to
improve the semantic segmentation on the target domain. Our method is trained
on two different mixed point cloud sets. The first is the composition of the
source point cloud with pseudo-labeled portions of points, or patches, of the
unlabeled target point cloud. Target patches bring the target modality in the
source domain making the altered source domain more similar to the target
domain. The second is the composition of the unlabeled target point cloud with
randomly selected patches of the source point cloud. Source patches make the
altered target domain more similar to the source domain, preventing
overfitting from noisy pseudo-labels. If available, labeled points of the
target point clouds can also be used in both the mixed point cloud sets. This
target supervision can further reduce domain shift. The teacher-student
learning scheme iteratively improves pseudo labels, progressively reducing the
domain gap. Fig. 1 illustrates the block diagram of CoSMix.

Let $\mathcal{S}=\\{(\mathcal{X}^{s},\mathcal{Y}^{s})\\}$ be the source
dataset that is composed of $N^{s}=|\mathcal{S}|$ labeled point clouds, where
$\mathcal{X}^{s}$ is a point cloud and $\mathcal{Y}^{s}$ is its point-level
labels, and $|.|$ is the cardinality of a set. Labels take values from a set
of semantic classes $\mathcal{C}=\\{c\\}$, where $c$ is a semantic class. Let
$\mathcal{T}_{\textsf{U}}=\\{\mathcal{X}^{t}_{\textsf{U}}\\}$ be the unlabeled
target dataset composed of $N^{t}_{\textsf{U}}=|\mathcal{T}_{\textsf{U}}|$
unlabeled point clouds. Let
$\mathcal{T}_{\textsf{L}}=\\{(\mathcal{X}^{t}_{\textsf{L}},\mathcal{Y}^{t}_{\textsf{L}})\\}$
be the semi-supervised set of $N^{t}_{\textsf{L}}=|\mathcal{T}_{\textsf{L}}|$
labeled target point clouds with $N^{t}_{\textsf{L}}\ll N^{t}_{\textsf{U}}$.

On the upper branch, the source point cloud $\mathcal{X}^{s}$ is mixed with
selected patches of the target point cloud $\mathcal{X}^{t}_{\textsf{U}}$ and,
selected patches of the supervised point cloud $\mathcal{X}^{t}_{\textsf{L}}$
when available. The unlabeled target patches from
$\mathcal{X}^{t}_{\textsf{U}}$ are subsets of points that correspond to the
most confident pseudo-labels $\hat{\mathcal{Y}^{t}_{\textsf{U}}}$ that the
teacher network produces during training. The supervised target patches are
subsets of points that are randomly selected based on the class frequency
distribution in the source training set. On the lower branch, the target point
cloud $\mathcal{X}^{t}_{\textsf{U}}$ is mixed with the selected patches of the
source point cloud $\mathcal{X}^{s}$ and with the selected patches of
$\mathcal{X}^{t}_{\textsf{L}}$, if available. The source patches are subsets
of points that are randomly selected based on their class frequency
distribution in the training set.

We define the branch that mixes target point cloud patches to the source point
cloud as $t\rightarrow s$ and the branch that does the vice versa as
$s\rightarrow t$. Let $\mathcal{X}^{t\rightarrow s}$ be the mixed point cloud
obtained from the upper branch, and $\mathcal{X}^{s\rightarrow t}$ be the
mixed point cloud obtained from the lower branch. Lastly, let
$\Phi_{\mathcal{\theta}}$ and $\Phi_{\mathcal{\theta^{\prime}}}$ be the
student and teacher deep networks with learnable parameters $\theta$ and
$\theta^{\prime}$, respectively.

###  3.2 Semantic selection

To train the student networks with balanced data, we perform a selection of
reliable and informative point cloud patches prior to mixing points and labels
across domains. To select patches from the source point cloud, we use the
class frequency distribution by counting the number of points of each semantic
class within $\mathcal{S}$. Unlike DSP [15] that selects long-tail classes in
advance, we exploit the source distribution and the semantic classes available
to dynamically sample classes at each iteration.

Let $P_{\mathcal{Y}}^{s}$ be the class frequency distribution of
$\mathcal{S}$. We create a function $f$ that randomly selects a subset of
classes at each iteration based on the labels
$\tilde{\mathcal{Y}}^{s}\subset\mathcal{Y}^{s}$. $f$ performs a weighted
random sampling of $\alpha$ classes from the input point cloud by using
$1-P_{\mathcal{Y}}^{s}$ as the class weight for each class. $\alpha$ is an
hyperparameter that regulates the ratio of selected classes for each point
cloud. The output of $f$ is a set point-level labels belonging to the sampled
classes, i.e. $\tilde{\mathcal{Y}}^{s}$. The likelihood that $f$ selects a
class $c$ is inversely proportional to its class frequency in $\mathcal{S}$.
Formally we have

$\tilde{\mathcal{Y}}^{s}=f(\mathcal{Y}^{s},1-P_{\mathcal{Y}}^{s},\alpha).$ (1)

Example: with $\alpha=0.5$, the algorithm selects a number of patches
corresponding to the 50% of the available classes, i.e. long-tailed classes
are selected with a higher likelihood.

Let $\tilde{\mathcal{X}}^{s}$ be the set of points that correspond to
$\tilde{\mathcal{Y}}^{s}$, and let
$\tilde{\mathcal{X}}^{s}_{c}\subset\tilde{\mathcal{X}}^{s}$ be a patch (set of
points) that belongs to class $c\in\mathcal{C}$. To select patches from the
target point clouds, we apply the same set of operations but using the pseudo-
labels produced by the teacher network based on their prediction confidence.
Specifically, we define a function $g$ that selects reliable pseudo-labels
based on their confidence value. The selected pseudo-labels are defined as

$\tilde{\mathcal{Y}}^{t}_{\textsf{U}}=g(\Phi_{\theta^{\prime}}(\mathcal{X}^{t}_{\textsf{U}}),\zeta),$
(2)

where $\Phi_{\theta^{\prime}}$ is the teacher network, $\zeta$ is the
confidence threshold used by the function $g$ and
$\tilde{\mathcal{Y}}^{t}_{\textsf{U}}\subset\hat{\mathcal{Y}}^{t}_{\textsf{U}}$.

Let $\tilde{\mathcal{X}}^{t}_{\textsf{U}}$ be the set of points that
correspond to $\tilde{\mathcal{Y}}^{t}_{\textsf{U}}$.

In the case of target supervision, we apply $f$ to the target labels
$\mathcal{Y}^{t}_{\textsf{L}}$ and randomly select target patches as

$\tilde{\mathcal{Y}}^{t}_{\textsf{L}}=f(\mathcal{Y}^{t}_{\textsf{L}},1-P_{\mathcal{Y}}^{s},\mu),$
(3)

where $\mu$ is an hyperparameter that regulates the ratio of selected classes
for each point cloud similarly to $\alpha$.

###  3.3 Compositional mix

The goal of our compositional mixing module is to create mixed point clouds
based on the selected semantic patches. The compositional mix involves three
consecutive operations: local random augmentation, where patches are augmented
randomly and independently from each other; concatenation, where the augmented
patches are concatenated to the point cloud of the other domain to create the
mixed point cloud; global random augmentation, where the mixed point cloud is
randomly augmented. This module is applied twice, once for the $t\rightarrow
s$ branch (top of Fig. 1), where target patches are mixed within the source
point cloud, and once for the $s\rightarrow t$ branch (bottom of Fig. 1),
where source patches are mixed within the target point cloud. Unlike Mix3D
[21], our mixing strategy embeds data augmentation at local level and global
level.

Let $\delta$ be the indicator function that we define as

$\delta(\mathcal{T}_{\textsf{L}})=\begin{cases}1\,\,\,\,\,\,\,if\,\,\,\,\,\mathcal{T}_{\textsf{L}}\neq\emptyset\\\
0\,\,\,\,\,\,\,\text{otherwise},\end{cases}$ (4)

which indicates whether the supervised target set $\mathcal{T}_{\textsf{L}}$
is empty or not. This can be interpreted as the user desire or need to use
additional target supervision.

In the $s\rightarrow t$ branch, we apply the local random augmentation $h$ to
all the points $\tilde{\mathcal{X}}^{s}_{c}\subset\tilde{\mathcal{X}}^{s}$. We
repeat this operation for all $c\in\tilde{\mathcal{Y}}^{s}$. Note that $h$ is
a local and random augmentation that produces a different result each time it
is applied to a set of points. We define the result of this operation as

$h(\tilde{\mathcal{X}}^{s})=\left\\{h(\tilde{\mathcal{X}}^{s}_{c}),\forall
c\in\tilde{\mathcal{Y}}^{s}\right\\}.$ (5)

If $\delta(\mathcal{T}_{\textsf{L}})=1$ we can apply $h$ also to
$\tilde{\mathcal{X}}^{t}_{\textsf{L}}$ and obtain

$h(\tilde{\mathcal{X}}^{t}_{\textsf{L}})=\left\\{h(\tilde{\mathcal{X}}^{t}_{\textsf{L}}),\forall
c\in\tilde{\mathcal{Y}}^{t}_{\textsf{L}}\right\\}.$ (6)

Then, we concatenate the locally augmented patches with the target point cloud
$X^{t}_{\textsf{L}}$ and we apply the global random augmentation, such as

$\mathcal{X}^{s\rightarrow t}=\begin{cases}r(h(\tilde{\mathcal{X}}^{s})\cup
h(\tilde{\mathcal{X}}^{t}_{\textsf{L}})\cup\mathcal{X}^{t}_{\textsf{U}})\,\,\,\,\,if\,\,\,\,\delta(\mathcal{T}_{\textsf{L}})=1,\\\
\\\
r(h(\tilde{\mathcal{X}}^{s})\cup\mathcal{X}^{t}_{\textsf{U}})\,\,\,\,\,\,\,\,\,\,\,\,\text{otherwise}\end{cases}$
(7)

Their respective labels are concatenated accordingly as

$\mathcal{Y}^{s\rightarrow t}=\begin{cases}r(h(\tilde{\mathcal{Y}}^{s})\cup
h(\tilde{\mathcal{Y}}^{t}_{\textsf{L}})\cup\mathcal{Y}^{t}_{\textsf{U}})\,\,\,\,\,if\,\,\,\,\delta(\mathcal{T}_{\textsf{L}})=1,\\\
\\\
r(h(\tilde{\mathcal{Y}}^{s})\cup\mathcal{Y}^{t}_{\textsf{U}})\,\,\,\,\,\,\,\,\,\,\,\,\text{otherwise},\end{cases}$
(8)

where $r$ is the global augmentation function.

The same operations of Eq. 7-8 are also performed in the $t\rightarrow s$
branch by mixing target patches within the source point cloud. Instead of
using source labels, we use the teacher network to generate pseudo-labels from
the target data. Additionally, we use target supervision if
$\delta(\mathcal{T}_{\textsf{L}})=1$. Then, we concatenate them with the
labels of the source data. This results in $\mathcal{X}^{t\rightarrow s}$ and
$\mathcal{Y}^{t\rightarrow s}$. Note that $\mathcal{T}_{\textsf{L}}$ may be
used without compositional mix and without double branched mixing. We
implement $h$ and $r$ by using typical augmentation strategies for point
clouds [4], i.e. random rotation, scaling, and translation. We report
additional information in Sec. 4.2.

###  3.4 Network update

We leverage the teacher-student learning scheme to facilitate the transfer of
knowledge acquired during the course of the training with mixed domains. We
use the teacher network $\Phi_{\theta^{\prime}}$ to produce target pseudo-
labels $\hat{\mathcal{Y}}^{t}_{\textsf{U}}$ for the student network
$\Phi_{\theta}$, and train $\Phi_{\theta}$ to segment target point clouds by
using the mixed point clouds $\mathcal{X}^{s\rightarrow t}$ and
$\mathcal{X}^{t\rightarrow s}$ based on their mixed labels and pseudo-labels
(Sec. 3.3).

At each batch iteration, we update the student parameters $\Phi_{\theta}$ to
minimize a total objective loss $\mathcal{L}_{tot}$ defined as

$\mathcal{L}_{tot}=\mathcal{L}_{s\rightarrow t}+\mathcal{L}_{t\rightarrow s},$
(9)

where $\mathcal{L}_{s\rightarrow t}$ and $\mathcal{L}_{t\rightarrow s}$ are
the $s\rightarrow t$ and $t\rightarrow s$ branch losses, respectively. Given
$\mathcal{X}^{s\rightarrow t}$ and $\mathcal{Y}^{s\rightarrow t}$, we define
the segmentation loss for the $s\rightarrow t$ branch as

$\mathcal{L}_{s\rightarrow
t}=\mathcal{L}_{seg}(\Phi_{\theta}(\mathcal{X}^{s\rightarrow
t}),\mathcal{Y}^{s\rightarrow t}),$ (10)

the objective of which is to minimize the segmentation error over
$\mathcal{X}^{s\rightarrow t}$, thus learning to segment source patches in the
target domain. Similarly, given $\mathcal{X}^{t\rightarrow s}$ and
$\mathcal{Y}^{t\rightarrow s}$, we define the segmentation loss for the
$t\rightarrow s$ branch as

$\mathcal{L}_{t\rightarrow
s}=\mathcal{L}_{seg}(\Phi_{\theta}(\mathcal{X}^{t\rightarrow
s}),\mathcal{Y}^{t\rightarrow s}),$ (11)

whose objective is to minimize the segmentation error over
$\mathcal{X}^{t\rightarrow s}$ where target patches are composed with source
data. We implement $\mathcal{L}_{seg}$ as the Dice segmentation loss [66],
which we found effective for the segmentation of large-scale point clouds as
it can cope with long-tail classes well.

Lastly, we update the teacher parameters $\theta^{\prime}$ every $\gamma$
iterations following the exponential moving average (EMA)[67] approach

$\theta^{\prime}_{i}=\beta\theta^{\prime}_{i-1}+(1-\beta)\theta,$ (12)

where $i$ indicates the training iteration and $\beta$ is a smoothing
coefficient hyperparamenter.

##  4 Experiments

We evaluate our method in both synthetic-to-real and real-to-real UDA and SSDA
settings. We use SynLiDAR [9] as synthetic dataset, and SemanticKITTI [5, 30,
26], SemanticPOSS [6], and nuScenes [7] as real-world datasets. We compare
CoSMix with five state-of-the-art UDA methods: two general purpose adaptation
methods (ADDA [49], Ent-Min [50]), one image segmentation method (ST [18]),
and two point cloud segmentation methods (PCT [9], ST-PCT [9]). Then, we
compare CoSMix with five state-of-the-art SSDA methods: three general purpose
adaptation methods (MMD [49], MME [68], APE [65]), and two point cloud
segmentation methods (PCT [9], APE-PCT [9]). We refer to CoSMix-UDA and
CoSMix-SSDA to indicate the version of CoSMix for UDA and SSDA, respectively,
we use CoSMix to refer to our method in general otherwise. PCT, ST-PCT and
APE-PCT are the only three state-of-the-art methods developed for 360∘ LiDAR
point clouds and have only been applied for synthetic-to-real UDA and SSDA
settings. We re-implemented the comparison method and adapted to the same
backbone network as that of CoSMix. We refer to these methods as EntMin⋆ [50],
ST⋆ [18], MME⋆ [68], MMD⋆ [49], Source⋆, Target⋆ and Fine-tuned⋆. Moreover, we
extended EntMin [50] and ST [18] to the SSDA setting, and refer to them as
EntMin-SSDA⋆ and ST-SSDA⋆. For completeness, we also include the results of
these methods as they are reported in [9].

###  4.1 Datasets and metrics

SynLiDAR [9] is a large-scale synthetic dataset that is created with the
Unreal Engine [69]. It is composed of 198,396 annotated point clouds with 32
semantic classes. We use 19,840 point clouds for training and 1,976 point
clouds for validation [9]. SemanticPOSS [6] is composed of 2,988 annotated
real-world point cloud with 14 semantic classes. We use the sequence $03$ for
validation and the remaining sequences for training [6]. For the SSDA
settings, we follow [9] and use the point cloud $172$ of sequence $02$ as the
semi-supervised target set. SemanticKITTI [5] is a large-scale segmentation
dataset consisting of LiDAR acquisitions of the popular KITTI dataset [30,
26]. It is composed of 43,552 annotated real-world point clouds with more than
19 semantic classes. We use sequence $08$ for validation and the remaining
sequences for training [5]. For the SSDA settings, we follow [9] and use the
point cloud $848$ from sequence $06$ and the point cloud $940$ from sequence
$02$ as semi-supervised target set. nuScenes [7] is a large-scale segmentation
dataset. It is composed of real-world $850$ sequences ($700$ for training and
$150$ for validation), for a total of $34,000$ annotated point clouds with 32
semantic classes. We use the official training and validation splits in all
our experiments. For the SSDA settings, we follow the same selection protocol
used in [9] and use the point cloud with token
n015-2018-07-24-11-13-19+0800__LIDAR_TOP__1532402013197655 as semi-supervised
target set.

We make source and target labels compatible across our datasets, i.e. SynLiDAR
$\rightarrow$ SemanticPOSS, SynLiDAR $\rightarrow$ SemanticKITTI and,
SemanticKITTI $\rightarrow$ nuScenes. In SynLiDAR $\rightarrow$ SemanticPOSS
and SynLiDAR $\rightarrow$ SemanticKITTI, we follow [9] and map labels into
$14$ segmentation classes and $19$ segmentation classes, respectively. In
SemanticKITTI $\rightarrow$ nuScenes we map source and target labels into $7$
common segmentation classes as in [70].

We evaluate the semantic segmentation performance before and after domain
adaptation [9] by using the Intersection over the Union (IoU) [71] for each
segmentation class and report the per-class IoU. We average the IoU over all
the segmented classes and report the mean Intersection over the Union (mIoU).

###  4.2 Implementation details

We implemented CoSMix in PyTorch and run our experiments on 4$\times$NVIDIA
A100 (40GB SXM4). We use MinkowskiNet as our point cloud segmentation network
[4], in particular we use MinkUNet32 as in [9]. We pre-train our network on
the source domain with Dice loss [66] starting from randomly initialized
weights. In SSDA, we start from the pre-trained source model and finetune on
both source and labeled target for two additional epochs. The finetuned model
is used as pre-trained model in the semi-supervised settings. In UDA, we
initialize student and teacher networks with the parameters obtained after
pre-training. The pre-training and adaptation stage share the same
hyperparameters. In both the pre-training and adaptation steps, we use
Stochastic Gradient Descent with a learning rate of $0.001$.

We set the value of $\alpha$ by examining the long-tailed classes present in
the source domain during the adaptation process. Similarly, we set the
parameter $\mu$ to the same value. We assign the values of $\alpha$ and $\mu$
based on our prior experience, rather than optimizing these parameters through
a systematic process. In the target semantic selection function $g$, we
establish the value of $\zeta$ based on a qualitative assessment of a few
target frames, with the aim of producing spatially compact predictions. This
approach yields approximately 80% of pseudo-labeled points per scene.

On SynLiDAR $\rightarrow$ SemanticPOSS, we use a batch size of $12$ and
perform adaptation for $10$ epochs. We set source and supervised target
semantic selection ($f$) with $\alpha=0.5$ and $\mu=0.5$ while we set target
semantic selection ($g$) with a confidence threshold $\zeta=0.85$. On SynLiDAR
$\rightarrow$ SemanticKITTI, we use a batch size of $16$, adapting for $3$
epochs. During source and supervised target semantic selection ($f$) we set
$\alpha=0.5$ and $\mu=0.5$ while in target semantic selection ($g$) we use a
confidence threshold of $\zeta=0.90$. We use these last same hyperparameters
also on SemanticKITTI $\rightarrow$ nuScenes, and SynLiDAR $\rightarrow$
nuScenes.

Our local augmentations $h$ and global augmentations $r$ are based on data
augmentation strategies that are typical in the LiDAR segmentation literature
[4]. $h$ involves rigid rotation around the $z$-axis, scaling along all the
axes and random point downsampling. We remove $xy$ rotation to produce co-
planar and concentric mixed point clouds, and to preserve point ranges. For
the same reason, we remove rigid translations. We bound rotations between
$[-\pi/2,\pi/2]$ and scaling between $[0.95,1.05]$, and perform random
downsampling for $50\%$ of the patch points. $r$ involves rigid rotation,
translation and scaling along all the three axes. We set the parameters of $r$
the same as those used in [4]. During the network update step (Sec. 3.4), we
update the teacher parameters $\theta^{\prime}_{i}$ with $\beta=0.99$. On
SynLiDAR $\rightarrow$ SemanticPOSS, we set $\gamma=1$ and do not perform
parameter tuning. On SynLiDAR $\rightarrow$ SemanticKITTI, we increase
$\gamma$ to $\gamma=500$ to obtain a stable teacher behavior, i.e. stable
source performance, high average confidence of pseudo-labels, and $\sim 80\%$
of pseudo-labeled points. We use these same hyperparameters also on
SemanticKITTI $\rightarrow$ nuScenes, and SynLiDAR $\rightarrow$ nuScenes.

###  4.3 Quantitative comparisons for UDA

TABLE II: Unsupervised adaptation results on SynLiDAR $\rightarrow$
SemanticPOSS. We denote our reproduced baselines and results with ⋆, e.g.,
Source⋆. Source⋆ and Target⋆ correspond to the model trained on the source
synthetic dataset (lower bound) and on the target real dataset (upper bound),
respectively. Results are reported in terms of mean Intersection over the
Union (mIoU).

Model | pers. | rider | car | trunk | plants | traf. | pole | garb. | buil. | cone. | fence | bike | grou. | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Source | 3.7 | 25.1 | 12.0 | 10.8 | 53.4 | 0.0 | 19.4 | 12.9 | 49.1 | 3.1 | 20.3 | 0.0 | 59.6 | 20.7
Source⋆ | 21.7 | 20.1 | 9.7 | 3.4 | 56.8 | 4.8 | 24.1 | 6.1 | 39.9 | 0.3 | 15.3 | 5.3 | 73.4 | 21.6
Target⋆ | 61.8 | 54.7 | 33.0 | 19.3 | 73.9 | 26.7 | 30.9 | 11.0 | 71.3 | 32.5 | 44.6 | 43.2 | 78.5 | 44.7
ADDA [49] | 27.5 | 35.1 | 18.8 | 12.4 | 53.4 | 2.8 | 27.0 | 12.2 | 64.7 | 1.3 | 6.3 | 6.8 | 55.3 | 24.9
Ent-Min [50] | 24.2 | 32.2 | 21.4 | 18.9 | 61.0 | 2.5 | 36.3 | 8.3 | 56.7 | 3.1 | 5.3 | 4.8 | 57.1 | 25.5
Ent-Min⋆ [50] | 24.8 | 28.0 | 13.4 | 4.1 | 59.6 | 2.0 | 23.3 | 5.8 | 47.0 | 0.0 | 16.1 | 5.8 | 71.6 | 23.2
ST [18] | 23.5 | 31.8 | 22.0 | 18.9 | 63.2 | 1.9 | 41.6 | 13.5 | 58.2 | 1.0 | 9.1 | 6.8 | 60.3 | 27.1
ST⋆ [18] | 47.7 | 42.6 | 24.4 | 13.8 | 62.5 | 3.3 | 36.1 | 23.5 | 50.9 | 18.8 | 14.6 | 4.0 | 68.9 | 31.6
PCT [9] | 13.0 | 35.4 | 13.7 | 10.2 | 53.1 | 1.4 | 23.8 | 12.7 | 52.9 | 0.8 | 13.7 | 1.1 | 66.2 | 22.9
ST-PCT [9] | 28.9 | 34.8 | 27.8 | 18.6 | 63.7 | 4.9 | 41.0 | 16.6 | 64.1 | 1.6 | 12.1 | 6.6 | 63.9 | 29.6
CoSMix-UDA | 55.8 | 51.4 | 36.2 | 23.5 | 71.3 | 22.5 | 34.2 | 28.9 | 66.2 | 20.4 | 24.9 | 10.6 | 78.7 | 40.4

TABLE III: Unsupervised adaptation results on SynLiDAR $\rightarrow$
SemanticKITTI. We denote our reproduced baselines and results with ⋆, e.g.,
Source⋆. Source⋆ and Target⋆ correspond to the model trained on the source
synthetic dataset (lower bound) and on the target real dataset (upper bound),
respectively. Results are reported in terms of mean Intersection over the
Union (mIoU).

Model |  car |  bi.cle |  mt.cle |  truck |  oth-v. |  pers. |  b.clst |  m.clst |  road |  park. |  sidew. |  oth-g. |  build. |  fence |  veget. |  trunk |  terra. |  pole |  traff. | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Source | 42.0 | 5.0 | 4.8 | 0.4 | 2.5 | 12.4 | 43.3 | 1.8 | 48.7 | 4.5 | 31.0 | 0.0 | 18.6 | 11.5 | 60.2 | 30.0 | 48.3 | 19.3 | 3.0 | 20.4
Source⋆ | 60.7 | 1.9 | 22.0 | 10.3 | 8.0 | 16.7 | 11.3 | 20.3 | 70.4 | 6.4 | 40.4 | 0.0 | 25.6 | 8.6 | 59.5 | 18.4 | 29.1 | 29.0 | 13.9 | 23.8
Target⋆ | 90.0 | 6.3 | 20.3 | 63.0 | 18.1 | 31.1 | 39.6 | 5.8 | 90.9 | 29.0 | 74.7 | 4.0 | 85.4 | 23.3 | 83.9 | 46.2 | 62.2 | 40.7 | 20.6 | 44.0
ADDA [49] | 52.5 | 4.5 | 11.9 | 0.3 | 3.9 | 9.4 | 27.9 | 0.5 | 52.8 | 4.9 | 27.4 | 0.0 | 61.0 | 17.0 | 57.4 | 34.5 | 42.9 | 23.2 | 4.5 | 23.0
Ent-Min [50] | 58.3 | 5.1 | 14.3 | 0.3 | 1.8 | 14.3 | 44.5 | 0.5 | 50.4 | 4.3 | 34.8 | 0.0 | 48.3 | 19.7 | 67.5 | 34.8 | 52.0 | 33.0 | 6.1 | 25.8
Ent-Min⋆ [50] | 63.8 | 8.5 | 23.0 | 15.9 | 5.0 | 17.2 | 33.3 | 22.8 | 61.6 | 3.1 | 34.4 | 0.2 | 52.2 | 6.2 | 63.3 | 16.9 | 19.9 | 27.5 | 9.4 | 25.5
ST [18] | 62.0 | 5.0 | 12.4 | 1.3 | 9.2 | 16.7 | 44.2 | 0.4 | 53.0 | 2.5 | 28.4 | 0.0 | 57.1 | 18.7 | 69.8 | 35.0 | 48.7 | 32.5 | 6.9 | 26.5
ST⋆ [18] | 69.7 | 6.4 | 18.3 | 4.4 | 5.8 | 14.8 | 23.3 | 20.2 | 54.2 | 5.3 | 34.1 | 0.1 | 44.3 | 5.1 | 63.5 | 16.8 | 26.9 | 30.6 | 12.2 | 24.0
PCT [9] | 53.4 | 5.4 | 7.4 | 0.8 | 10.9 | 12.0 | 43.2 | 0.3 | 50.8 | 3.7 | 29.4 | 0.0 | 48.0 | 10.4 | 68.2 | 33.1 | 40.0 | 29.5 | 6.9 | 23.9
ST-PCT [9] | 70.8 | 7.3 | 13.1 | 1.9 | 8.4 | 12.6 | 44.0 | 0.6 | 56.4 | 4.5 | 31.8 | 0.0 | 66.7 | 23.7 | 73.3 | 34.6 | 48.4 | 39.4 | 11.7 | 28.9
CoSMix-UDA | 75.1 | 6.8 | 29.4 | 27.1 | 11.1 | 22.1 | 25.0 | 24.7 | 79.3 | 14.9 | 46.7 | 0.1 | 53.4 | 13.0 | 67.7 | 31.4 | 32.1 | 37.9 | 13.4 | 32.2

TABLE IV: Unsupervised adaptation results on SemanticKITTI $\rightarrow$
nuScenes. We denote our reproduced baselines and results with ⋆, e.g.,
Source⋆. Source⋆ and Target⋆ correspond to the model trained on the source
real dataset (lower bound) and on the target real dataset (upper bound),
respectively. Results are reported in terms of mean Intersection over the
Union (mIoU).

Model |  car |  pers. |  road |  side. |  terr. |  manm. |  vege. | mIoU
---|---|---|---|---|---|---|---|---
Source⋆ | 29.4 | 15.6 | 73.2 | 29.1 | 14.7 | 58.5 | 59.9 | 40.1
Target⋆ | 35.8 | 43.2 | 93.6 | 62.1 | 49.0 | 76.4 | 73.9 | 62.0
EntMin⋆[50] | 33.3 | 12.6 | 78.3 | 35.7 | 18.4 | 63.1 | 62.4 | 43.4
ST⋆[18] | 30.6 | 20.6 | 79.1 | 34.4 | 18.9 | 62.4 | 59.3 | 43.6
CoSMix-UDA | 32.1 | 26.3 | 78.1 | 35.1 | 20.2 | 66.4 | 65.2 | 46.2

Synthetic-to-real. Tabs. II&III report the results in the UDA settings on
SynLiDAR $\rightarrow$ SemanticPOSS, and on SynLiDAR $\rightarrow$
SemanticKITTI, respectively. The Source⋆ model is the lower bound of each
scenario with $21.6$ mIoU on SynLiDAR $\rightarrow$ SemanticPOSS and $23.8$
mIoU on SynLiDAR $\rightarrow$ SemanticKITTI. The Target⋆ model is the upper
bound of each scenario with $44.7$ mIoU on SynLiDAR $\rightarrow$ SemanticPOSS
and $44.0$ mIoU on SynLiDAR $\rightarrow$ SemanticKITTI. Note that Source⋆
models always outperform Source. This may be due to a better parameter choice
that leads an improved generalization ability. In SynLiDAR $\rightarrow$
SemanticPOSS (Tab. II), CoSMix-UDA outperforms the other methods on all the
classes, except on pole where ST achieves a better result. On average, we
achieve $40.4$ mIoU, surpassing ST-PCT by $+10.8$ mIoU and improving over the
Source⋆ of $+18.8$ mIoU. CoSMix-UDA improves also on difficult classes as
person, traffic-sign, cone, and bike, whose performance are rather low before
domain adaptation. ST⋆ and EntMin⋆ improve over Source⋆. ST⋆ improves over ST
while EntMin⋆ achieves lower performance. Tab. III reports the results of
SynLiDAR $\rightarrow$ SemanticKITTI. SemanticKITTI is challenging as the
validation sequence includes a wide range of different scenarios with a large
number of semantic classes. CoSMix-UDA improves all the classes when compared
to Source⋆, except for traffic-cone. We believe this is due to the noise
introduced by the pseudo labels on these classes and in related classes such
as $road$. CoSMix-UDA improves on $10$ out of $19$ classes, with a large
margin in the classes car, motorcycle, truck, person, road, parking and
sidewalk. On average, we achieve state-of-the-art performance with a $32.2$
mIoU, outperforming ST-PCT by $+3.3$ mIoU and improving over Source⋆ of about
$+8.4$ mIoU.

Real-to-real. Tab. IV reports the results on SemanticKITTI $\rightarrow$
nuScenes in the UDA setting. SemanticKITTI $\rightarrow$ nuScenes is a more
challenging direction as source and target sensors are different, and nuScenes
has rather sparse point clouds. Source⋆ and Target⋆ models achieve $40.1$ mIoU
and $62.0$ mIoU, respectively. CoSMix-UDA outperforms the compared methods on
$4$ out of $7$ classes, with the largest margin on the class person. On
average, EntMin⋆ and ST⋆ achieve $43.4$ mIoU and $43.6$ mIoU, showing a
limited improvement over Source⋆. CoSMix-UDA achieves the best results of
$46.2$ mIoU, outperforming all the compared methods.

###  4.4 Quantitative comparison for SSDA

Synthetic-to-real. Tabs. V&VI report the results in the SSDA settings on
SynLiDAR $\rightarrow$ SemanticPOSS, and on SynLiDAR $\rightarrow$
SemanticKITTI, respectively. Source⋆ and Target⋆ models are the lower and
upper bound of the UDA settings. The Fine-tuned⋆ model is obtained by fine-
tuning Source⋆ with the semi-supervised target samples. It shows the highest
possible bound without any adaptation approach. Fine-tuned⋆ always outperforms
Fine-tuned from [9]. Similarly, the discrepancy between MMD⋆ and MME⋆, and the
results reported in [9] may due to a different parameter choice. In SynLiDAR
$\rightarrow$ SemanticPOSS (Tab. V), CoSMix-SSDA outperforms all the
comparison methods on all the classes, except on plants, fence and bike where
MME and MME⋆ achieve better results. On average, we reach $41.0$ mIoU,
outperforming APE-PCT by $+9.8$ mIoU and improving over Source⋆ by $+19.4$ and
over Fine-tuned⋆ by $+15.5$. Compared to CoSMix-UDA, CoSMix-SSDA achieves a
$+0.6$ mIoU, getting closer to the Target upper bound. In SynLiDAR
$\rightarrow$ SemanticKITTI (Tab. VI), CoSMix-SSDA brings a significant
improvement on $12$ out $19$, especially on car, truck, motorcyclist, road,
parking, and pole. On average, CoSMix-SSDA achieves $34.3$ mIoU, outperforming
the best baseline APE-PCT by $+7.3$ mIoU and improving over Source⋆ of $+10.5$
mIoU and over Fine-tuned⋆ of $+9.8$ mIoU. Compared to our UDA pipeline,
CoSMix-SSDA improves of $+2.1$ mIoU, showing that the additional target
supervision is beneficial for further reducing the domain gap.

TABLE V: Semi-supervised adaptation results on SynLiDAR $\rightarrow$
SemanticPOSS. We denote our reproduced baselines and results with ⋆, e.g.,
Source⋆. Source⋆ and Target⋆ correspond to the model trained on the source
synthetic dataset (lower bound) and on the target real dataset (upper bound),
respectively. Results are reported in terms of mean Intersection over the
Union (mIoU).

Model | pers. | rider | car | trunk | plants | traf. | pole | garb. | buil. | cone. | fence | bike | grou. | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Source | 3.7 | 25.1 | 12.0 | 10.8 | 53.4 | 0.0 | 19.4 | 12.9 | 49.1 | 3.1 | 20.3 | 0.0 | 59.6 | 20.7
Source⋆ | 21.7 | 20.1 | 9.7 | 3.4 | 56.8 | 4.8 | 24.1 | 6.1 | 39.9 | 0.3 | 15.3 | 5.3 | 73.4 | 21.6
Fine-tuned | 25.2 | 36.1 | 18.2 | 12.8 | 58.6 | 1.7 | 30.5 | 5.6 | 25.7 | 3.0 | 12.0 | 10.6 | 75.6 | 24.3
Fine-tuned⋆ | 25.2 | 27.2 | 21.6 | 9.6 | 60.4 | 0.7 | 16.2 | 10.8 | 44.5 | 12.0 | 24.1 | 2.5 | 76.7 | 25.5
Target⋆ | 61.8 | 54.7 | 33.0 | 19.3 | 73.9 | 26.7 | 30.9 | 11.0 | 71.3 | 32.5 | 44.6 | 43.2 | 78.5 | 44.7
MMD [49] | 25.5 | 35.7 | 28.9 | 6.7 | 64.3 | 1.7 | 23.2 | 5.6 | 53.3 | 3.3 | 30.2 | 13.9 | 70.4 | 27.9
MMD⋆[49] | 28.1 | 12.2 | 18.8 | 11.4 | 71.5 | 10.0 | 14.7 | 0.0 | 64.6 | 0.0 | 28.1 | 25.1 | 78.6 | 27.9
MME [68] | 33.2 | 40.2 | 25.0 | 11.0 | 61.9 | 0.4 | 31.2 | 7.3 | 56.1 | 5.7 | 37.1 | 6.7 | 71.2 | 29.8
MME⋆[68] | 35.8 | 16.1 | 21.4 | 7.9 | 73.7 | 7.9 | 24.2 | 1.5 | 67.6 | 0.0 | 32.8 | 32.0 | 77.0 | 30.6
APE [65] | 34.3 | 40.1 | 21.5 | 16.3 | 62.6 | 0.9 | 31.1 | 2.3 | 55.9 | 13.3 | 34.3 | 9.6 | 71.6 | 30.3
EntMin-SSDA⋆ | 24.7 | 9.4 | 16.5 | 10.7 | 69.9 | 6.7 | 11.7 | 0.0 | 62.6 | 0.0 | 25.2 | 22.5 | 78.9 | 26.1
ST-SSDA⋆ | 40.1 | 24.3 | 22.5 | 7.9 | 70.2 | 13.4 | 21.7 | 1.4 | 66.9 | 0.1 | 34.7 | 32.0 | 78.1 | 31.8
PCT [9] | 25.8 | 36.8 | 27.8 | 11.3 | 62.2 | 1.9 | 31.2 | 5.2 | 58.7 | 2.6 | 34.3 | 8.5 | 68.7 | 28.8
APE-PCT [9] | 34.7 | 36.3 | 27.2 | 15.8 | 62.9 | 0.8 | 31.6 | 8.7 | 62.3 | 9.8 | 35.1 | 9.3 | 70.9 | 31.2
CoSMix-SSDA | 54.9 | 50.6 | 33.4 | 22.5 | 73.0 | 13.6 | 38.4 | 26.4 | 68.5 | 16.2 | 29.6 | 27.4 | 79.0 | 41.0

TABLE VI: Semi-supervised adaptation results on SynLiDAR $\rightarrow$
SemanticKITTI. We denote our reproduced baselines and results with ⋆, e.g.,
Source⋆. Source⋆ and Target⋆ correspond to the model trained on the source
synthetic dataset (lower bound) and on the target real dataset (upper bound),
respectively. Results are reported in terms of mean Intersection over the
Union (mIoU).

Model |  car |  bi.cle |  mt.cle |  truck |  oth-v. |  pers. |  b.clst |  m.clst |  road |  park. |  sidew. |  oth-g. |  build. |  fence |  veget. |  trunk |  terra. |  pole |  traff. | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Source | 42.0 | 5.0 | 4.8 | 0.4 | 2.5 | 12.4 | 43.3 | 1.8 | 48.7 | 4.5 | 31.0 | 0.0 | 18.6 | 11.5 | 60.2 | 30.0 | 48.3 | 19.3 | 3.0 | 20.4
Source⋆ | 60.7 | 1.9 | 22.0 | 10.3 | 8.0 | 16.7 | 11.3 | 20.3 | 70.4 | 6.4 | 40.4 | 0.0 | 25.6 | 8.6 | 59.5 | 18.4 | 29.1 | 29.0 | 13.9 | 23.8
Fine-tuned | 56.2 | 3.0 | 15.1 | 1.0 | 5.0 | 20.2 | 42.1 | 2.8 | 52.1 | 0.7 | 19.8 | 0.0 | 41.3 | 5.8 | 62.1 | 34.0 | 42.0 | 24.6 | 1.4 | 22.6
Fine-tuned⋆ | 61.8 | 2.8 | 21.7 | 10.8 | 4.2 | 14.5 | 18.0 | 15.9 | 65.6 | 6.4 | 40.2 | 0.0 | 34.2 | 7.0 | 60.6 | 22.1 | 39.8 | 32.2 | 8.4 | 24.5
Target⋆ | 90.0 | 6.3 | 20.3 | 63.0 | 18.1 | 31.1 | 39.6 | 5.8 | 90.9 | 29.0 | 74.7 | 4.0 | 85.4 | 23.3 | 83.9 | 46.2 | 62.2 | 40.7 | 20.6 | 44.0
MMD [49] | 56.4 | 3.3 | 13.3 | 1.5 | 6.1 | 21.4 | 34.6 | 1.6 | 54.3 | 0.4 | 21.4 | 0.0 | 50.2 | 5.8 | 61.2 | 37.0 | 44.9 | 31.6 | 2.2 | 23.5
MMD⋆[49] | 46.5 | 3.2 | 6.3 | 12.1 | 3.3 | 8.8 | 21.7 | 13.4 | 47.2 | 4.6 | 29.9 | 0.0 | 50.6 | 5.9 | 62.2 | 16.2 | 23.3 | 19.4 | 4.7 | 20.0
MME [68] | 51.0 | 5.6 | 13.1 | 1.3 | 7.3 | 15.1 | 54.4 | 4.4 | 43.1 | 0.2 | 28.3 | 0.0 | 60.7 | 13.3 | 66.1 | 30.1 | 39.9 | 24.8 | 6.6 | 24.5
MME⋆[68] | 28.7 | 0.2 | 1.0 | 1.8 | 0.9 | 2.0 | 1.6 | 3.5 | 53.6 | 1.8 | 31.1 | 0.0 | 40.6 | 7.2 | 57.6 | 12.1 | 26.7 | 14.4 | 0.1 | 15.0
APE [65] | 58.6 | 6.2 | 16.6 | 3.1 | 11.3 | 14.2 | 35.8 | 3.7 | 61.5 | 1.7 | 30.3 | 0.0 | 54.7 | 15.4 | 64.6 | 20.0 | 45.5 | 23.9 | 9.1 | 25.1
EntMin-SSDA⋆ | 52.0 | 2.6 | 7.8 | 10.3 | 3.5 | 8.4 | 20.9 | 13.2 | 42.1 | 3.5 | 31.2 | 0.0 | 44.1 | 5.8 | 62.5 | 15.2 | 22.7 | 18.2 | 5.8 | 19.5
ST-SSDA⋆ | 60.0 | 2.8 | 10.6 | 14.6 | 5.0 | 10.4 | 19.2 | 20.8 | 63.9 | 4.5 | 35.7 | 0.1 | 43.4 | 7.1 | 62.2 | 13.3 | 26.9 | 24.8 | 10.4 | 22.9
PCT [9] | 56.0 | 7.0 | 17.1 | 2.8 | 9.9 | 23.7 | 43.7 | 5.6 | 55.3 | 0.8 | 22.9 | 0.0 | 50.1 | 8.4 | 65.3 | 23.1 | 43.5 | 28.8 | 7.5 | 24.8
APE-PCT [9] | 58.1 | 7.3 | 17.8 | 2.6 | 13.9 | 24.7 | 46.5 | 5.1 | 60.5 | 1.9 | 31.3 | 0.0 | 56.8 | 14.6 | 67.9 | 23.7 | 44.3 | 26.1 | 9.3 | 27.0
CoSMix-SSDA | 76.9 | 10.4 | 27.1 | 23.1 | 13.4 | 24.0 | 21.7 | 27.9 | 75.8 | 17.9 | 49.7 | 0.1 | 60.3 | 14.7 | 69.8 | 36.8 | 40.9 | 45.6 | 16.2 | 34.3

TABLE VII: Semi-supervised adaptation results on SemanticKITTI $\rightarrow$
nuScenes. We denote our reproduced baselines and results with ⋆, e.g.,
Source⋆. Source⋆ and Target⋆ correspond to the model trained on the source
real dataset (lower bound) and on the target real dataset (upper bound),
respectively. Results are reported in terms of mean Intersection over the
Union (mIoU).

Model |  car |  pers. |  road |  side. |  terr. |  manm. |  vege. | mIoU
---|---|---|---|---|---|---|---|---
Source⋆ | 29.4 | 15.6 | 73.2 | 29.1 | 14.7 | 58.5 | 59.9 | 40.1
Fine-tuned⋆ | 47.0 | 22.9 | 75.1 | 28.6 | 15.3 | 61.8 | 53.7 | 43.5
Target⋆ | 35.8 | 43.2 | 93.6 | 62.1 | 49.0 | 76.4 | 73.9 | 62.0
MMD⋆ [49] | 38.3 | 14.1 | 83.2 | 32.4 | 33.9 | 63.7 | 63.2 | 47.0
MME⋆ [68] | 41.0 | 9.5 | 83.9 | 31.7 | 32.9 | 63.3 | 57.8 | 45.7
EntMin-SSDA⋆ | 31.8 | 13.6 | 81.8 | 35.5 | 30.7 | 66.2 | 65.7 | 46.5
ST-SSDA⋆ | 44.9 | 13.9 | 71.9 | 22.6 | 34.1 | 68.0 | 67.7 | 46.2
CoSMix-SSDA | 45.3 | 26.9 | 80.1 | 34.5 | 20.8 | 68.0 | 67.0 | 48.9

Real-to-real. Tab. VII reports the results on SemanticKITTI $\rightarrow$
nuScenes in the SSDA settings. Source⋆ and Target⋆ models achieve $40.1$ mIoU
and $62.0$ mIoU, respectively. The Fine-tuned⋆ model improves over Source⋆ and
achieves $43.5$ mIoU. CoSMix-SSDA achieves the best results on $3$ out of $7$
classes, with the largest margin on the class pedestrian. On average, MMD⋆ is
the best performing method among the comparison methods with $47.0$ mIoU.
CoSMix-SSDA achieves $48.9$ mIoU, outperforms all the comparison methods and
further improving over CoSMix-UDA.

###  4.5 Domain adaptation between different sensors

TABLE VIII: Adaptation results on SynLiDAR$\rightarrow$nuScenes. We denote our
reproduced baselines and results with ⋆, e.g., Source⋆. Source⋆ and Target⋆
correspond to the model trained on the source synthetic dataset (lower bound)
and on the target real dataset (upper bound), respectively. Results are
reported in terms of mean Intersection over the Union (mIoU).

Model | car | bicycle | motorcycle | truck | bus | pedestrian | road | sidewalk | building | vegetation | terrain | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---
Source⋆ | 23.7 | 2.8 | 10.3 | 15.8 | 4.9 | 20.8 | 63.6 | 18.0 | 47.7 | 50.0 | 3.3 | 23.7
Fine-tuned⋆ | 27.9 | 3.4 | 14.2 | 15.9 | 5.0 | 22.1 | 66.5 | 18.6 | 58.8 | 54.5 | 3.9 | 26.4
Target⋆ | 38.4 | 12.3 | 22.2 | 33.4 | 37.0 | 40.1 | 91.6 | 56.7 | 75.3 | 71.6 | 46.0 | 47.7
CoSMix-UDA | 27.8 | 4.8 | 11.9 | 16.1 | 6.4 | 24.2 | 67.1 | 19.6 | 60.3 | 59.2 | 2.7 | 27.3
CoSMix-SSDA | 28.8 | 4.6 | 7.8 | 11.1 | 10.1 | 22.7 | 72.8 | 21.6 | 60.4 | 60.5 | 3.2 | 27.6

We study the domain adaptation performance of CoSMix when the source point
cloud is synthetically generated with a certain sensor and the target point
cloud is captured in the real world with a different sensor. We use SynLiDAR
as the source domain and nuScenes as the target domain. The semantic classes
are mapped into the common $11$ segmentation classes: car, bicycle,
motorcycle, truck, bus, pedestrian, road, sidewalk, building, vegetation and,
terrain. This case exhibits a rather strong domain shift, as the SynLiDAR
point clouds are dense and nearly noise-free, while the nuScenes point clouds
are sparser and noisier. We follow the same implementation details of
SemanticKITTI $\rightarrow$ nuScenes, except we change the target domain. Tab.
VIII reports the domain adaptation results on SynLiDAR $\rightarrow$ nuScenes
in both the UDA and SSDA settings. Source⋆ and Target⋆ models achieve $23.7$
mIoU and $47.7$ mIoU, respectively. Fine-tuned⋆ improves the performance to
$26.4$ mIoU. CoSMix-UDA improves over Source⋆ by achieving $27.3$ mIoU.
Despite the lack of target supervision, we also outperform the Fine-tuned⋆
baseline. CoSMix-SSDA further improves the results by achieving $27.6$ mIoU
when we introduce limited target supervision. We observed a lower improvement
of CoSMix compared to the other adaptation directions, which we attribute to
the different simulated sensor and the large density difference between
SynLiDAR and nuScenes scans.

###  4.6 Qualitative results

Fig. 2 shows some domain adaptation results on SynLiDAR $\rightarrow$
SemanticPOSS. Predictions of Source⋆ are often incorrect. CoSMix-UDA improves
the segmentation results with more homogeneous regions and correctly assigned
classes, and CoSMix-SSDA further improves the segmentation quality.

Fig. 3 shows the results on SynLiDAR $\rightarrow$ SemanticKITTI that follows
the same result pattern as in Fig. 2. Some classes (e.g. car, vegetation,
pole) greatly improve when CoSMix-SSDA is used. An evident increment of
performance can be observed from Source⋆ to CoSMix-UDA to CoSMix-SSDA in both
the studied domains. Although the limited amount of target supervision used in
CoSMix-SSDA, these experiments show evidence of the benefits of our SSDA
method.

\begin{overpic}[width=104.93794pt]{images/fig2_1a.png} \put(40.0,58.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{Source}${}^{\star}$} \put(130.0,58.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{CoSMix-UDA}} \put(235.0,58.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{CoSMix-SSDA}} \put(350.0,58.5){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{GT}} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_1b.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_1c.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_1d.png} \end{overpic}
---|---|---|---
\begin{overpic}[width=104.93794pt]{images/fig2_2a.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_2b.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_2c.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_2d.png} \end{overpic}
\begin{overpic}[width=104.93794pt]{images/fig2_3a.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_3b.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_3c.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig2_3d.png} \end{overpic}
\begin{overpic}[width=429.28616pt]{images/fig2_legend.png} \end{overpic}
Figure 2: Results on SynLiDAR $\rightarrow$ SemanticPOSS. Source⋆ predictions are often wrong and mingled in the same region. After adaptation, CoSMix-UDA and CoSMix-SSDA improves segmentation with homogeneous predictions and correctly assigned classes. The red circles highlight regions with interesting results. \begin{overpic}[width=104.93794pt]{images/fig3_1a.png} \put(40.0,58.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{Source}${}^{\star}$} \put(130.0,58.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{CoSMix-UDA}} \put(235.0,58.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{CoSMix-SSDA}} \put(350.0,58.5){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize{GT}} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_1b.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_1c.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_1d.png} \end{overpic}
---|---|---|---
\begin{overpic}[width=104.93794pt]{images/fig3_2a.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_2b.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_2c.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_2d.png} \end{overpic}
\begin{overpic}[width=104.93794pt]{images/fig3_3a.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_3b.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_3c.png} \end{overpic} | \begin{overpic}[width=104.93794pt]{images/fig3_3d.png} \end{overpic}
\begin{overpic}[width=424.94574pt]{images/fig3_legend.png} \end{overpic}
Figure 3: Results on SynLIDAR $\rightarrow$ SemanticKITTI. Source⋆ predictions
are often wrong and mingled in the same region. After adaptation, CoSMix-UDA
and CoSMix-SSDA improves segmentation with homogeneous predictions and
correctly assigned classes. The red circles highlight regions with interesting
results. TABLE IX: Ablation study of the CoSMix components: mixing strategy
($t\rightarrow s$ and $s\rightarrow t$), compositional mix augmentations
(local $h$ and global $r$), mean teacher update ($\beta$) and, weighted class
selection in semantic selection ($f$). Each combination is named with a
different version (a-h). Source⋆ performance are added as lower bound and
highlighted in gray to facilitate the reading.

CoSMix | mix | augs | others |
---|---|---|---|---
version | $t\rightarrow s$ | $s\rightarrow t$ | $h$ | $r$ | $\beta$ | $f$ | mIoU
Source⋆ | - | - | - | - | - | - | 21.6
(a) | ✓ |  |  |  |  |  | 31.6
(b) | ✓ |  |  |  | ✓ |  | 31.9
(c) | ✓ | ✓ |  |  |  |  | 35.0
(d) | ✓ | ✓ |  |  | ✓ |  | 35.4
(e) | ✓ | ✓ | ✓ |  | ✓ |  | 36.8
(f) | ✓ | ✓ |  | ✓ | ✓ |  | 37.3
(g) | ✓ | ✓ | ✓ | ✓ | ✓ |  | 39.0
(h) | ✓ | ✓ | ✓ | ✓ |  | ✓ | 39.1
Full | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 40.4

##  5 Ablation study

We investigate the performance of CoSMix in both its UDA and SSDA variants by
using the SynLiDAR $\rightarrow$ SemanticPOSS setup. The first three
experiments are designed to study CoSMix in the UDA setting. In Sec. 5.1, we
analyze CoSMix-UDA components. In Sec. 5.2, we compare our mixing approach
with three recent point cloud mixing strategies, namely, Mix3D [21],
PointCutMix [72] and PolarMix [73]. In Sec. 5.3, we investigate the robustness
of CoSMix to noisy pseudo-labels by changing the confidence threshold $\zeta$
and with different pre-trained models. In the last experiment (Sec. 5.4), we
analyze CoSMix in the SSDA setting, comparing our semi-supervised mixing
approach with three variations of our approach.

###  5.1 Method components

We analyze CoSMix by organizing its components into three groups: mixing
strategies (mix), augmentations (augs) and other components (others). In the
mix group, we assess the importance of the mixing strategies ($t\rightarrow s$
and $s\rightarrow t$) used in our compositional mix (Sec. 3.3) after semantic
selection. In the augs group, we assess the importance of the local $h$ and
global $r$ augmentations that are used in the compositional mix (Sec. 3.3). In
the others group, we assess the importance of the mean teacher update
($\beta$) (Sec. 3.4) and of the long-tail weighted sampling $f$ (Sec. 3.2).
When the $t\rightarrow s$ branch is active, also the pseudo-label filtering
$g$ is utilized, while when $f$ is not active, $\alpha=0.5$ source classes are
selected randomly. With different combinations of components, we obtain
different versions of CoSMix which we name CoSMix (a-h). The complete version
of our method is named Full, where all the components are activated. The
Source⋆ performance is also added as a reference for the lower bound. See Tab.
IX for the definition of these different versions.

When the $t\rightarrow s$ branch is used, CoSMix (a) achieves an initial
$31.6$ mIoU showing that the $t\rightarrow s$ branch provides a significant
adaptation contribution over the Source⋆. When we also use the $s\rightarrow
t$ branch and the mean teacher $\beta$, CoSMix (b-d) further improve
performance achieving a $35.4$ mIoU. By introducing local and global
augmentations in CoSMix (e-h), we can improve performance up to $39.1$ mIoU.
The best performance of $40.4$ mIoU is achieved with CoSMix Full where all the
components are activated.

###  5.2 Point Cloud Mix

We compare CoSMix with Mix3D [21], PointCutMix [72] and PolarMix [73] to show
the effectiveness of the different mixing designs. As per our knowledge, Mix3D
and PolarMix are the only mixup strategies designed for 3D semantic
segmentation, while PointCutMix and PolarMix are the only strategies for
mixing portions of different point clouds. We implement Mix3D and PointCutMix
based on authors descriptions: we concatenate point clouds (random crops for
PointCutMix) of the two domains, i.e., $\mathcal{X}^{s}$ and
$\mathcal{X}^{t}$, as well as their labels and pseudo-labels, i.e.,
$\mathcal{Y}^{s}$ and $\hat{\mathcal{Y}}^{t}$, respectively. PolarMix [73]
uses our same experimental settings and backbone therefore we consider the
results reported in their manuscript. We refer to these mixing strategies as
Mix3D⋆, PointCutMix⋆ and, PolarMix†. CoSMix double is our two-branch network
with sample mixing. For a fair comparison, we deactivate the weighted sampling
and the mean teacher update. We keep local and global augmentations activated.

Fig. 5a shows that Mix3D⋆ outperforms the Source⋆ model, achieving $28.5$
mIoU, followed by PolarMix† which achieves $30.4$ mIoU. PointCutMix⋆ reaches
$31.6$ mIoU, outperforming the previous strategies. When we use the
$t\rightarrow s$ branch alone we can achieve $32.9$ mIoU and when we use the
$s\rightarrow t$ branch alone, CoSMix can further improve the results,
achieving $34.8$ mIoU. This shows that the supervision from the source to
target is effective for adaptation on the target domain. When we use the
contribution from both branches simultaneously, CoSMix achieves the best
result with $38.9$ mIoU.

###  5.3 Robustness to noisy pseudo-labels

We investigate the robustness of CoSMix to increasingly noisier pseudo-labels.
Firstly, we study the effect of different confidence thresholds $\zeta$.
Secondly, we evaluate different versions of pre-trained models that we use for
generating pseudo-labels.

Confidence threshold. We study the importance of setting the correct
confidence threshold $\zeta$ for pseudo-label distillation in $g$ (Sec. 3.2).
We repeat the experiments with a confidence threshold from $0.65$ to $0.95$
and report the obtained adaptation performance in Fig. 5b. CoSMix is robust to
noisy pseudo-labels reaching a $40.2$ mIoU with the low threshold of $0.65$.
The best adaptation performance of $40.4$ mIoU is achieved with a confidence
threshold of $0.85$. By using a high confidence threshold of $0.95$
performance is affected reaching $39.2$ mIoU. With this configuration, too few
pseudo-labels are selected to provide an effective contribution for the
adaptation.

Model pre-training. We quantify the robustness of CoSMix and ST⋆ [18] in
response to pseudo-labels generated with different pre-trained models on
SynLiDAR and tested on SemanticPOSS. In this experiment, we only utilize ST⋆
as it is the sole method from those we benchmarked that is based on pseudo-
labels. We denote the pre-trained model as P⋆. Fig. 4 displays its performance
at different epochs: (a) 1, (b) 2, (c) 4, and (d) 9. Unlike CoSMix, ST⋆ proves
sensitive to pseudo-labels as it underperforms P⋆ in three out of the four
cases. A plausible explanation for this is that ST⋆ refines the pre-trained
model using filtered pseudo-labels during adaptation, depending on the quality
of pseudo-labels. This dependency may cause ST⋆ to drift during the adaptation
process, thus impacting performance. Differently, CoSMix blends source and
target (pseudo) labels, producing two intermediate domains with mixed labels.
In the mixed point clouds, pseudo-labels are integrated with (noise-free)
source labels ($t\rightarrow s$) or noise-free selections ($s\rightarrow t$),
thus mitigating the negative effects of noisy and imprecise regions.
Furthermore, our application of a teacher-based approach allows us to rely on
progressively more precise pseudo-labels, thereby minimizing undesirable drift
effects.

Figure 4: Adaptation results on SynLiDAR$\to$SemanticPOSS with different pre-
trained models. We compare the adaptation results of CoSMix (Ours) with ST⋆
starting from different initialization points (P⋆) indicated with (a-d).

###  5.4 Mixing target supervision

We compare CoSMix-SSDA to three alternative mixing strategies: naive,
$sup\rightarrow s$ and $sup\rightarrow t$. In Fig. 5c, we name each strategy
as CoSMix-SSDA (a-c). In version CoSMix-SSDA (a), we apply CoSMix-UDA without
mixing $\mathcal{T}_{\textsf{L}}$ in $\mathcal{X}^{s\rightarrow t}$ and
$\mathcal{X}^{t\rightarrow s}$. Dice segmentation loss is applied separately
on $\mathcal{T}_{\textsf{L}}$ and averaged with our total objective loss in
Eq. 9. In the single branch mixing with source point clouds ($sup\rightarrow
s$) and with target point clouds ($sup\rightarrow t$), versions (b-c), we
apply only the upper or lower branch of CoSMix-SSDA, respectively. Full is our
proposed double branched CoSMix-SSDA.

CoSMix-SSDA (a) approach reaches $33.7$ mIoU, which shows that traditional
training by using labeled target points as is leads to inferior performance
than using our SSDA approach. Both the single branch mixing strategies achieve
better performance with $38.9$ mIoU and $40.5$ mIoU for $sup\rightarrow s$ and
$sup\rightarrow t$, respectively. The version (b) shows that the mixed target
modality with noise-free annotations helps in reducing the domain shift. The
version (c) suggests that the addition of target noise-free labels helps us in
achieving higher performance. However, both the single branch approaches are
not sufficient to outperform the Full mixing strategy.

\begin{overpic}[width=145.2656pt]{images/fig5a.png} \put(55.0,-5.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize a)} \put(150.0,-5.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize b)} \put(250.0,-5.0){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\footnotesize c)} \end{overpic} | \begin{overpic}[width=146.13237pt]{images/fig5b.png} \end{overpic} | \begin{overpic}[width=142.65869pt]{images/fig5c.png} \end{overpic}
---|---|---
Figure 5: a) Comparison of the adaptation performance with different point
cloud mix up strategies. Compared to the recent mixing strategies Mix3D [21],
PointCutMix [72] and, PolarMix [73], our mixing strategy and its variations
achieve superior performance. b) Comparison of the adaptation performance on
confidence threshold values. Adaptation results show that $\zeta$ should be
set such that to achieve a trade-off between pseudo-label correctness and
object completeness. c) Comparison of the SSDA performance with different
mixing strategies: optimization without mix (naive), single branch mixing with
source point clouds ($sup\rightarrow s$), single branch mixing with
unsupervised target point clouds ($sup\rightarrow t$). Each variation is named
with a different version (a-c). In all the experiments, Source⋆ and Target⋆
performance is the lower and upper bound.

##  6 Conclusions

We introduced the first method for domain adaptation in 3D semantic
segmentation, featuring a novel 3D point cloud mixing strategy that harnesses
both semantic and structural information simultaneously. We developed two
variations of our approach: one for unsupervised adaptation (CoSMix-UDA) and
another for semi-supervised adaptation (CoSMix-SSDA). We performed
comprehensive evaluations in both synthetic-to-real and real-to-real contexts
within UDA and SSDA settings, utilizing large-scale, publicly available LiDAR
datasets. Experimental results demonstrated that our approach significantly
surpasses current state-of-the-art methods in both contexts. Moreover,
detailed analyses underscored the significance of each component within
CoSMix, confirming that our mixing strategy effectively addresses the issue of
domain shift in 3D LiDAR segmentation. A primary limitation of CoSMix is its
reliance on pseudo-labels, making the quality of the initial warm-up model on
the source domain crucial to the adaptation performance on the target domain.
An alternative approach could involve the implementation of self-supervised
learning in lieu of using source data. Future avenues for research might
encompass the incorporation of self-supervised learning tasks, domain
generalization, extending CoSMix to source-free adaptation tasks, and its
application to 3D object detection.

## Acknowledgments

This work was partially supported by OSRAM GmbH, by the MUR PNRR project FAIR
(PE00000013) funded by the NextGenerationEU, by the EU project FEROX Grant
Agreement no 101070440, by the PRIN PREVUE (Prot. 2017N2RK7K), the EU ISFP
PROTECTOR (101034216) and the EU H2020 MARVEL (957337) projects and, it was
carried out in the Vision and Learning joint laboratory of FBK and UNITN.

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|  Cristiano Saltori is a PhD candidate in Computer Vision at the University
of Trento, Italy. Before, he received his Infomation and Communication
Engineering Master’s degree from the University of Trento. His research
interests include deep learning, 3D scene understanding, perception and domain
adaptation.
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|  Fabio Galasso heads the Perception and Intelligence Lab (PINlab) at the
Dept. of Computer Science, Sapienza University of Rome (Italy). His research
interests include distributed and multi-agent intelligent systems, perception
(detection, recognition, re-identification, forecasting) and general
intelligence (reasoning, meta-learning, domain adaptation), within sustainable
and interpretable AI frameworks.
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|  Giuseppe Fiameni is a Data Scientist at NVIDIA where he oversees the AI
Technology Center in Italy, a collaboration among NVIDIA, CINI and CINECA to
accelerate academic research in the field of Artificial Intelligence through
collaboration projects. He has been working as HPC specialist and consultant
at CINECA, the largest HPC facility in Italy, providing support for large-
scale data analytics workloads.
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|  Nicu Sebe is Professor with the University of Trento, Italy, leading the
research in the areas of multimedia information retrieval and human behavior
understanding. He was the General Co- Chair of ACM Multimedia 2013 and 2022,
and the Program Chair of ACM Multimedia 2007 and 2011, ECCV 2016, ICCV 2017
and ICPR 2020. He is a fellow of the International Association for Pattern
Recognition.
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|  Fabio Poiesi is a Researcher in the Technologies of Vision (TeV) Lab at
Fondazione Bruno Kessler in Trento, Italy. He received his Ph.D. degree from
Queen Mary University of London (UK). He was a Postdoctoral Researcher in
Queen Mary University of London before moving to TeV. His research interests
are 3D scene understanding, object detection and tracking, and extended
reality.
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|  Elisa Ricci is an associate professor at the University of Trento and a
head of research unit at Fondazione Bruno Kessler. She received the Honorable
mention award at ICCV 2021 and the Best Paper Award at ACM MM 2021. Her
research interests are mainly in the areas of computer vision and deep
learning. She is an ELLIS fellow.
---|---