Yating<EMAIL_ADDRESS>Na<EMAIL_ADDRESS>Gim Hee
<EMAIL_ADDRESS>Department of Computer Science
National University of Singapore
Singapore Singapore University of Technology and Design
Singapore Towards Robust Few-shot Point Cloud Semantic Segmentation

# Towards Robust Few-shot Point Cloud Semantic Segmentation

###### Abstract

Few-shot point cloud semantic segmentation aims to train a model to quickly
adapt to new unseen classes with only a handful of support set samples.
However, the noise-free assumption in the support set can be easily violated
in many practical real-world settings. In this paper, we focus on improving
the robustness of few-shot point cloud segmentation under the detrimental
influence of noisy support sets during testing time. To this end, we first
propose a Component-level Clean Noise Separation (CCNS) representation
learning to learn discriminative feature representations that separates the
clean samples of the target classes from the noisy samples. Leveraging the
well-separated clean and noisy support samples from our CCNS, we further
propose a Multi-scale Degree-based Noise Suppression (MDNS) scheme to remove
the noisy shots from the support set. We conduct extensive experiments on
various noise settings on two benchmark datasets. Our results show that the
combination of CCNS and MDNS significantly improves the performance. Our code
is available at https://github.com/Pixie8888/R3DFSSeg.

##  1 Introduction

Few-shot point cloud semantic segmentation (3DFSSeg) [Zhao et al.(2021)Zhao,
Chua, and Lee, Mao et al.(2022)Mao, Guo, Xiaonan, Yuan, and Guo] is a
pragmatic direction as it is able to segment novel classes during testing
stage with only few labeled samples. In contrast to the fully-supervised
methods [Wang et al.(2019c)Wang, Sun, Liu, Sarma, Bronstein, and Solomon, Qi
et al.(2017a)Qi, Su, Mo, and Guibas, Qi et al.(2017b)Qi, Yi, Su, and Guibas]
which only work for close set, 3DFSSeg has better generalization ability.
However, it assumes that the learning samples of the novel classes are
correctly labeled during online testing time.

Unfortunately, the assumption of completely clean data could be violated in
practice due to a variety of reasons. First, human labeling is error-prone.
The irregular data structure, low-resolution, and subtle inter-class geometric
difference make human annotators themselves hard to correctly recognize
objects [Wang et al.(2019a)Wang, Wu, Wu, and Keutzer]. The crowdsourcing
labeling further stresses the annotation quality [Walter et al.(2020)Walter,
Kölle, and Yin]. As a consequence, ScanNet [Dai et al.(2017)Dai, Chang, Savva,
Halber, Funkhouser, and Nießner] still contains annotation mistakes [Ye et
al.(2021)Ye, Chen, Han, and Liao] after manual refinement over an extended
period of time. Second, the industry is actively seeking cheaper and more
efficient annotation system to replace human labeling, _e.g_ semi-automatic
labeling [Li and Zhang(2020), Wirth et al.(2019)Wirth, Quehl, Ota, and
Stiller] and fully automatic annotation [Humblot-Renaux et al.(2023)Humblot-
Renaux, Jensen, and Møgelmose, Bloembergen and Eijgenstein(2021), Chen et
al.(2019)Chen, Liao, Wang, Liu, and Liu]. It further challenges the curation
of high-quality data.

As shown in Fig. 1, we can refine the noisy annotations of the static base
class dataset offline by either manual checking or data-driven algorithm [Ye
et al.(2021)Ye, Chen, Han, and Liao] given enough time and budget. However, it
is impossible to invest the same amount of human supervision to guarantee
noise-free in every support set after model being deployed because the number
of new classes in the real world is infinite [Purushwalkam et
al.(2022)Purushwalkam, Morgado, and Gupta, Hariharan and Girshick(2017)].
Neither can we use data-driven algorithm [Ye et al.(2021)Ye, Chen, Han, and
Liao] to automatically clean the noise due to severe overfitting to the small
number of training samples per new class (cf. Tab. 1).

To this end, we tackle with the noisy labels in the testing stage of 3DFSSeg,
which is challenging but with high practical value. In 3DFSSeg, a few support
point clouds are provided as learning samples for each new class during meta-
testing. Each support sample (i.e. shot) is provided with a binary mask
indicating the presence of the corresponding class. Based on the given support
set, the model segments the new class in any unlabeled (i.e. query) point
clouds. As pointed out by [Ye et al.(2021)Ye, Chen, Han, and Liao, Humblot-
Renaux et al.(2023)Humblot-Renaux, Jensen, and Møgelmose] that the instance-
level noise is most common in the annotation, objects of other classes are
wrongly annotated as the target class and collected in the support set. We
define shots with incorrectly labeled foreground object as noisy shots. Thus,
the goal of robust few-shot point cloud semantic segmentation (R3DFSSeg) is to
learn a robust few-shot segmentor that is less influenced by the noisy shots.

Figure 1:  Comparison between noisy base and novel class dataset of 3DFSSeg.
(a) Base class dataset is static with finite samples. (b) Novel class dataset
is non-stationary as new classes are continuously collected in the online
testing stage. An example where a sofa and a curtain are wrongly annotated in
support set 1 and 2, respectively.

In this paper, we first propose a Component-level Clean Noise Separation
(CCNS) representation learning to learn robust representation that is
discriminative between features of clean and noisy points. Inspired by [Zhao
et al.(2021)Zhao, Chua, and Lee], we adopt the meta-learning paradigm for few-
shot point cloud segmentation. During meta-training, we randomly inject noise
into the support set by sampling point clouds containing foreground objects
from other classes to mimic the noisy meta-testing environments. We introduce
a class-wise supervised contrastive learning on the noisy support set to
separate the clean samples of the target classes from the noisy samples. To
obtain more fine-grained and diverse contrastive features, we further propose
the use of farthest point sampling to decompose the masked points in the
feature space into multiple components. Intuitively, our CCNS is designed to
encourage features from different classes to be well-separated, such that the
clean shots in the support set would form the largest cluster in the feature
space when learning converges.

We further propose a Multi-scale Degree-based Noise Suppression (MDNS) scheme
to remove the noisy shots from the support set during testing stage. Our MDNS
separates clean from noisy samples by checking the degree of each sample in a
fully connected pair-wise similarity graph. Clean samples tend to form well-
defined clusters with higher degrees in the pair-wise similarity graph. In
contrast, noisy samples are relatively scattered with lower degrees of
connectivity in the feature space.

Our main contributions can be summarized as follows: 1) To the best of our
knowledge, we are the first to study the problem of robust few-shot point
cloud semantic segmentation, which is important in real-world applications
since noisy labels are inevitable in practice. 2) We propose a component-level
clean noise separation method for representation learning to enhance the
class-level discrimination in the embedding space. 3) We propose a multi-scale
degree-based noise suppression scheme that is able to effectively remove noisy
samples from the small support set for each new class during testing. 4) We
conduct extensive experiments on two benchmark datasets (i.e. S3DIS and
ScanNet) with various noise settings and show superior results over the
baselines.

##  2 Related Work

#### Few-shot Learning.

Few-shot learning aims to transfer knowledge learned from the abundant samples
of the seen class to a set of unseen classes with only few labeled samples.
One of the dominant approach is the metric-based [Snell et al.(2017)Snell,
Swersky, and Zemel, Vinyals et al.(2016)Vinyals, Blundell, Lillicrap,
Wierstra, et al.] methods, which meta-learns a transferable feature embedding
that coincides with a fixed metric. The pioneer work ProtoNet [Snell et
al.(2017)Snell, Swersky, and Zemel] predicts query label by finding the
nearest class prototype under the Euclidean distance. The key to the metric-
based method is the discriminative feature embedding with compact class
clusters [Ma et al.(2021)Ma, Xie, Han, Chang, Galstyan, and Abd-Almageed, Yang
et al.(2022)Yang, Wang, and Zhu, Gao et al.(2021)Gao, Fei, Liu, Lu, and Xiang,
Ye et al.(2020)Ye, Hu, Zhan, and Sha]. Ye _et al_[Ye et al.(2020)Ye, Hu, Zhan,
and Sha] apply the contrastive objective to align the training instances close
to its own class center after the embedding adaptation. Although we also use
contrastive learning in the episodic training, we adopt fine-grained
contrastive objective (i.e. feature components) to better capture the diverse
intra-class distribution of point cloud.

#### Few-shot Semantic Segmentation.

Few-shot semantic segmentation segments semantic objects in an image [Wang et
al.(2019b)Wang, Liew, Zou, Zhou, and Feng, Zhang et al.(2019b)Zhang, Lin, Liu,
Yao, and Shen, Liu et al.(2020b)Liu, Zhang, Zhang, and He] or a point cloud
[Zhao et al.(2021)Zhao, Chua, and Lee, Mao et al.(2022)Mao, Guo, Xiaonan,
Yuan, and Guo, Lai et al.(2022)Lai, Chen, Zhang, Zhang, Lin, and Wu] with only
few annotated samples. The 2D image semantic segmentation can be categorized
into relation-based method [Zhang et al.(2019b)Zhang, Lin, Liu, Yao, and Shen,
Zhang et al.(2019a)Zhang, Lin, Liu, Guo, Wu, and Yao, Tian et al.(2020)Tian,
Zhao, Shu, Yang, Li, and Jia, Zhang et al.(2021)Zhang, Xiao, and Qin] and
prototype-based method [Liu et al.(2020b)Liu, Zhang, Zhang, and He, Yang et
al.(2020)Yang, Liu, Li, Jiao, and Ye, Wang et al.(2019b)Wang, Liew, Zou, Zhou,
and Feng]. Zhao _et al_[Zhao et al.(2021)Zhao, Chua, and Lee] propose the
first work on 3D few-shot point cloud semantic segmentation. They generate
multi-prototypes via farthest point sampling to better capture the complex
data distribution of the point cloud. The transductive inference is conducted
between multi-prototypes and query points to infer the label for each query
point. However, all these works assume that the annotation in the given
support are accurate during testing time. In practice, this is a very strong
assumption given that the pixel-level and point-level annotation are extremely
tedious and error-prone. In view of this limitation, this paper studies the
problem of robust few-shot point cloud semantic segmentation and proposes a
effective model that can better adapt to real world applications.

#### Learning with Noisy Labels.

Learning with noisy labels is gaining increasing attention as the deep neural
networks are shown to be extremely vulnerable to the noisy labels [Arpit et
al.(2017)Arpit, Jastrzębski, Ballas, Krueger, Bengio, Kanwal, Maharaj,
Fischer, Courville, Bengio, et al., Han et al.(2018)Han, Yao, Yu, Niu, Xu, Hu,
Tsang, and Sugiyama, Li et al.(2020)Li, Socher, and Hoi]. There are three
major approaches: label correction using the prediction of the model as the
new label [Li et al.(2020)Li, Socher, and Hoi, Reed et al.(2014)Reed, Lee,
Anguelov, Szegedy, Erhan, and Rabinovich, Liu et al.(2020a)Liu, Niles-Weed,
Razavian, and Fernandez-Granda, Song et al.(2019)Song, Kim, and Lee], sample
selection using small loss criterion to selectively update model [Han et
al.(2018)Han, Yao, Yu, Niu, Xu, Hu, Tsang, and Sugiyama, Wei et al.(2020)Wei,
Feng, Chen, and An, Yu et al.(2019)Yu, Han, Yao, Niu, Tsang, and Sugiyama] and
learning robust representation [Li et al.(2022)Li, Xia, Ge, and Liu, Wu et
al.(2021)Wu, Wei, Jiang, Mao, Tang, and Li, Li et al.(2021)Li, Xiong, and Hoi,
Iscen et al.(2022)Iscen, Valmadre, Arnab, and Schmid, Ortego et
al.(2021)Ortego, Arazo, Albert, O’Connor, and McGuinness]. PNAL [Ye et
al.(2021)Ye, Chen, Han, and Liao] proposes robust point cloud semantic
segmentation (R3DSeg) that studies label noise in the fully-supervised
setting. It stores the prediction history of every point in the training
dataset and corrects point labels in the cluster-wise manner epoch by epoch.
All these noise-robust methods deal with static dataset in the offline
training stage and require massive samples to train (data-driven). In
contrast, R3DFSSeg addresses noise in the online testing stage, where new
classes appear continuously with small support set. The data-driven algorithms
would thus overfit in R3DFSSeg.

Existing methods dealing with noisy label of novel classes in the few-shot
learning are only for 2D image classification (R2DFSL) [Liang et
al.(2022)Liang, Rangrej, Petrovic, and Hassner, Mazumder et al.(2021)Mazumder,
Singh, and Namboodiri, Lu et al.(2020)Lu, Jin, Liang, and Zhang]. RNNP
[Mazumder et al.(2021)Mazumder, Singh, and Namboodiri] proposes a non-
parametric test method by combining data augmentation with k-means to refine
the class prototype. Liang _et al_[Liang et al.(2022)Liang, Rangrej, Petrovic,
and Hassner] learn a robust prototype generater by relying on the self-
attention module inside the Transformer [Vaswani et al.(2017)Vaswani, Shazeer,
Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin] to weigh down the
noisy shots. Compared to 2D classification, 3D point cloud segmentation is
more challenging as it requires per-point classification and point cloud has
much larger intra-class variance. Thus, the 2D methods, which only generate
one robust prototype per class, fail on the R3DFSSeg.

Figure 2: The architecture of our framework. ‘S’ represents the support point
cloud and ‘Q’ represents the query point cloud. The left figure shows the
pipeline during meta-training, where we conduct component-level clean noise
separation representation learning for each episode class. Components of
different classes are pushed away from each other. The right figure shows the
pipeline during meta-testing, where we perform multi-scale degree-based noise
suppression to remove the noisy shots.

##  3 Our Method

#### Problem Formulation.

The few-shot point cloud segmentation consists of two datasets:
$\mathcal{T}_{base}$ and $\mathcal{T}_{novel}$ sampled from disjoint classes
$\mathcal{C}_{base}$ and $\mathcal{C}_{novel}$, respectively. The goal is to
learn a model from $\mathcal{C}_{base}$ and generalize to the
$\mathcal{C}_{novel}$. Following previous work [Zhao et al.(2021)Zhao, Chua,
and Lee], we adopt the episodic training on the $\mathcal{C}_{base}$ to
emulate the few-shot setting during testing. In each $N$-way $K$-shot episode,
$N$ is the number of classes to be learned, and $K$ is the number of labeled
samples per class. The labeled samples are termed as the support set:
$S=\left\\{\left(P_{k}^{1},M_{k}^{1}\right)_{k=1}^{K},\ldots,\left(P_{k}^{N},M_{k}^{N}\right)_{k=1}^{K}\right\\}$.
Each point cloud $P_{k}^{n}\in\mathbb{R}^{m\times f_{0}}$ contains $m$ points
with input feature dimension of $f_{0}$. The $M_{k}^{n}\in\mathbb{R}^{m\times
1}$ is the corresponding binary mask indicating the presence of class $n$.

We are also given a set of $T$ unlabeled point clouds, termed as the query
set: $Q=\left\\{\left(R_{i},L_{i}\right)\right\\}_{i=1}^{T}$. Each query point
cloud $R_{i}\in\mathbb{R}^{m\times f_{0}}$ is associated with the ground truth
label $L_{i}\in\mathbb{R}^{m\times 1}$ only available in the training stage.
During testing, $M_{k}^{n}$ can wrongly assign object of another class to
class $n$ due to the instance-level labeling error [Ye et al.(2021)Ye, Chen,
Han, and Liao]. We denote the noisy mask $\tilde{M}_{k}^{n}$ and the
corresponding point cloud $\tilde{P}_{k}^{n}$ as the noisy sample, and its
correct class assignment as $Y_{k}$. Consequently, the support set $S$ becomes
the mixture of clean and noisy shots. The goal of robust few-shot point cloud
semantic segmentation is to correctly predict the query label by learning from
the noisy support set $S$.

#### Framework Overview.

Fig. 2 illustrates our proposed framework. We choose AttMPTI [Zhao et
al.(2021)Zhao, Chua, and Lee] as our few-shot segmentor since it achieves
state-of-the-art performance in the few-shot point cloud segmentation. In
addition, AttMPTI is potentially robust to the noise when a good feature
embedding is guaranteed (Sec. 3.1). In view of this, we propose the Component-
level Clean Noise Separation (CCNS) representation learning during meta-
training to enhance the discrimination and generalization of the feature
embedding for AttMPTI (Sec. 3.2). We further propose the multi-scale degree-
based noise suppression (MDNS) to remove the noisy shots during meta-testing
based on their similarity graph (Sec. 3.3).

###  3.1 Why Choose AttMPTI?

AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee] is the state-of-the-art few-
shot point cloud segmentation method. It consists of a feature extractor to
embed the support and query point cloud into the same metric space, a multi-
prototype generation module to generate prototypes from support set, and a
label propagation module to infer query label. Compared to ProtoNet [Snell et
al.(2017)Snell, Swersky, and Zemel], AttMPTI has several unique components
that gives it the potential to be robust, in addition to showing more superior
performance. First, AttMPTI generates multi-prototypes via FPS [Qi et
al.(2017b)Qi, Yi, Su, and Guibas], while ProtoNet uses mean aggregation of all
the relevant class feature. The sampled seed points via FPS are able to
represent the diversity of the feature space, and the local prototype is
generated by clustering each point to the nearest seed point based on the
Euclidean distance in the feature space. In this way, the multi-prototypes can
inherently separate the clean and noisy points in the prototype-level. As
shown in Fig. 3, the clean ratio of local prototypes is either 1 (100% clean)
or 0 (100% noise), but it seldom produces a half-clean prototype. In
comparison, the global prototype used in the ProtoNet leads to a clean-noise
compound.

Figure 3: Comparison of prototype cleanness from different methods on a 5-shot
with 40% out-episode noise setting. ‘1’ means the prototype only containing
clean-labeled points, and ‘0’ means the prototype only containing points that
are incorrectly labeled as the target class. Values in between 0-1 represent
the portion of clean-labeled points in the prototype.

Second, AttMPTI infers query labels via label propagation [Zhou et
al.(2003)Zhou, Bousquet, Lal, Weston, and Schölkopf] in a transductive
fashion, while ProtoNet infers each query point independently with the set of
class prototypes. The label propagation is based on the manifold smoothness,
i.e. nearby samples in the feature space share the same label, and it has the
ability to correct the noisy label [Wu et al.(2021)Wu, Wei, Jiang, Mao, Tang,
and Li, Iscen et al.(2022)Iscen, Valmadre, Arnab, and Schmid]. In contrast,
ProtoNet independently and identically predicts the label for each query point
based on the global prototypes that are potentially noisy. The lack of
reasoning the relationships among the support and query prevents the model
from being able to correct the support noise. Although the design of AttMPTI
shows a better potential than ProtoNet in resisting the noise existing in the
support set, the performance of both multi-prototype generation and label
propagation are subjected to the discriminativity of the feature embeddings.
To enhance the representation learning, we propose to perform component-level
clean-noise separation.

Figure 4: t-SNE [Van der Maaten and Hinton(2008)] visualization of the CCNS on
a 5-shot support set with 2 noisy shots. Each dot represents a point in the
feature space and each triangle represents a feature component. Different
colors represent different classes with blue indicating the target class. The
arrow shows the direction to pull the feature components.

###  3.2 Component-level Clean Noise Separation

Our component-level clean noise separation (CCNS) representation learning aims
to enhance the class-wise discrimination in the feature space. We randomly
replace some of the K support shots with shots sampled from other classes
during episodic training and induce the model to differentiate clean and noisy
shots in the feature space. With these synthesized support sets with noisy
labels, we perform a clean-noise separation representation learning for each
way (i.e. class) by optimizing the model with the class-wise contrastive
learning among the $K$ support shots as follow:

$\small\mathcal{L}_{\text{CNS}}=\frac{1}{K}\sum_{k=1}^{K}\left(\frac{-1}{|A(z_{k})|}\sum_{z_{g}\in
A(z_{k})}\log\frac{\exp\left(z_{k}\cdot
z_{g}/\tau\right)}{\sum\limits_{h\backslash k}\exp\left(z_{k}\cdot
z_{h}/\tau\right)}\right),$ (1)

where $z_{k}\in\mathbb{R}^{d}$ is the L2 normalized average foreground feature
of the support point cloud $P_{k}$ in the projection space.
$A(z_{k})=\left\\{z_{g}\mid Y_{g}=Y_{k}\right\\}$ is the set of positive
samples $z_{g}$ with its semantic label $Y_{g}$ the same as the semantic label
$Y_{k}$ of $z_{k}$. $|A(z_{k})|$ is the cardinality and $\tau$ is the
temperature. By training with $\mathcal{L}_{\text{CNS}}$, the shots with same
foreground class are encouraged to stay together while staying away from
samples of other classes.

Unfortunately, a simple mean aggregation of the foreground area tends to be
sub-optimal in representing the class distribution since the distribution of
point features of each class is very large as shown in Fig. 4. To this end, we
conduct class-wise contrastive learning in a more fine-grained way by dividing
the features in each foreground area into local components. The feature
components aggregate local patterns that exhibit similar fine-grained
semantics, and have better coverage of the feature space compared to the naive
mean aggregation.  Specifically, we first perform FPS in the feature space and
then locally aggregate the point features into a set of feature components
$\left\\{z_{k}^{1},\cdots,z_{k}^{R}\right\\}$, to replace the original
holistic $z_{k}$. Consequently, the component-level clean noise separation
$\mathcal{L}_{\text{CCNS}}$ is formulated as:

$\mathcal{L}_{\text{CCNS}}=\frac{1}{KR}\sum\limits_{k=1}^{K}\sum\limits_{i=1}^{R}\left(\frac{-1}{|A(z_{k}^{i})|}\sum\limits_{z_{g}^{j}\in
A(z_{k}^{i})}\log\frac{\exp\left(z_{k}^{i}\cdot
z_{g}^{j}/\tau\right)}{\sum\limits_{h,b\backslash(k,i)}\exp\left(z_{k}^{i}\cdot
z_{h}^{b}/\tau\right)}\right),$ (2)

where the $A(z_{k}^{i})=\left\\{z_{g}^{j}\mid Y_{g}=Y_{k}\right\\}$ is the set
of positive samples with the same semantic label $Y_{g}$ as $Y_{k}$, and the
$|A(z_{k}^{i})|$ is the cardinality. As shown in Fig. 4, each component
represents a different aspect of its corresponding shot in the feature space.
Essentially, it forms a multi-view self-supervised contrastive learning for
each shot, where the ‘view’ is a local component in the feature space.
Correspondingly, the components at the boarder of the class distribution
automatically serve as the hard negative samples to other classes and hard
positive samples to its own class, which are the key to a successful
contrastive learning [Khosla et al.(2020)Khosla, Teterwak, Wang, Sarna, Tian,
Isola, Maschinot, Liu, and Krishnan, Chen et al.(2020)Chen, Kornblith,
Norouzi, and Hinton].

The final optimization objective during the training stage is given by:

$\mathcal{L}=\mathcal{L}_{\text{CE}}+\lambda\mathcal{L}_{\text{CCNS}},$ (3)

where $\lambda$ is a hyper-parameter to weigh the contribution of
$\mathcal{L}_{\text{CCNS}}$. $\mathcal{L}_{CE}$ is the original cross-entropy
loss in AttMPTI.

###  3.3 Multi-scale Degree-based Noise Suppression

Although the clean and noisy points can separate under the well-learned
embedding space, the prototype generation and label propagation module are
still exposed to the mislabeled shots during testing time. To reduce their
negative influence during testing, we design a degree-based noise suppression
scheme to automatically remove the suspicious noisy shots. Specifically, we
build a fully connected graph G on the K support shots for each way. We
average the foreground feature $x_{i}\in\mathbb{R}^{d}$ of the $i$-th shot as
the feature of node i. The weight $W_{ij}$ of the edge encodes the affinity
between the two end nodes $i$ and $j$ as follow:

$W_{ij}:=\begin{cases}{\left[x_{i}^{\top}x_{j}\right]_{+}^{\gamma},}&\text{ if
}i\neq j\\\ 0,&\text{ otherwise }\end{cases}.\vspace{-1mm}$ (4)

We then compute the degree $d_{i}=\sum_{j}W_{ij}$ for each node i.
Essentially, the degree reflects the nodes connection in the graph. The noisy
shots tend to have lower degree since the clean shots usually form a cluster
with the largest size and the noisy shots are scattered in the feature space.
Consequently, we identify them based on the clean indicator:

$I_{i}:=\begin{cases}1&\text{ if }d_{i}>thr\\\ 0,&\text{ otherwise
}\end{cases},\vspace{-2mm}$ (5)

where we set the $thr$ as the mean of the $\left\\{d_{i}\right\\}_{i=1}^{K}$.
The shots with $I=0$ are treated as noise and removed.

Some point clouds may have complex data distribution that cannot be
sufficiently represented by a global representation. To mitigate this problem,
we extend the single-level degree-based noise suppression scheme to multi-
level, thus yielding the Multi-scale Degree-based Noise Suppression (MDNS).
Our MDNS can be more robust to some complex samples and consequently improve
the accuracy of clean sample identification. Specifically, we add an
additional level to perform noise suppression. We evenly split the foreground
object along the x/y/z coordinates, and denote the number of cuts along the
x/y/z coordinates as $n_{x}$/$n_{y}$/$n_{z}$. The foreground feature in each
sub-shot is locally aggregated and the feature set for each shot is enlarged
to $\left\\{x_{i,s}^{1},\cdots,x_{i,s}^{e}\right\\}$, where $e=n_{x}\times
n_{y}\times n_{z}$. The single representation $x_{i}$ is the case of
$\left\\{n_{x}=1,n_{y}=1,n_{z}=1\right\\}$ and is considered as the coarsest
scale with $s=1$. We then send them into the noise suppression module to get
the clean indicator $\left\\{I_{i,s}^{1},\cdots,I_{i,s}^{e}\right\\}$, where
the majority voting is performed get the shot-level indicator $I_{i,s}$.
Lastly, we assemble the final prediction $I_{i}$ as the majority voting of the
prediction at each scale $\left\\{I_{i,1},\ldots,I_{i,s}\right\\}$.

##  4 Experiments

###  4.1 Datasets and Noise Settings

#### Datasets.

We conduct experiments on S3DIS [Armeni et al.(2016)Armeni, Sener, Zamir,
Jiang, Brilakis, Fischer, and Savarese] and ScanNet [Dai et al.(2017)Dai,
Chang, Savva, Halber, Funkhouser, and Nießner]. S3DIS contains point clouds of
272 rooms collected from six indoor areas with annotation of 12 semantic
classes. ScanNet contains point clouds of 1,513 scans from 707 unique indoor
scenes with annotation of 20 semantic classes. Following [Zhao et
al.(2021)Zhao, Chua, and Lee], we split each room into non-overlapping blocks
with size of $1\text{m}\times 1\text{m}$ on the xy plane. Consequently, S3DIS
and ScanNet contains 7,547 and 36,350 blocks, respectively. We sample
$m=2,048$ points as the input point cloud from a block. The input feature
$f_{0}$ corresponds to XYZ, RGB and normalized XYZ values. During training, we
randomly sample one episode by first sampling N classes from
$\mathcal{C}_{base}$ and then sampling $NK$ point clouds as the support set
and $T$ point clouds as the query set. The support mask $M$ and the query
label $L$ are modified from its original annotation to only indicate the
presence of the target classes with irrelevant classes as the background. The
testing episodes are formed in a similar way, except for that we exhaustively
sample 100 episodes for each combination of N classes from the
$\mathcal{C}_{novel}$. We use the data split 0 of [Zhao et al.(2021)Zhao,
Chua, and Lee] as the test classes on both datasets. We adopt the mean
Intersection over Union (mIoU) as the evaluation metric.

model | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
| 20% | 40% | 40% | 60%
|  2-way |  3-way |  2-way | 3-way |  2-way |  3-way |  2-way | 3-way |  2-way |  3-way
PNAL [Ye et al.(2021)Ye, Chen, Han, and Liao] |  13.67 |  8.12 |  8.94 | 5.45 |  5.95 |  3.13 |  8.08 | 4.28 |  4.77 |  2.87
Tra-NFS [Liang et al.(2022)Liang, Rangrej, Petrovic, and Hassner] |  44.98 |  31.67 |  43.44 | 30.68 |  37.27 |  27.39 |  41.72 | 28.43 |  35.67 |  23.20
ProtoNet [Snell et al.(2017)Snell, Swersky, and Zemel] |  57.02 |  46.78 |  54.21 | 43.57 |  42.57 |  36.71 |  50.01 | 39.31 |  44.96 |  36.08
AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee] |  65.90 |  51.71 |  60.01 | 47.96 |  38.81 |  37.56 |  58.60 | 44.76 |  51.18 |  40.32
Ours |  68.21 |  54.79 |  66.02 | 52.91 |  58.01 |  48.72 |  66.09 | 50.71 |  58.84 |  46.19

Table 1: Results on the S3DIS using mIoU metric on 2-way 5-shot and 3-way
5-shot.

model | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
| 20% | 40% | 40% | 60%
|  2-way |  3-way |  2-way | 3-way |  2-way |  3-way |  2-way | 3-way |  2-way |  3-way
Tra-NFS [Liang et al.(2022)Liang, Rangrej, Petrovic, and Hassner] |  41.89 |  31.56 |  39.72 | 29.20 |  34.25 |  25.07 |  38.42 | 27.29 |  34.68 |  23.78
ProtoNet [Snell et al.(2017)Snell, Swersky, and Zemel] |  47.55 |  38.97 |  44.19 | 36.46 |  34.57 |  30.23 |  42.47 | 33.88 |  36.64 |  28.55
AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee] |  54.16 |  44.52 |  46.63 | 38.83 |  31.57 |  27.62 |  43.31 | 34.33 |  36.45 |  26.79
Ours |  53.50 |  43.84 |  49.78 | 41.01 |  38.70 |  34.03 |  47.90 | 38.93 |  38.42 |  28.81

Table 2: Results on the ScanNet using mIoU metric on 2-way 5-shot and 3-way
5-shot.

#### Noise Settings.

We explore two types of label noise: 1) In-episode noise samples noisy shots
from other N-1 classes of the current episode. It studies how the mix of the N
foreground classes affects the prediction of query point. We test the models
on in-episode noise ratio of 20% and 40%. 2) Out-episode noise samples noisy
shots from outside of the N classes in the $\mathcal{C}_{novel}$. It studies
how the outliers affect the prediction of the query point. We test the models
on out-episode noise ratio of 40% and 60%.

The noise rate is defined as the percentage of the $K$ support shots.
Following existing literature of learning with noisy labels [Han et
al.(2018)Han, Yao, Yu, Niu, Xu, Hu, Tsang, and Sugiyama, Li et al.(2020)Li,
Socher, and Hoi, Liang et al.(2022)Liang, Rangrej, Petrovic, and Hassner], we
define the noise ratio with the restriction that the percentage of clean
labeled samples is larger than any noisy class. We thus can only consider up
to 40% noise for the in-episode noise and up to 60% noise for the out-episode
noise in both 2-way 5-shot and 3-way 5-shot point cloud segmentation.

###  4.2 Implementation Details

We adopt the AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee] as the few-shot
segmentor and follow the same training procedure as AttMPTI. We first pre-
train the feature extractor 100 epochs on the $\mathcal{C}_{base}$ with
learning rate of 0.001 and Adam optimizer. In the episodic training, the
feature extractor is fine-tuned with learning rate of 0.0001 and other
learnable modules are optimized with learning rate of 0.001. The projection
head consists of one fully-connected layer with the output dimension d as 128.
Both $\lambda$ and $\tau$ are set to 0.1, and R is set 4 in
$\mathcal{L}_{\text{CCNS}}$. We randomly generate noisy shots in each episode
during training by sampling shots from $\mathcal{C}_{base}$. The noise ratio
is randomly chosen from $\left\\{0,0.2,0.4\right\\}$. In MDNS, the $\gamma$ is
set to 3 for scale s=1 and to 1 for any other scale. The MDNS is conducted in
two scales: $\left\\{n_{x}=1,n_{y}=1,n_{z}=1\right\\}$ and
$\left\\{n_{x}=2,n_{y}=2,n_{z}=1\right\\}$. All the experiments are done using
one GTX 3090 GPU.

Figure 5: Qualitative comparison of a 2-way 5-shot point cloud segmentation
with 40% out-episode noise on S3DIS.

###  4.3 Main Results

We compare with 3DFSSeg methods AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee]
and ProtoNet [Snell et al.(2017)Snell, Swersky, and Zemel], R2DFSL method Tra-
NFS [Liang et al.(2022)Liang, Rangrej, Petrovic, and Hassner] and R3DSeg
method PNAL [Ye et al.(2021)Ye, Chen, Han, and Liao]. All methods use the same
feature extractor as AttMPTI for fair comparison.

Tab. 1 and Tab. 2 presents the experiment results on the noisy 2-way 5-shot
and 3-way 5-shot point cloud segmentation on S3DIS and ScanNet, respectively.
AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee] usually has better performance
than ProtoNet [Snell et al.(2017)Snell, Swersky, and Zemel] in terms of
various noise setting, but both methods suffer considerably with increasing
noise ratio. It suggests that few-shot segmentor is indeed vulnerable to the
support noise. On the other hand, our method is able to largely improve the
robustness of the AttMPTI over all noise settings on both datasets. Fig. 5
presents the qualitative results of a 2-way 5-shot point cloud segmentation
with 40% out-episode noise on the S3DIS. It shows our method can correctly
segments the target classes in the query point while AttMPTI fails.  We notice
that our model is slightly worse than AttMPTI in the 0% setting in Tab. 2. We
postulate that our method can predict correct labels, but the noisy ground
truths of ScanNet [Ye et al.(2021)Ye, Chen, Han, and Liao] cannot reflect the
true performance of our method. This postulation is evidenced by the great
superiority of our method over baseline methods on S3DIS, which is a dataset
with clean ground truths. It suggests that our method can adapt to the unknown
test environment (both clean and noise test), which is important for model
deployment in real world.

2D robust few-shot learner Tra-NFS [Liang et al.(2022)Liang, Rangrej,
Petrovic, and Hassner] performs poorly on R3DFSSeg due to severe modality gap,
i.e. point cloud has larger intra-class viriance than 2D images, making Tra-
NFS hard to detect clean shots. 3D robust point cloud segmentor PNAL [Ye et
al.(2021)Ye, Chen, Han, and Liao] also fails in the few-shot setting due to
small support set in each episode.

We further notice that the in-episode noise has larger negative influence than
the out-episode noise, _e.g_ 40% in-episode noise vs 40% out-episode noise. We
believe the reason is that the features in each foreground class usually form
a compact cluster. The in-episode noise causes the labels in this compact
cluster to be different, which severely confuses the model of which class this
cluster belongs to. In contrast, the out-episode noise are usually separated
from the foregrond classes in the feature space, and is less likely to
influence them.

model | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
20% | 40% | 40% | 60%
AttMPTI | 65.90 | 60.01 | 38.81 | 58.60 | 51.11
AttMPTI+CCNS | 68.50 | 63.10 | 41.75 | 63.77 | 56.79
AttMPTI+MDNS | 64.80 | 63.03 | 52.78 | 61.73 | 52.98
Ours | 68.21 | 66.02 | 58.01 | 66.09 | 58.84

Table 3: Effectiveness of CCNS and MDNS on the S3DIS on 2-way 5-shot. ‘Ours’
consists of both CCNS and MDNS.

###  4.4 Ablation Study

#### Effectiveness of CCNS and MDNS.

We analyze the effectiveness of our proposed component-level clean noise
suppression (CCNS) and multi-scale degree-based noise suppression (MDNS) on
S3DIS in Tab. 3. It is worth noting that the robustness of AttMPTI is improved
by simplely adding our feature representation learning, i.e. CCNS. It verifies
our claim that AttMPTI has the potential to be noise robust (by FPS based
multi-prototype generation and label propagation), yet is subject to how
discriminative the feature embedding is.  MDNS improves performance on most
settings and its performance gains are also subjected to the discriminativity
of the feature space. By further equipping with CCNS, our final model achieves
consistent and significant improvements under all settings.  The ablation
study on hyperparameter choosing is provided in the supplementary material.

model | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
20% | 40% | 40% | 60%
AttMPTI | 32.75 | 27.96 | 20.72 | 23.89 | 17.54
Ours | 32.74 | 30.79 | 26.73 | 28.13 | 21.22

Table 4: 5-way 5-shot setting on ScanNet.

#### High way setting.

Tab. 4 shows results of 5-way 5-shot setting on ScanNet. Our model again can
significantly outperform AttMPTI on all noise settings.

##  5 Conclusion

In this paper, we address the new task of robust few-shot point cloud
segmentation, which is a more general setting that considers label noise in
the support set. We design the Component-level Clean Noise Separation (CCNS)
representation learning to learn a discriminative feature embedding. Our CCNS
encourages the features from different classes to stay away from each other,
and concurrently induces the clean shots to form the largest cluster in the
feature space. Leveraging the clean samples identified from our CCNS, we
further propose the Multi-scale Degree-based Noise Suppression (MDNS) to
remove the noisy shots before the prototype generation based on their affinity
with other samples in the support set. Experiment results that outperform the
baselines show the feasibility of our proposed method.

#### Acknowledgement.

This research is supported by the National Research Foundation, Singapore
under its AI Singapore Programme (AISG Award No: AISG2-RP-2021-024), and the
Tier 2 grant MOE-T2EP20120-0011 from the Singapore Ministry of Education. This
research is also supported by the SUTD-ZJU Thematic Research Grant RS-
MEZJU-00031. The work is fully done at the National University of Singapore.

## Supplementary Material

##  Appendix A Ablation Study

#### Analysis of different R values.

Tab. A1 shows the ablation study of different number of components for each
shot in the component-level clean noise separation. ‘R=1’ is the shot-level
representation. It can be seen that the performance of ‘R=1’ is generally
worse than that of the component-level contrastive learning, which verifies
that the feature is sub-optimized with a single holistic aggregation. By
dividing into local components, we can get more fine-grained and diverse
positive and negative samples with ‘R=4’ having the best performance.

model | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
20% | 40% | 40% | 60%
R=1 | 67.62 | 65.83 | 55.94 | 65.17 | 57.46
R=2 | 67.93 | 66.28 | 57.41 | 65.08 | 58.57
R=4 | 68.21 | 66.02 | 58.01 | 66.09 | 58.84
R=8 | 67.40 | 65.58 | 56.66 | 65.52 | 57.94

Table A1: Effects of different number of components in CCNS.

#### Analysis of different noise ratios in CCNS.

We analyze different combination of noise ratio in the episodic training since
our component-level clean noise separation is conducted among the clean and
noisy shots. ‘$\left\\{0.2,0.4\right\\}$’ has large performance drop when
comparing with ‘$\left\\{0,0.2,0.4\right\\}$’, which suggests that it is very
necessary to include noise-free episodes during training. By further adding
noise ratio of 0.6 (with the restriction that any number of noisy class should
not outnumber the clean shots), there is again a significant drop in
performance. We can conclude that only a mix of a proper portion of noisy and
clean episodes during training can bring decent improvement in the noisy test.

Training Noise | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
20% | 40% | 40% | 60%
$\left\\{0.2,0.4\right\\}$ | 63.66 | 60.91 | 49.51 | 59.95 | 50.55
$\left\\{0,0.2,0.4\right\\}$ | 68.21 | 66.02 | 58.01 | 66.09 | 58.84
$\left\\{0,0.2,0.4,0.6\right\\}$ | 66.40 | 65.39 | 55.00 | 63.39 | 56.11

Table A2: Effects of different simulated noise combinations.

#### Analysis of different scales in MDNS.

Tab. A3 presents the analysis of different scales in the multi-scale degree-
based noise suppression. Due to space limitation, we only provide the
comparison of selected scales from the many possibilities of combinations. We
first analyze what constitutes a good scale. It is almost guaranteed that the
holistic scale $\left\\{1/1/1\right\\}$ gives decent performance since the
mean representation covers the general information. The performance varies a
lot when the foreground objects are divided into fine-grained scales. By
comparing $\left\\{2/2/1\right\\}$, $\left\\{1/2/2\right\\}$ and
$\left\\{2/1/2\right\\}$, we can see that a cut on the z-axis causes a
significant drop in performance on the heavy noise setting. By comparing
$\left\\{3/3/1\right\\}$ with $\left\\{2/2/1\right\\}$, we can see that the
cuts that are too fine-grained cause a performance drop due to the severe lack
of the global information in the sub-shots. Overall, $\left\\{1/1/1\right\\}$
and $\left\\{2/2/1\right\\}$ are the good scales and their combination
achieves the best performance.

$\left\\{n_{x}/n_{y}/n_{z}\right\\}$ | In-episode Noise | Out-episode Noise
---|---|---
20% | 40% | 40% | 60%
$\left\\{1/1/1\right\\}$ | 65.94 | 57.27 | 65.90 | 58.70
$\left\\{2/2/1\right\\}$ | 66.47 | 56.56 | 65.96 | 57.99
$\left\\{1/2/2\right\\}$ | 66.37 | 55.29 | 65.96 | 54.56
$\left\\{2/1/2\right\\}$ | 66.31 | 54.81 | 65.44 | 54.96
$\left\\{3/3/1\right\\}$ | 65.99 | 54.69 | 65.24 | 57.69
$\left\\{1/1/1\right\\}$ & $\left\\{2/2/1\right\\}$ | 66.02 | 58.01 | 66.09 | 58.84
$\left\\{1/1/1\right\\}$ & $\left\\{2/2/1\right\\}$ & $\left\\{3/3/1\right\\}$ | 65.81 | 58.01 | 66.00 | 57.51

Table A3: Effects of different scale choices in MDNS.

##  Appendix B t-SNE Visualization

Fig. B1 presents visualization of the feature distribution of testing classes
on the S3DIS via t-SNE [Van der Maaten and Hinton(2008)]. Each color
represents a different class. By learning with our proposed component-level
clean noise separation, the intra-class is more compact and inter-class is
more separable.

Fig. B2 presents visualization of the feature distribution of training classes
on the S3DIS via t-SNE [Van der Maaten and Hinton(2008)]. Different colors
represent different classes. It is obvious that the class-wise feature
distribution of our method is more distinctive and compact than AttMPTI.
Together with Fig. B1, we can conclude that our feature representation
learning is able to not only make feature embedding of seen classes
discriminative but also generalize to unseen classes.

Figure B1: t-SNE [Van der Maaten and Hinton(2008)] comparison on the testing
classes of S3DIS. Figure B2: t-SNE [Van der Maaten and Hinton(2008)]
comparison on the training classes of S3DIS.

##  Appendix C Experiment Results on ScanNet

#### Effectiveness of CCNS and MDNS.

We analyze the effectiveness of our proposed component-level clean noise
suppression (CCNS) and multi-scale degree-based noise suppression (MDNS) on
ScanNet in Tab. C4. Both CCNS and MDNS are effective, and the combination of
them achieves best overall performance. It is worth highlighting that the
robustness of AttMPTI is improved by simply adding our feature representation
learning, i.e. CCNS. It verifies our claim that AttMPTI has the potential to
be noise robust (by FPS based multi-prototype generation and label
propagation), yet is subject to how discriminative the feature embedding is.

model | 0% | In-episode Noise | Out-episode Noise
---|---|---|---
20% | 40% | 40% | 60%
AttMPTI | 54.16 | 46.63 | 31.57 | 43.31 | 36.45
AttMPTI+CCNS | 54.79 | 47.80 | 32.92 | 45.54 | 38.57
Ours | 53.50 | 49.78 | 38.70 | 47.90 | 38.42

Table C4: Effectiveness of CCNS and MDNS on the ScanNet on 2-way 5-shot.
‘Ours’ consists of both CCNS and MDNS.

#### Qualitative Results.

Fig. C3 presents the qualitative comparison between our method and AttMPTI
under a 2-way 5-shot point cloud segmentation with 40% out-episode noise on
ScanNet [Dai et al.(2017)Dai, Chang, Savva, Halber, Funkhouser, and Nießner].
With the interference of the noisy shots, AttMPTI [Zhao et al.(2021)Zhao,
Chua, and Lee] either fails to segment the target semantic object (see the
result in the first row) or wrongly segment some background points as the
target class (see the result in the second row). In contrast, our method is
able to give reliable segmentation results with respect to the target classes.

Figure C3: Qualitative comparison between AttMPTI and our method under a 2-way
5-shot point cloud segmentation with 40% out-episode noise on ScanNet. Each
row shows the segmentation results of a target class from the corresponding
support set in the 2-way setting.

##  Appendix D Data split

We follow the data split of [Zhao et al.(2021)Zhao, Chua, and Lee], and adopt
the split 0 as the testing classes as shown in Tab. D5.

Dataset | Meta-Testing Classes | Meta-Training Classes
---|---|---
S3DIS |  | beam, board, bookcase,
---
ceiling, chair, column
| door, floor, sofa,
---
table, wall, window
ScanNet |  | bathtub, bed, bookshelf,
---
cabinet, chair, counter,
curtain, desk, door, floor
| otherfurniture, picture, refrigerator,
---
shower curtain, sink, sofa,
table, toilet, wall, window

Table D5: Data split in S3DIS and ScanNet.

##  Appendix E Clean Ratio Comparison

Tab. E6 lists the clean ratios of the original support set (‘Original’) and
the filtered support set produced by the MDNS (‘Ours’) during meta-testing.
The clean ratio in each noise setting is given by first computing the
percentage of the number of the clean shots in the corresponding set of one
episode and then averaging the percentages in all episodes. As can be clearly
seen from Tab. E6, our method can significantly improve the clean ratio in all
the noise setting.

Model | In-episode Noise | Out-episode Noise
---|---|---
|  20% |  40% |  40% |  60%
Original |  0.8000 |  0.6000 |  0.6000 |  0.4000
Ours |  0.9749 |  0.8211 |  0.8476 |  0.5602

Table E6: Comparison of the clean ratio of the 2-way 5-shot noisy support set
in the meta-testing stage on the S3DIS.

##  Appendix F Baseline Setups

We compare our method with few-shot point cloud semantic segmentation
(3DFSSeg) methods AttMPTI [Zhao et al.(2021)Zhao, Chua, and Lee] and ProtoNet
[Snell et al.(2017)Snell, Swersky, and Zemel], robust few-shot learning
(R2DFSL) method Tra-NFS [Liang et al.(2022)Liang, Rangrej, Petrovic, and
Hassner] and robust point cloud semantic segmentation (R3DSeg) method PNAL [Ye
et al.(2021)Ye, Chen, Han, and Liao]. All methods use the same feature
extractor as AttMPTI for fair comparison.

We follow the official code in AttMPTI to train ProtoNet and AttMPTI. For Tra-
NFS, we adopt three-layer transformer encoder to generate robust prototype. We
also randomly inject noise into the support set by sampling point clouds
containing foreground objects from other classes during meta-training. For
PNAL, we apply its robust training algorithm on each noisy support set and
then test the performance on the corresponding query point cloud in each
episode during meta-testing. We do not carry forward the knowledge from one
episode to the next as suggested in [Mazumder et al.(2021)Mazumder, Singh, and
Namboodiri].

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