Wonseok<EMAIL_ADDRESS>Gyusam<EMAIL_ADDRESS>Seokha
<EMAIL_ADDRESS>Giljoo<EMAIL_ADDRESS>Chanyoung
<EMAIL_ADDRESS>Younghyun<EMAIL_ADDRESS>Jinkyu
<EMAIL_ADDRESS>Sangpil<EMAIL_ADDRESS>Department of
Artificial Intelligence,
Korea University,
Seoul, Republic of Korea School of Computing,
KAIST,
Daejeon, Republic of Korea Department of Computer Science and Engineering,
Korea University,
Seoul, Republic of Korea Autonomous Driving Center,
Hyundai Motor Company R&D Division,
Seoul, Republic of Korea Overlap Region Aware Multi-view 3D Object Detection
**footnotetext: Corresponding Authors

# ORA3D: Overlap Region Aware Multi-view 3D Object Detection

###### Abstract

Current multi-view 3D object detection methods often fail to detect objects in
the overlap region properly, and the networks’ understanding of the scene is
often limited to that of a monocular detection network. Moreover, objects in
the overlap region are often largely occluded or suffer from deformation due
to camera distortion, causing a domain shift. To mitigate this issue, we
propose using the following two main modules: (1) Stereo Disparity Estimation
for Weak Depth Supervision and (2) Adversarial Overlap Region Discriminator.
The former utilizes the traditional stereo disparity estimation method to
obtain reliable disparity information from the overlap region. Given the
disparity estimates as supervision, we propose regularizing the network to
fully utilize the geometric potential of binocular images and improve the
overall detection accuracy accordingly. Further, the latter module minimizes
the representational gap between non-overlap and overlapping regions. We
demonstrate the effectiveness of the proposed method with the nuScenes large-
scale multi-view 3D object detection data. Our experiments show that our
proposed method outperforms current state-of-the-art models, i.e., DETR3D and
BEVDet.

##  1 Introduction

Figure 1: Examples where DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang,
Wang, Zhao, and Solomon] fails to properly detect objects in the overlapped
regions (see dotted line), resulting in performance degradation due to
multiple false positives. The first row represents the bird’s eye view of the
two scenes in the second row (see there are more false positives in the
overlapped regions than in others). The second row shows overlapped areas
between multi-view images used as input to the network.

Object detection in 3D space plays a crucial role in various real-world
applications, including autonomous driving systems. Existing 3D object
detection methods [He et al.(2020)He, Wang, Zeng, Zeng, Liu, and Zeng, Shi et
al.(2020)Shi, Guo, Jiang, Wang, Shi, Wang, and Li, Deng et al.(2020)Deng, Shi,
Li, Zhou, Zhang, and Li, Shi et al.(2021)Shi, Jiang, Deng, Wang, Guo, Shi,
Wang, and Li, Mao et al.(2021)Mao, Niu, Bai, Liang, Xu, and Xu] based on point
clouds from LiDAR sensors often yield reliable results, but these methods
suffer from a large budget to establish LiDAR sensors per vehicle. Further,
camera-based object detection methods [Wang et al.(2021a)Wang, Zhu, Pang, and
Lin, Wang et al.(2022a)Wang, Xinge, Pang, and Lin, Wang et al.(2022b)Wang,
Guizilini, Zhang, Wang, Zhao, and Solomon, Li et al.(2020)Li, Zhao, Liu, and
Cao] using monocular images are economical but, their performance is
suboptimal due to insufficient depth cues. Stereo vision-based object
detection methods [Liu et al.(2021)Liu, Wang, and Liu, Chen et al.(2020)Chen,
Liu, Shen, and Jia, Wang et al.(2021b)Wang, Yang, Hu, Liang, and Urtasun, Li
et al.(2019)Li, Chen, and Shen] might be an alternative option as they
outperform monocular detection approaches with accurate depth estimation.
Still, constraints in setting surround-view stereo vision systems need to be
resolved. Recently, multi-view (and surround-view) camera systems have become
an alternative balanced option as they can resolve some of the weaknesses of
monocular and stereo vision systems for the 3D object detection task,
potentially replacing LiDAR sensors.

Existing camera-only 3D object detection methods [Wang et al.(2019)Wang, Chao,
Garg, Hariharan, Campbell, and Weinberger, You et al.(2020)You, Wang, Chao,
Garg, Pleiss, Hariharan, Campbell, and Weinberger, Wang et al.(2021a)Wang,
Zhu, Pang, and Lin, Wang et al.(2022a)Wang, Xinge, Pang, and Lin] have mainly
focused on predicting accurate depth to improve performance. Although
estimating precise depth significantly impacts accuracy, it remains
challenging. Primarily, there is a problem that depth may not be represented
adequately on the pixel (e.g., difficulty in dealing with distant objects on
pixels and depth compounding error properly). A landmark work in the camera-
only multi-view 3D object detection task is DETR3D [Wang et al.(2022b)Wang,
Guizilini, Zhang, Wang, Zhao, and Solomon]. It introduces a promising multi-
view detection pipeline that processes six images concurrently in an end-to-
end manner, predicting all objects around simultaneously and implicitly
utilizing rich information in the overlapping regions. Even though DETR3D
performs reasonably well, we found that the network (without explicit
guidance) does not totally use the geometric potentials of multi-view camera
systems. Specifically, the network’s understanding of the scene could be
limited to that of a monocular detection network, resulting in multiple false
positives in the overlapped regions, as shown in Figure 1. Thus, how to deal
with this issue to boost detection accuracy remains a crucial problem.

As reported by Chen  _et al_ [Chen et al.(2020)Chen, Liu, Shen, and Jia],
disparity supervision, which fully pilots the network by exploiting the strong
association of binocular images, substantially improves detection performance.
Inspired by this observation, we propose to use stereo disparity estimation
techniques on the overlap region, which is between all adjacent camera pairs
in the surround-view setting. Although this region is relatively small, it
serves as a geometric link between two images. Consequently, we apply outputs
from the traditional stereo disparity estimation model as weak depth
supervision to improve the detection accuracy over the overlap region. We
empirically found that this supervision significantly improves the overall
detection accuracy.

This is only part of a story. Unlike the human vision system that quickly
identifies an object across overlapped cameras, we empirically observe that
DNN has a strong inductive bias toward identifying objects individually in
each single-view image. This often results in failing to utilize additional
information of the same instance appearing in other different-view images.
Moreover, we observe a domain shift effect between the overlap region (i.e.,
the region far from the camera center) and the non-overlap region (i.e., the
region near the camera center) due to camera lens distortion. Thus, we further
propose to train an adversarial overlap region discriminator, which minimizes
the domain gap between objects in the non-overlap regions vs. overlap regions.
We validate from experiments that such adversarial training makes the overlap
region performance more robust.

We start from the state-of-the-art multi-view 3D object detection model called
DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] as
shown in Figure 2. Built upon DETR3D, we introduce the following two main
modules: (i) Stereo Disparity Estimation for Weak Supervision and (ii)
Adversarial Overlap Region Discriminator. We evaluate the effectiveness of our
proposed method using the nuScenes [Caesar et al.(2020)Caesar, Bankiti, Lang,
Vora, Liong, Xu, Krishnan, Pan, Baldan, and Beijbom] dataset, which is a
widely used large-scale multi-view 3D object detection benchmark. Through
comprehensive experiments, we verify that our proposed model generally
outperforms the state-of-the-art approaches in the camera-only 3D object
detection task. Our main contributions are summarized as follows:

  * •

We report that existing works often neglect properly dealing with objects in
the overlap region, which limits fully using the geometric potentials of
multi-view camera systems, causing performance degradation.

  * •

We propose to use outputs from the traditional stereo disparity estimation
model on the overlap region and apply them as weak supervision to improve the
detection accuracy over the overlap region. We empirically find that this
supervision significantly improves the overall detection accuracy.

  * •

We introduce an overlap region discriminator that adversarially learns to
minimize the covariate shift between objects from non-overlap regions vs. from
overlap regions.

Figure 2: An overview of our proposed architecture. Built upon DETR3D [Wang et
al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon], our model takes
multi-view camera inputs and outputs a set of 3D bounding boxes for objects in
the scene. Our model consists of two main modules: (1) Stereo Matching Network
for Weak Depth Supervision, where our depth estimation head is trained to
predict a dense depth map of the overlap region. The ground-truth depth map is
obtained by a traditional stereo disparity estimation algorithm. (2)
Adversarial Overlap Region Discriminator, which minimizes the gap between non-
overlap regions vs. overlap regions, improving the overall detection
performance.

##  2 Related Work

3D Object Detection. As the study of inferring objects in 3D plane begins,
Mono3D [Chen et al.(2016)Chen, Kundu, Zhang, Ma, Fidler, and Urtasun] conducts
3D object detection task using multiple information such as RGB images, object
instruction segmentation, context, and location prior information. The model
proposed by Roddick, T.  _et al_ [Roddick et al.(2018)Roddick, Kendall, and
Cipolla] utilizes Bird-Eye-View(BEV) to compensate for performance degradation
due to incorrect depth feature extracted from solely RGB images. MonoPSR [Ku
et al.(2019)Ku, Pon, and Waslander] presents a method to significantly reduce
3D search space using 2D object detection and exploit point clouds to recover
local shape and scale. FCOS3D [Wang et al.(2021a)Wang, Zhu, Pang, and Lin] and
RTM3D [Li et al.(2020)Li, Zhao, Liu, and Cao] predicts 3D bounding box more
accurately through features guided by geometric information. In addition, PGD
[Wang et al.(2022a)Wang, Xinge, Pang, and Lin] shows that geometric
interactions between objects enhance the reliability of depth. Yet, the
methods mentioned above perform detection tasks independently for each image.
Thus, to expand for multiple cameras, each frame processing is performed
before integrating the outputs in the post-processing stage. To address this
problem, DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and
Solomon] introduces a 3D object detection method that simultaneously processes
multi-view images. DETR3D predicts 3D bounding boxes via backward geometric
projection and operates set-to-set prediction without post-processing. Also,
ImVoxelNet [Rukhovich et al.(2022)Rukhovich, Vorontsova, and Konushin]
implements a surrounding view 3D object detector similar to the LiDAR system
by optimizing the chronic computation of multiple images. Although they use
multi-view images, they do not take advantage of the features of multiple
camera settings. Hence, we focus on overlaying clues between images, which are
multi-view characteristics, to improve detection performance.

In addition to the approaches mentioned above, recently, another solution of
transforming the image features into BEV representation and applying it to 3D
object detection has been widely studied. Lift-Splat-Shoot (LSS) [Philion and
Fidler(2020)] introduces a view transform method that infers depth
distribution and projects multiple image features into BEV representation.
BEVDet [Huang et al.(2021)Huang, Huang, Zhu, Yun, and Du] and BEVDet4D [Huang
and Huang(2022)], which extend LSS, demonstrate that applying the BEV features
to 3D object detection is practical. Following BEVDet, BEVDepth [Li et
al.(2022a)Li, Ge, Yu, Yang, Wang, Shi, Sun, and Li] constructs better BEV
features with trustworthy depth prediction. BEVFormer [Li et al.(2022b)Li,
Wang, Li, Xie, Sima, Lu, Yu, and Dai] employs spatiotemporal cues by
interacting with spatial and temporal space via predefined grid-shaped BEV
queries. Note that our proposed method could potentially be applied to BEV-
based approaches in an ad-hoc manner, as we focus on regularizing networks to
deal with objects in the overlap region properly. However, we leave it as
future work and will first focus on improving DETR3D-based approaches.

Stereo 3D Object Detection. The stereo 3D object detection task that utilizes
binocular information is similar to the human system. Inspired by depth
estimation models [Zbontar et al.(2016)Zbontar, LeCun, et al., Kendall et
al.(2017)Kendall, Martirosyan, Dasgupta, Henry, Kennedy, Bachrach, and Bry,
Chang and Chen(2018)], DSGN [Chen et al.(2020)Chen, Liu, Shen, and Jia]
proposes an end-to-end model that simultaneously uses plane-sweep volume and
3D geometric volume to predict 3D bounding boxes. PLUMEnet [Wang et
al.(2021b)Wang, Yang, Hu, Liang, and Urtasun] directly constructs a pseudo-
LiDAR feature volume (PLUME) in 3D space. Reliable depth features extracted
from the stereo view yield outstanding performance, whereas the 3D cost volume
formed in the stereo network directs a lot of computation complexity.
YOLOStereo3D [Liu et al.(2021)Liu, Wang, and Liu], a single-stage 3D detection
network, effectively deals with the trade-off between computational complexity
and depth accuracy with light-weight cost volume. Additionally,
MobileStereoNet [Shamsafar et al.(2022)Shamsafar, Woerz, Rahim, and Zell]
introduces a way to leverage MobileNets [Howard et al.(2017)Howard, Zhu, Chen,
Kalenichenko, Wang, Weyand, Andreetto, and Adam] to reduce the computation
cost of deep networks without sacrificing accuracy. From these observations,
our method utilizes the disparity information as 3D geometric cues in multi-
view settings to enhance the accuracy of 3D object detection.

##  3 ORA3D

In the following sections, we present a novel multi-view 3D object detection
model that leverages rich information from a camera-only multi-view vision
system. Our model is built upon the state-of-the-art DETR3D [Wang et
al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] model, and we
propose to use the following two main modules: Stereo Disparity Estimation for
Weak Supervision (Section 3.1) and Adversarial Overlap Region Discriminator
(Section 3.2).

###  3.1 Stereo Disparity Estimation for Weak Depth Supervision

Existing work suggests that 3D object detectors from stereo vision can take
advantage of estimating accurate depth for objects from binocular images. Such
depth information is helpful for camera-only object detectors, which often
lack reliable depth information. Our surround-view camera setting differs from
the conventional stereo vision task – only a tiny portion of the overlap
region (i.e., less than 20%) is available. In this work, we advocate for
leveraging such overlap regions to supervise networks learning depth cues,
potentially providing better 3D detection performance.

Figure 3: Following Liu  _et al_ [Liu et al.(2021)Liu, Wang, and Liu], our
Stereo Disparity Estimation head is co-trained to compute the disparity map
from two overlapped images.

Learning Depth Cue by Multi-view Stereo Matching. When considering the multi-
view camera system, adjacent cameras have a strong association. We regard this
association comes from overlap regions and can be extended to geometric
guides. To interactively supervise the network, we train the Stereo Disparity
Estimation head, which reconstructs a dense disparity map with overlap region
pairs of neighboring cameras.

We follow the recent work by Liu  _et al_ [Liu et al.(2021)Liu, Wang, and Liu]
to implement the disparity estimation head. As illustrated in Figure 3, our
stereo network extracts features of an image pair with a standard visual
encoder. Our disparity estimation head outputs a cost volume through multi-
scale stereo matching. To obtain the target disparity map, we use the output
from the conventional stereo matching algorithm [Hirschmuller(2007)], which
performs pixel-wise mutual information-based matching.

Further, we use a binary mask to consider losses from overlap regions,
blocking gradients from non-overlap regions for training our Stereo Disparity
Estimation module. Given the known internal camera parameters and external
parameters, we first map the point $(x_{s},y_{s})$ in the source image
coordinate to the point $(X,Y,Z)$ in the world coordinate, i.e.,
$(x_{s},y_{s})^{\top}\mapsto(X,Y,Z)^{\top}$. Then, we project the point
$(X,Y,Z)$ back to the point $(x_{t},y_{t})$ in the target (or neighboring)
camera coordinate frame, i.e., $(X,Y,Z)^{\top}\mapsto(x_{t},y_{t})^{\top}$. We
provide more details in the supplemental material.

Stereo Disparity Estimation Loss. We use the following stereo focal loss
[Zhang et al.(2020)Zhang, Chen, Bai, Yu, Yu, Li, and Yang] ${\mathcal{L}}_{d}$
to optimize our Stereo Disparity Estimation head:

${\mathcal{L}}_{d}=\frac{1}{|\mathcal{I}_{o}|}\sum_{x\in\mathcal{I}_{o}}\sum_{d=0}^{D-1}((1-P_{x}(d)))^{-\alpha}(-P_{x}(d)\log{{\hat{P}_{x}(d)}})$
(1)

for pixels $x$ in the overlap region $\mathcal{I}_{o}$. Note that
$d\in\\{0,1,\dots,D-1\\}$ represents a discretized disparity, and
$\alpha\in\mathcal{R}$ is the focus weight that is set to 1.0. $P_{x}(d)$
represents the target probabilistic distribution given a disparity $d$ for a
pixel $x$ defined as follows: $P_{x}(d)=\textrm{Softmax}(-2|d-d^{gt}|)$ where
$d^{gt}$ is the target (or ground-truth) disparity map. Similarly,
$\hat{P}_{p}(d)$ is the predicted probabilistic distribution of a given
disparity $d$.

###  3.2 Adversarial Overlap Region Discriminator

In general, the human visual system can easily recognize objects at the edge
of the image as well as in the center. Furthermore, it is not difficult to
identify the same instances that appear simultaneously in different-view
images. However, empirically, we discovered that DNN-based multi-view object
detection models have a strong inductive bias toward identifying objects
individually in each image. Note that objects in the overlap region are often
occluded mainly due to limited Field Of View (FOV) of the camera or suffer
from deformation due to camera lens distortion (e.g., pincushion distortion).
Finally, we observe a domain shift effect between the overlap region (i.e.,
region far from camera center) vs. non-overlap region (i.e., region near
camera center).

In addition to leveraging stereo disparity information, which gives an
implicit bias to process the same object in different-view images together, we
propose to use a regularizer to explicitly minimize the representational gap
between non-overlap regions vs. overlap regions. Specifically, we constrain
the object detection head from learning region-invariant information via an
adversarial framework using Gradient Reversal Layer (GRL) [Ganin et
al.(2016)Ganin, Ustinova, Ajakan, Germain, Larochelle, Laviolette, Marchand,
and Lempitsky].

Given a query feature ${\bf q}_{i}$ for $i=\\{1,2,\dots,|Q|\\}$ in the
transformer (i.e., object detection decoder), an overlap region discriminator
$f_{d}$ takes such query features. Formally, this discriminator needs to
correctly predict its source region. Thus, this classifier $f_{d}$ is trained
using region classification loss $\mathcal{L}_{r}$ as follows:
${\mathcal{L}}_{r}=-\mathbb{E}_{q,y_{r}\sim\mathbb{D}}\big{[}\sum_{r\in\mathcal{R}}y_{r}\log
f_{d}(q)_{r}\big{]}$ where $\mathbb{E}_{q,y_{r}\sim\mathbb{D}}$ indicates an
expectation over samples $(q,y_{r})$, which are drawn from the (input) data
distribution $\mathbb{D}$. Plus, $f_{d}$ is trained to classify whether a
query feature is from the overlap region ($y_{r}$ is set to 1) or from the
non-overlap region ($y_{r}$ is set to 0), so the output dimension from this
module is 2 (i.e. $y_{r}\in\\{0,1\\}$). Notably, to reduce inductive bias, we
design this ordinary discriminator as a special. We intentionally make a loss
negative (i.e., $-\mathcal{L}_{r}$), preventing the discriminator from rightly
distinguishing. Finally, our network is robust to all areas, minimizing bias
that boosts performance degradation for overlapped regions.

Loss Function. Ultimately, our model is trained end-to-end by minimizing the
following loss function $\mathcal{L}_{\text{total}}$:

${\mathcal{L}}_{\text{total}}=~{}\lambda_{\text{cls}}{\mathcal{L}}_{\text{cls}}~{}+~{}\lambda_{\text{box}}{\mathcal{L}}_{\text{box}}~{}+~{}\lambda_{d}{\mathcal{L}}_{d}~{}-~{}\lambda_{r}{\mathcal{L}}_{r}\vspace{-0.3em}$
(2)

where $\lambda_{\text{cls}}$, $\lambda_{\text{box}}$, $\lambda_{d}$, and
$\lambda_{r}$ are hyperparameters that are drawn from a grid search to control
the strength of $\lambda_{\text{cls}}$, $\mathcal{L}_{\text{box}}$,
$\mathcal{L}_{d}$, and $\mathcal{L}_{r}$, respectively.

##  4 Experiments

Dataset. We use the nuScenes [Caesar et al.(2020)Caesar, Bankiti, Lang, Vora,
Liong, Xu, Krishnan, Pan, Baldan, and Beijbom] dataset, a large-scale multi-
view object detection benchmark. The nuScenes dataset provides a full
360-degree field of view captured by six different viewing cameras. This
comprises 20-second-long 1,000 video sequences, which are fully annotated with
3D bounding boxes for 10 object classes. The dataset covers 28k annotated
samples for training, and validation and test contain 6k scenes each.

Evaluation Metrics. We follow the official evaluation protocol of nuScenes
[Caesar et al.(2020)Caesar, Bankiti, Lang, Vora, Liong, Xu, Krishnan, Pan,
Baldan, and Beijbom]. We use a set of True Positive metrics (TP metrics) for
each prediction that was matched with a ground-truth box. We employ the
following 5 TP metrics: Average Translation Error(ATE), Average Scale
Error(ASE), Average Orientation Error(AOE), Average Velocity Error(AVE), and
Average Attribute Error(AAE). All TP metrics are also calculated using a 2m
center distance threshold during matching, and they are all designed to be
positive scalars. We also measure mean average precision (mAP). Lastly, we use
the nuScenes Detection Score (NDS) to measures a consolidated scalar metric
defined as follows: $\text{NDS}={1\over
10}[5~{}\text{mAP}+\sum_{\text{mTP}\in\mathbb{TP}}{(1-\textnormal{min}(1,\text{mTP}))}]$
where $\mathbb{TP}$ is five TP metrics. Note that implementation and training
details are provided in the supplemental material.

Table 1:  Comparing our 3D object detector and the state-of-the-art on the
nuScenes [Caesar et al.(2020)Caesar, Bankiti, Lang, Vora, Liong, Xu, Krishnan,
Pan, Baldan, and Beijbom] dataset. All methods based on camera modality.
$\dagger:$ trained with CBGS [Zhu et al.(2019)Zhu, Jiang, Zhou, Li, and Yu].
$\ast:$ initialized from pre-trained model on extra data. $\ddagger:$
initialized from DD3D checkpoint.

Model |  Mono$|$Multi |  Backbone |  NDS($\uparrow$) |  mAP($\uparrow$)
---|---|---|---|---
CenterNet [Zhou et al.(2019)Zhou, Wang, and Krähenbühl] | Mono | DLA34 | 0.328 | 0.306
FCOS3D [Wang et al.(2021a)Wang, Zhu, Pang, and Lin] | Mono | ResNet101 | 0.415 | 0.343
PGD [Wang et al.(2022a)Wang, Xinge, Pang, and Lin] | Mono | ResNet101 | 0.428 | 0.369
DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] | Multi | ResNet101 | 0.425 | 0.346
DETR3D† [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] | Multi | ResNet101 | 0.434 | 0.349
Ours | Multi | ResNet101 | 0.445 | 0.367

(a) Validation set

Model |  Mono$|$Multi |  Backbone |  NDS($\uparrow$) |  mAP($\uparrow$)
---|---|---|---|---
FCOS3D [Wang et al.(2021a)Wang, Zhu, Pang, and Lin] | Mono | ResNet101 | 0.428 | 0.358
PGD [Wang et al.(2022a)Wang, Xinge, Pang, and Lin] | Mono | ResNet101 | 0.448 | 0.386
DD3D∗ [Park et al.(2021)Park, Ambrus, Guizilini, Li, and Gaidon] | Mono | V2-99 | 0.477 | 0.418
DETR3D‡ [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] | Multi | V2-99 | 0.479 | 0.412
BEVDet‡ [Huang et al.(2021)Huang, Huang, Zhu, Yun, and Du] | Multi | V2-99 | 0.482 | 0.422
Ours‡ | Multi | V2-99 | 0.489 | 0.423

(b) Test set

Performance Comparison with SOTA. We compare our proposed ORA3D with existing
state-of-the-art methods including CenterNet [Zhou et al.(2019)Zhou, Wang, and
Krähenbühl], FCOS3D [Wang et al.(2021a)Wang, Zhu, Pang, and Lin], PGD [Wang et
al.(2022a)Wang, Xinge, Pang, and Lin], DD3D [Park et al.(2021)Park, Ambrus,
Guizilini, Li, and Gaidon], DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang,
Wang, Zhao, and Solomon], and BEVDet [Huang et al.(2021)Huang, Huang, Zhu,
Yun, and Du]. The first four approaches take multiple single-view images
independently and combine detection outputs for the final output using non-
maximum suppression (NMS). The last two approaches (DETR3D and BEVDet) are
state-of-the-art multi-view 3D object detection models. As shown in Table 1
(a) and (b), ORA3D generally outperforms other methods (compare the last row
vs. others) in terms of (a consolidated metric) NDS and mAP both in validation
and test data. Note that FCOS3D uses test-time augmentation and a customized
data augmentation strategy, thus requiring more epochs and model ensembles.
Though we do not use such an augmentation strategy, ours performs better than
these methods. Note that we initialize with DD3D [Park et al.(2021)Park,
Ambrus, Guizilini, Li, and Gaidon] pre-trained model and use the same backbone
(V2-99 [Lee and Park(2020)]) for a fair comparison with DETR3D and BEVDet.

Table 2: Average precision (AP) for each object class on the nuScenes [Caesar
et al.(2020)Caesar, Bankiti, Lang, Vora, Liong, Xu, Krishnan, Pan, Baldan, and
Beijbom] test set. Higher value is better. Abbr. C.V: construction vehicle,
T.C: traffic cone.

Model |  Car |  Truck |  Bus |  Trailer |  C.V |  Ped. |  Motor. |  Bicycle |  T.C |  Barrier |  mAP
---|---|---|---|---|---|---|---|---|---|---|---
DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] | 0.603 | 0.333 | 0.290 | 0.358 | 0.170 | 0.455 | 0.413 | 0.308 | 0.627 | 0.565 | 0.412
Ours | 0.609 | 0.338 | 0.323 | 0.347 | 0.174 | 0.467 | 0.420 | 0.311 | 0.649 | 0.589 | 0.423

Further, we observe in Table 2 that ours show higher mAP scores in all objects
except Trailer. Note that ours is initialized from the same backbone of DETR3D
[Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon]. This
confirms that our proposed regularization terms clearly improve the overall
detection performance.

Table 3: Detection performance comparison with the state-of-the-art approaches
for objects in the overlap region. nuScenes [Caesar et al.(2020)Caesar,
Bankiti, Lang, Vora, Liong, Xu, Krishnan, Pan, Baldan, and Beijbom] validation
set are used and all use the same backbone.

Model | Mono$|$Multi | NDS ($\uparrow$) | mAP ($\uparrow$) | mATE ($\downarrow$) | mASE ($\downarrow$) | mAOE ($\downarrow$) | mAVE ($\downarrow$) | mAAE ($\downarrow$)
---|---|---|---|---|---|---|---|---
FCOS3D [Wang et al.(2021a)Wang, Zhu, Pang, and Lin] | Mono | 0.317 | 0.213 | 0.841 | 0.276 | 0.604 | 1.122 | 0.173
DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] | Multi | 0.356 | 0.231 | 0.825 | 0.280 | 0.400 | 0.863 | 0.223
Ours | Multi | 0.408 | 0.264 | 0.677 | 0.280 | 0.361 | 0.746 | 0.181

Performance Comparison in Overlap Region. We compare our proposed ORA3D again
with existing state-of-the-art methods (FCOS3D [Wang et al.(2021a)Wang, Zhu,
Pang, and Lin], and DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang,
Zhao, and Solomon]), but we now focus on overlap regions. As shown in Table 3,
our method outperforms FCOS3D and DETR3D in terms of all metrics except mASE
and mAAE. This confirms that our overlap region aware approach effectively
deals with objects in the overlap region, resulting in a large performance
gain. For a fair comparison, all models use a ResNet101-based backbone, and
only FCOS3D uses an augmentation strategy.

Figure 4: The computing budget and performance of different paradigms on the
nuScenes [Caesar et al.(2020)Caesar, Bankiti, Lang, Vora, Liong, Xu, Krishnan,
Pan, Baldan, and Beijbom] validation set. Note that DETR3D$+$ indicates a
DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon]
model with CBGS [Zhu et al.(2019)Zhu, Jiang, Zhou, Li, and Yu]. Figure 5:
Qualitative comparison between our method (purple) and DETR3D [Wang et
al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] (green). Detected
3D bounding boxes for objects in the scene are projected into a bird’s eye
view perspective (left) and the image plane (right). See DETR3D produces more
false positives in the overlapped regions than ours.

Qualitative Analysis. Existing CNN-based methods benefit from inductive bias
even with relatively small datasets. However, large-scale datasets have become
more common, and in these environments, inductive bias easily overfits
specific datasets and causes various domain shift issues. In this section, we
demonstrate that our proposed methods address these concerns while maximizing
the capacity of multi-view camera systems.

Fig. 5 shows the visualized results of 3D bounding boxes predicted by DETR3D
(see green boxes) and our proposed method (see pink boxes). The ground-truth
bounding boxes (see blue boxes) are overlaid. We project the predicted and
ground-truth bounding boxes in the BEV perspective. In general, DETR3D and our
proposed method generate reasonable results. However, the difference in the
overlap region is apparent between the two methods, where DETR3D exhibits a
relatively large number of false positive detections. Especially, our model is
more robust for truncated or distorted objects across the entire region.
Overall, our simple and effective methods amplify accuracy both qualitatively
and quantitatively.

Analysis of Computations. We compare our proposed method with existing 3D
object detection methods, including FCOS3D [Wang et al.(2021a)Wang, Zhu, Pang,
and Lin], PGD [Wang et al.(2022a)Wang, Xinge, Pang, and Lin], BEVDet [Huang et
al.(2021)Huang, Huang, Zhu, Yun, and Du] and DETR3D [Wang et al.(2022b)Wang,
Guizilini, Zhang, Wang, Zhao, and Solomon]. As illustrated in Fig. 4, our
method outperforms other methods while requiring only a computational budget
comparable to DETR3D. Furthermore, DETR3D uses CBGS [Zhu et al.(2019)Zhu,
Jiang, Zhou, Li, and Yu], a helpful strategy for more balanced data
distribution, to achieve higher performance. This strategy, however,
alleviates the problem of data imbalance but requires more than four times the
training time. Our method without CBGS demands less training time, but has the
most impressive performance in Fig 4. Additionally, although another great
work, BEVDet, uses fewer FLOPs by using smaller-sized images, ours shows
higher scores (NDS: 0.445, mAP: 0.364). It would be worth exploring as a
future work applying our approach to BEV-based models for potential
performance improvement.

Table 4: Ablative analysis of our methods and DETR3D [Wang et al.(2022b)Wang,
Guizilini, Zhang, Wang, Zhao, and Solomon] on the nuScenes [Caesar et
al.(2020)Caesar, Bankiti, Lang, Vora, Liong, Xu, Krishnan, Pan, Baldan, and
Beijbom] mini validation set for the whole and overlap regions.

Model | Whole Region | Overlap Region
---|---|---
NDS ($\uparrow$) | mAP ($\uparrow$) | NDS ($\uparrow$) | mAP ($\uparrow$)
A. DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang, Wang, Zhao, and Solomon] | 0.338 | 0.247 | 0.266 | 0.151
B. A + $\mathcal{L}_{d}$ | 0.348 | 0.273 | 0.276 | 0.195
C. A + $\mathcal{L}_{r}$ | 0.339 | 0.286 | 0.274 | 0.215
D. A + $\mathcal{L}_{d}$ \+ $\mathcal{L}_{r}$ (Ours) | 0.353 | 0.288 | 0.280 | 0.207

Ablation Study. We evaluate the variants of our method with DETR3D with and
without the following two main loss terms: (i) $\mathcal{L}_{d}$: Stereo
Disparity Estimation loss, (ii) $\mathcal{L}_{r}$: Adversarial Overlap Region
Discrimination loss. In Table 4, we quantitatively analyze the importance of
our proposed methods for the whole region and the overlap region. Note that we
use the nuScenes mini validation set. In Table 4 Whole Region, we observe that
adding either $\mathcal{L}_{d}$ improves most of the metrics, while a similar
trend is observed by adding $\mathcal{L}_{r}$ as well. Indeed, the stereo
disparity loss is reflected during training to induce semantic geometric
potentials evenly in all areas without distinguishing between overlap and non-
overlap regions. In Table 4 Overlap Region, the effect of each component on
the overlap region is further confirmed. Most importantly, we discover the
remarkable efficiency of $\mathcal{L}_{r}$ in the target domain. The
adversarial loss completely addresses the domain shift effect by deceiving the
model into not correctly discriminating which region objects belong to. As a
result, we confirm that our components improve overall performance.

Figure 6: t-SNE visualization [van der Maaten and Hinton(2008)] of non-
overlapped (blue) and overlapped (red) regions’ features. We discover that our
Overlap Region Discriminator suitably overcomes the domain shift effect
between two regions. Best viewed in color.

Analysis of Domain Shift Effect. We suppose that the multi-view 3D object
detection method, such as DETR3D [Wang et al.(2022b)Wang, Guizilini, Zhang,
Wang, Zhao, and Solomon], without consideration of lens distortion and limited
FOV, is likely to have an inductive bias between central and outer regions,
causing a domain shift effect. To find the degree of inductive bias, we
visualize the t-SNE [van der Maaten and Hinton(2008)] results of central (non-
overlapped, blue) and edge (overlapped, red) regions’ features in Fig 6. As we
clearly see in the first row (DETR3D), the features of each two groups form
distinguishable clusters. Our method starts with this observation. The overlap
regions in multi-view images are clues to improving the overall performance.
Ultimately, the features of our method’s two groups (second row) are
distributed harmoniously, minimizing bias. This distribution indicates that
our proposed Overlap Region Discriminator successfully minimizes the
representational gap.

##  5 Conclusion

In this paper, we present a novel pipeline to enhance the performance of 3D
object detection using the 3D geometric cues in multi-view settings. We
recognize that the overlaying region between images has become a weakness due
to issues such as disconnected information and overlapping objects. However,
the overlap region is also a novel material with the potential to improve
overall performance. We develop approaches to reasonably use this small but
highly informative area. The first is a light and robust stereo disparity
estimation network for small overlap regions. This module allows the network
to fully utilize the geometric potential of binocular images. Next is the
adversarial overlap region discriminator, which is adversarially trained to
minimize the gap between non-overlap regions and overlap regions. Overall, the
experiments demonstrate that our two methods successfully work on detection
accuracy.

## Acknowledgement

This work is supported by Autonomous Driving Center, Hyundai Motor Company R&D
Division. S. Kim, W. Roh, G. Chang, and C. Kim are partially supported by the
National Research Foundation of Korea grant (NRF-2022R1F1A1074334) and
Institute of Information & communications Technology Planning & Evaluation
(IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079,
Artificial Intelligence Graduate School Program(Korea University). S. Moon and
J. Kim are partially supported by the ICT Creative Consilience program
(IITP-2022-2022-0-01819) and the ITRC(Information Technology Research Center)
support program (IITP-2022-RS-2022-00156295).

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