Towards Learning Group-Equivariant Features for Domain Adaptive 3D Detection
Authors: Sangyun Shin
Yuhang He
Madhu Vankadari
Ta-Ying Cheng
Qian Xie
Andrew Markham
Niki Trigoni
Venue: NeurIPS 2024 poster (2024)
Keywords: Domain Adaptive 3D Detection
Domain Adaptation
3D Detection
Self-Training
Primary area: machine_vision

Abstract: The performance of 3D object detection in large outdoor point clouds deteriorates significantly in an unseen environment due to the inter-domain gap. To address these challenges, most existing methods for domain adaptation harness self-training schemes and attempt to bridge the gap by focusing on a single factor that causes the inter-domain gap, such as objects' sizes, shapes, and foreground density variation. However, the resulting adaptations suggest that there is still a substantial inter-domain gap left to be minimized. We argue that this is due to two limitations: 1) Biased pseudo-label collection from self-training. 2) Multiple factors jointly contributing to how the object is perceived in the unseen target domain. In this work, we propose a grouping-exploration strategy framework,  Group Explorer Domain Adaptation ($\textbf{GroupEXP-DA}$), to addresses those two issues. Specifically, our grouping divides the available label sets into multiple clusters and ensures all of them have equal learning attention with the group-equivariant spatial feature, avoiding dominant types of objects causing imbalance problems. Moreover, grouping learns to divide objects by considering inherent factors in a data-driven manner, without considering each factor separately as existing works. On top of the group-equivariant spatial feature that selectively detects objects similar to the input group, we additionally introduce an explorative group update strategy that reduces the false negative detection in the target domain, further reducing the inter-domain gap. During inference, only the learned group features are necessary for making the group-equivariant spatial feature, placing our method as a simple add-on that can be applicable to most existing detectors. We show how each module contributes to substantially bridging the inter-domain gaps compared to existing works across large urban outdoor datasets such as NuScenes, Waymo, and KITTI.

Towards Learning Group-Equivariant Features for Domain Adaptive 3D Detection
Sangyun Shin' Yuhang Het Madhu Vankadari" Ta-Ying Cheng
Qian Xie Andrew Markham Niki Trigoni
'Dppartment of Computer Science, University of Oxford, United Kingdom 'Scoool of Computer Science, University of Leeds, United Kingdom
https: http//uunshiin/rruuppp/pp gitttt sangyun. shin@cs. sangyun.shin&ss. ox. ac.uk
Abstract
The performance of 3D object detection in large outdoor point clouds deteriorates significantly an unseen environment due to the inter-domain gap. To address these challenges, most existing methods for domain adaptation harness self- training schemes and attempt to bridge the gap by focusing on a single factor that causes the inter-domain gap, such as objects' sizes, shapes, and foreground density vari- ation. However, the resulting adaptations suggest that there still a substantial inter-domain gap left to be minimized. We argue that this is due to two limitations: 1) Biased pseudo-label collection from self-training. 2) Multiple factors jointly contributing to how the object is perceived in the unseen target domain. In this work, we propose a grouping-exploration strategy framework, Group Explorer Domain Adaptation (GroupEXP-DA)) our grouping divides the available label sets into multiple clusters and ensures all of them have equal learning attention with the group-equivarian spatial fea- ture, avoiding dominant types of objects causing imbalance problems. Moreover, grouping learns divide objects by considering inherent factors data-driven manner, without considering each factor separately as existing works. On top of the group-equivariant spatial feature that selectively detects objects similar the input group, we additionally introduce an explorative group update strategy that reduces the false negative detection the target domain, further reducing the inter-domain gap. During inference, only the learned group features are necessary for making the group-equivariant spatial feature, placing our method as a simple add-on that can be applicable to most existing detectors. We show how each module contributes to substantially bridging the inter-domain gaps compared to existing works across large urban outdoor datasets such as NuScenes, Waymo, and KITTI.
addresses those issues. Specifically,
Introduction
I Learning 3D object detection [42, 12, 27] and segmentation [50, 11, 32, 10] in large outdoor point cloud scenes is of increasing importance due its wide range of applications, such as autonomous driving [19] and Augmented Reality (AR) [14]. However, annotating such 3D data is expensive and labor-intensive. This limits the applicability and generalization of off-the-shelf models to diverse real-world applications. To mitigate the dependency large-scale datasets with labels, studies for Domain Adaptation (DA) have gained considerable attention from the community [38, 25, 41, 46, 44, 43, 20, 17, 40, 8, 3, 31, 7]. In the context of 3D detection, DA is motivated by utilizing knowledge learned from a source domain to adapt to a target domain using a pseudo-labe set.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Typically, in the widely used self-training scheme [44, 43], a detection model is pre-trained on the source domain and constructs an initial pseudo-label set for re-training in the target domain. The pseudo-label set progressively expands as more pseudo-labels are collected after each retraining. Here, the pseudo-label set consists of detections with high confidence scores. Based on the principle of self-training, recent existing works focus on specific factors that cause the inter-domain gap. These can be broadly categorized into domain variations in object sizes [38], density [8], or geometric structure [17, 20]. Despite good progress in bridging the inter-domain gap, two challenges remain unsolved: 1) Biased pseudo-labels due to the conservative collection strategy of the self-training scheme 2) Strict separation of multiple factors that jointly contribute to the creation of dnter-domain gap. Typically, one domain, a group of objects sharing common features outnumber other objects having different features, as addressed in existing works [38, 8, 17, 20J. While this may not cause significant performance deterioration in the source domain, where the environment is similar, could cause bias under the self-training scheme. For example, detection with high confidence scores typically comes from objects belonging to dominant groups, as they are the ones that the detector learns the most of in the source domain. As shown in Figure 1 (a), the recall of objects in the target domain is significantly higher when the objects contain similar features as the dominant objects in the source domain, ignoring other objects. As a partial solution to this problem, existing works focus on a single factor only to address the inter-domain gap e.g. size or point cloud density. However, as object's appearance is Domain Distributions
Recall after Adaptation in Target Domaïn
400
350
1,0
1.0
Foneground Density
300-
Foreground Demsity
100.6
100,6
-250
200
200.2
2002
150-
209 8-
299.8
100-
50
399.4
399.0
10
7.3
40.5
$2.0
7.1
18.3
29.5
$40.8
52.0
0.0
Object Volume
Object Volume
Object Volume
Figure I: Several factors causing the inter-domain gap, such as the point density and object volume in NuScenes (Target) and Waymo (Source) datasets are illustrated with the fitted multivariate Gaussian distribution (left). The baseline [43] adaptation primarily detects objects having features near the mean of the distributions indicated by red circles. On the other hand, the proposed adaptation first groups objects and explores the target domain to reduce the false negative. The heatmaps show average recall (right). Objects with extreme sparsity are excluded for clear visualization.
Figure shows, single factor cannot explain all the domain variation result of multiple factors jointly influencing the foreground points.
In this work, we address the aforementioned two issues of the current domain adaptive 3D detection. Our core intuition comes from the fact that the factors causing inter-domain gaps often already exist in the source domain to an extent. For example, the density of points often varies due to the distance and viewing angle of the sensor even inside domain. Object sizes and shapes would also vary even in one single domain. Based on this observation, we aim to reduce the bias of the detection model to dominant objects by finding groups and evenly distributing the detector's attention during learning to all groups that would be otherwise largely neglected due to less dominant features. Nevertheless, finding optimal groups that represent the intra-domain gap is not straightforward because multiple factors jointly contribute to variations in objects' appearances. To address this issue, introduce data-driven grouping method that finds object groups with different characteristics. The groups are then progressively updated for adaptation, redistributing the available labels according each group to learn the different characteristics found for each group in the target domain. Our contributions can be summarized as follows:
1. We introduce new alternative approach for domain adaptation, Group Explorer Domain Adaptation, (GroupExp-DA), which reflects on the available labels in order to understand the target domain, bridging the inter-domain gap.
2. To ensure each object group receives equal attention for learning, we introduce the Group- Equivariant spatial feature, which is learned for selectively detecting objects similar to the input group. preventing dominant types of objects from causing imbalance problems.

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3. To make the best use of the Group-Equivand Equivariant feature's 's selective detection ability depending on the imput group. we propose an exploration strategy that encourages the groups to reflect the target domain by redistributing available labels, leading to fewer false negatives for the adaptation.
4. Extensive adaptation experiments on KITTI, Waymo, and NuScenes datasets show the effective- ness of our approach for bridging inter-domain gaps.
2 Related Work
Point Cloud-based 3D Detection for Outdoor Scene. Existing methods for 3D point cloud-based object detection can be divided into two main categories: point-based and voxel-based. Based on PointNet series [21. 24], point-based methods [233, 29, 29, 22, 45] propose to extract features from unordered and unstructured raw point-cloud directly. Voxel- based methods divide the unstructured point clouds into regular voxel grids and are fed into encoders either based on sparse convolution [42, 51, 12, 30, 26, 2, 9, 48] or Transformer [34, 16, 5, 37, 15]. The encoded spatial features, which are also called Bird's Eyes View (BEV) features, are then fed into Regional Proposal Networks (RPN) for the final detection. There are also few methods that combine point- and voxel-based methods [27, 28, 391. Currently, in terms of performance, voxel-based detectors dominate the point-cloud-based 3D detection for outdoor scenes. Therefore, we employ Second-loU [42] and PointPillars [12] as the base detectors for our experiments, as they are most-widely used detectors that existing works are built on.
The target of domain adaptive 3D detection is to mitigate the inter- domain gap between the source and the target by focusing on several factors, such as object size [38, 25], point cloud deterioration [41], and the encoder separation for domains [46]. ST3D series [44, 43] propose self-training scheme that progressively collects pseudo-label sets in the target domain for retraining. Built on self-training scheme, DA for 3D detection has been extensively studied to acquire higher quality pseudo-labed labels by addressing the inter-domain gap caused by geometric shape with prototype learning [20, 17], toose-e density variation using knowledge distillation [40], general density variation with beam augmentation and knowledge distillation [8], cross-domain examination to measure the consistency of pseudo-labels [3] and focusing on specific architecture [49]. A considerable number of existing works focus single factor, such density, shape, and size, to bridge the inter-domain gap. Instead, we attempt to see the inter-domain gap as a result multiple factors combined and bridge the gap by finding inherent groups.
Domain Adaptive Detection.
3 Method
3.1
Framework Overview
Following the self-training--asse unsupervised domain adaptation scheme [44, 43] for 3D detection, we are given point clouds X = X, U X, and labels Y= Y, Y as the initial set. Here, X, and Y, point could and box labels of the known source domain. On the other hand, X, and Y, the point cloud and initial pseudo label set the unlabeled target domain. Y, is collected by the detector trained only the source domain using X, and Y, A label in Y consists of seven parameters defining a 3D box with three parameters for center (z, y, 2). three parameters for size (I, w, h), and one parameter for vertical rotation 0. Our goal is improve the detector's 's inter-domain adaptation by focusing on the pseudo-label collection. In particular, instead of treating all available objects in Y equivalently, aim to understand objects in Y better by grouping.
Specifically, as depicted in Figure 2 and Alg. 1, we first extract foreground points from available boxes and learn to encode objects into the object descriptor from the points (Sec.3.2). The descriptors are then used to progressively group objects (Sec. 3.3). Our Group Region Correlation (Sec. 3.4) takes the group features input and fuses with RPN to selectively detect objects similar to the individual group
3.2 Object Descriptor Extraction
Given a pair of point cloud E x3 and label set y E R consisting of np points and ni boxes from X and Y. respectively, this module aims to produce learnable object descriptor Foby E Xdoky during training. First, foreground points, Potj {p Jk=1 are extracted using
3

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Backbone
RPN
Fbev
Point Cloud
Feber
3D Box Detection
Group-Region Correlation (Sec. 3.4)
(Sec, 3.2)
Existing Pipeline
Object
Used for Training Testt
Descriptor
Grouping & Exploration (See. 3.3)
Used for Training
Pobj
7bb Pobl
P Pabj
Extraction
Fobj
Figure 2: Overall pipeline of proposed method. During training, extract foreground points from existing 3D box labels and feed them to the Object Descriptor Extraction module to acquire object descriptors. The descriptors used for grouping & exploration and fed into the Group- Correlation module to generate RPN to detect objects similar to each group.
from the input point cloud Our object descriptor extraction module then takes Poby as input and outputs object descriptors Foo; using neural networks consisting of MLP and global max pooling, which are adopted from [21, 13J. The motivation behind the architectural choice is two-fold: (1) An arbitrary number of object points, Pohj" can be processed efficiently without involving comparably slow sampling techniques. (2) Global max pooling offers permutation invariant features, which helps Fotj less prone to overfitting by certain permutations from viewing angles, etc. During training, Fabj serves the input for the progressive grouping process and is not used during inference.
3.3 Progressive Grouping
Determining similarities between objects is complex problem as intra-object variations are created by various factors, such as density, shape, size, etc. To find groups that, when combined, explain this intra-object variation, we utilize Gaussian Mixture Model (GMM) based grouping for the following advantages: (1) GMM better captures the heterogeneity of data using only a few more parameters, such covariance and weights, compared to the proximity- -bsed methods. (2) The parameters that define groups can be efficiently updated with weighted linear combinations and are also differentiable for learning the groups in data-driven manner. In the following sections, explain the details of how the groups are initialized, determined for each sample, and updated.
Initialization Given all available pairs of scans and labels, X and Y, we first extract Fabj from each pair and stack them. All of the stacked {Fo, Rtutal are then used for the initialization. Here, Nosoal stands for the total number of pairs in X and Y. Specifically, we initialize ng groups using K- Means clustering on {F obj Pitote where each cluster forms group. After this, Maximum Likelihood
Estimation is performed for each group acquire parameters, such mean fr Xdes, covariance E Re Xdaky Xdaty, and weight & E "R that define a GMM-based group. This initialization is required only
before the training. In the following, all procedures based single pair of scan and label, x and which can be extended to batch-wise operation.
Determining Group for New Sample The probability ph-i of k-th sample belonging i-th group estimated as
O (FF oby 1 o³)
pk-t
(1)
En (NE obg lut, Øt)²
where A((.)) ) is the probability density function of multivariate Gaussian distribution that outputs the likelihood of sample Fi, given mean μ and covariance σ. The group labels G E TR for all the ma samples Fobj are then acquired using P. That is, the group label G for k-th sample is specifically determined as:
G arg max pk-
Explorative Update during Training Typically, pseudo-label sets from the target domain are incorporated into the existing label set from the source domain and considered the same label set

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* Group Centroid
Label (Source)
Pseudo-label
Unfound Lahels in Target
GroupyDecision Boundary -
Explomation Direction
Baseline
Grouping
Explorative Update
Figure 3: Conceptual diagram comparing pseudo-label generation processes (a) baseline [44, 43] with our (b) grouping followed by (c) the explorative update using pseudo-labels.
for training [44, 43], as shown in Figure 3 (a). However, we argue that for the domain adaptation, the pseudo-labed set should be given more weight on its usage than the source label sets because they contain inherent features of the target domain, which can be used to understand the target domain in terms of objects' appearances. To address this, we introduce an explorative group update strategy using pseudo-labels. For i-th group, its mean ju RRda,, covariance o' Rd Xdots, and the group weight @' are acquired follows:
b,s
8¹
Σ (F
(F) obj,t Ri)T, 3 Tbb.i
(3)
Tub,5 Rmm
Tbb
T2b
Rmm
where Fobj. is subset of Foby that belong to i-th group using G in Eqn. as:
Fobj,i { G = =i),
(4)
and no.s is the number of samples in Forg. Similar to the update rule for prototype learning [20]. each group parameters updated with linear combination
== p= ap
a) o= ao + (1-a)ō,
a a)
(5)
where a is coefficient that affects how conservatively each parameter is updated. After the update, n' is considered as a representative of i-th group and utilized as a query input for generating group- equivariant spatial features. Accordingly, during training, the samples that belong to the i-th group are used as the foreground boxes. Intuitively, this process distributes source labels according to groups found in the target domain that the source labels used for learning find similar objects in the target domain, as shown in Figure (b) and (c).
For training, to ensure that the groups learn to be distinctive enough, we adopt inter-group repel [17] based on contrastive loss.
ng 1
Drep
Σ
mar(0,cco((L ,e))),
(6)
where cos(.) ) is a function that calculates cosine-simillarity between two inputs. addition, to encourage similar features for group cohesion, intra-group attraction loss, Tatt is used:
Rg
Latt
FF Fojj p ) =
=1k=
where k i| is indicator function that 1 if & i and 0 otherwise. During the inference, only the learned y is necessary for the following Group-Region Correlation.
3.4 Group-Region Correlation
In typical voxel-based 3D object detector's pipeline, voxelized points are fed into the backbone, which outputs the spatial feature Fbev E H xdeer as shown Figure Pbev then fed into
RPN to make final box predictions. Given input group queries μ {4²)) 1==1, pº Rdot and Feev. Our Group-Region Correlation aims at producing the spatial features Fetev n x x that
5

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Algorithm Training Pipeline
Require: point-cloud x, (pseudo) labels y. Group parameters G
1: Extract obj. descriptors Fotj using x and (Sec 3.2)
2: Determine groups of Fobj and update G (Sec 3.3)
3: Update G using Fob, according to the determined groups (Sec 3.3)
4: Calculate I.rep and Lutt as Sec 3.3
5: Extract spatial feature Fbee from the backbone (Sec 3.4)
6: Make group equivariant features for each group using G and Fbee (Sec 3.4)
7: Predict boxes using shared RPN for each group equivariant feature
8: Calculate Ldet as in Eq. 10.
9: Calculate gradients using Lreps Latts and Leat-
10: Update all modules' weights using the gradients
equivariant to the group query so that Febeu provide selective features for detecting objects similar to each group. In the following sections, we explain how each group and spatial feature are correlated and then used by RPN detect the corresponding objects.
Group Equivariant Spatial Feature The aim of Group-Region Correlation module to make spatial features Fbev selectively attend to objects that are similar to the query group. The following RPN then detects only certain objects that are similar to the query group. To achieve this, we utilize cross-attention with μ as query and Fbee as key and value to encourage features from μ and Fbe to cross-attend to generate necessary features. Intuitively, μ is compared to Fbev to find the object that is similar to each group. For i-th group, the attended group-equivariant spatial features Fedeu E R x W xdsev are acquired as: Fiber cAttn( Fbev: Fbeu),
(8)
where cAltn(.) refers to the cross attention [36] that takes query, key, and value as input and outputs the cross-attended feature. Here, μ² the i-th query in j.. The group-equivariat spatial features Fober can then be fed into any existing RPN strcutures [42, 4, 12] to detect foreground objects for each group. The ground-trui truth boxes in y that belong to the i-th group are utilized as the foreground boxes for Fx obey to train the follwing RPN.
Regional Proposal Network (RPN) Following the general architecture of RPN [42, 12], our object- ness and box regression heads take Febev E IR. Rx as input and predict objectness scores Fas " # xxX and box parameters Fhoz RRxxx to form 3D boxes on the dense spatial grid corresponding to Febev-
For the training of i-th group, standard training losses for RPN based detection, I applied as existing works [42, 12, 30, 26, 2, 9, 48). Specifically, given the box labels y², Fats and Fi are used for calculating first-stage box detection training loss, Laeet, as:
L' L'jocut + Lion.
(9)
where foont stands for Focal Loss [18] and Liea is box regression loss. Here, the foreground labels and regression targets for Lfocul and Looz are calculated using y' depending on the individual base detectors' configurations. Similarly, for the second-stage box refinement training, F Feber is used with Fats and Fooz for Rol Pooling. Then, the pooled features are fed into classification and box regression head for refinement with architectures depending on the detectors to calculate the second stage loss Lt D The detection loss for all groups, Lasts is then acquired by iterating over all groups as:
Tedet
1
Σ
ag
Tiaen + T.
(10)
1gg
1=1
3.5 Overall Training
Apart from Lyep and Lott for grouping, our overall training losses are defined the same as the general RPN learning for detection.
= X Lrep + AgLast Aa
(11)
Using I. we train our system following the self self-training scheme [44, 43]. Further details can be found in B.

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Experiment
4.1 Datasets
We evaluate our methods against various baselines across three different datasets, such as KITTI [6], NuScenes [1], and Waymo [33]. KITTI contains 7481 frames of point clouds for training and validation, and all the data collected with 64-beam Velodyne LiDAR. NuScenes dataset contains 28130 training and 6019 validation point clouds collected with a 32-beam roof LiDAR. Waymo dataset contains 122000 training and 30407 validation frames of point clouds collected with five LiDAR sensors, i.e., one 64-heam LiDAR and four 200-beam LiDAR.
4.2 Implementation Details
For a fair comparison with existing domain adaptive 3D detection methods, we build our model on
two base detectors, Second IoU [42] and PointPillars [12], following [44, 43, 17, 8], that are widely used and applicable to most recent detectors with the implementation based on OpenPCDet [35] and parameters from ST3D [44]. Following [17], we first train each detector for 50 epochs with batch-size 8 as a pretraining step using single NVIDIA A10 GPU. In the self-training stage, we train 30 more epochs for the tuning to adapt to the target domain. The learning rate is set to 1 15 using Adam optimizer with Cosine annealing [47] for scheduling the learning rate. In order to improve the learning stability, the baseline RPN is also learned in addition the group learning. While the group equivariant RPNs are used for the pseudo label collection during self- self-training, only the baseline RPN used for the final testing, ensuring the same execution speed as the existing pipeline. The feature dimensions doev and dobj for Fbee and μ are both set to 512 for c.tttn. for updating the group parameters empirically set to 0.8. ' A2 and Xg are set to 0.5, 0.5, and 1.0, respectively.
4.3 Comparing Methods
We compare recent existing 3D domain adaptive detection methods, such SN [38], 3D-CoCo [46], ST3D [44, 43), GPA-3D [17] and DTS [8] with our proposed method. As our method based on the self-training, we set ST3D [44] as our baseline and show experimental results by comparing with more recent methods. Additionally, we also illustrate the performance the oracle models, which refer to fully-supervised model on the target domain directly as upper bound. Following the most recent works [17, 8], all methods are compared in three adaptation scenarios focusing on "car" class: (I) Waymo- NuScenes (2) NuScenes + KITTI (3) Waymo - KITTI.
4.4 Evaluation Metric
Following [44, 43, 17, 8], we adopt Average Precision(AP) as our primary evaluation metric and evaluate model on Bird-Eyes- View (BEV) IoU, APBEV, and 3D Box IoU, APsd. with 40 varying recall points and 0.7 as the IoU threshold.
4.4.1 Quantitative Result
Waymo + KITTI Table 1 (first task) shows the quantitative results of 3D detection in APREY and
A APsn When using Second-loU as detector [42], our proposed method outperforms the baseline ST3D series for 4.75/11.88 in APBEv/APts respectively. Compared with the SOTA method, DTS [8],
1.14/2.21 improvements are made. When using PointPillars as the base detector [12], our approach gains 2.34/3.91 improvements compared to the best performing method, GPA-3D [17].
NuScenes + KITTI As shown in Table (second task), our approach shows 0.97/5.80 performance gains in terms of APrey/AP's with Second-loU [42] as the base detector. Compared with SOTA
DTS [8], 0.07/1.60 60 improvements are acquired. With PointPillars [12] as the base detector, our approach exceeds the baseline and DTS by 21.49/41.74 and 2.39/1.04, respectively.
Waymo - NuScenes Table (third task) illustrates the adaptation results. Our approach outperforms the baseline and the best-performing method by 8.55/5.72 and 3.27/2.91 in APREy/ APay/APssp. respectively. with Second-IoU as the detector. Similarly, with PointPillars as the detector, 14.89/7.6 884 and 32.8/1 .4 improvements gained compared with the baseline and SOTA in terms of APs../APP..
7

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Table 1: Quantitative comparisons the recent domain adaptive 3D detection methods on three
adaptation scenarios. The top-3 performing methods are labeled in different colors.
SECOND-IOU
PointPillars
Task
Methods
APPEv/APPa
Closed Gap
AP'EEv/APaD
Closed Gap
Source Only
67.64/27.48
47.8/11.3
SN [38]
78.96/59.20
72.33/69.00
27.4/6.4
-55.14/-8.49
3D-CoCo [46]
76.1/42.9
76.49/52.25
ST3D [44]
02.19/61.88
92.97/74.72
58.1/23.2
27.84/19.47
ST3D++ [43]
80.78/65.66 6,64
83,96/83.01
Waymo -> KITTI
GPA-3D [17]
83.79/70.88
103.19/94.41
77.29/50.84
79.70/65.44 46
DTS [8]
85.80/71.5
115.9/95.7
76. 16.1/50.2
76.50/64.4
Ours
86.94/73.70
123.2/100.4
78.44/54.11
82.81/71.0
Oracle
83,3/73.55
84.8/71.6
Source Only
51.8/17.9
22.8/0.5
SN [38]
59.7/37.6
25.1/35.4
39.3/2.0
26.6/2.1
3D-CoCo [46]
77.0/47.2
87.4/65.7
ST3D [44]
75.9/54.1
76.6/59.5
60.4/11.1
60.6/14.9
NuScenes - KITTI
ST3D+ [43]
80.5/62.4
91.1/80.0
DTS [8]
81,4/66.6
94.0/87.6
79.5/51
91.5/72.2
Ours
81.47/68.2
98.3/90.0
81.89/52.84
95.3/73.6
Oracle
83.3/73.5
84.8/71.6
Source Only
32.91/17.24
27.8/12.1
SN[38]
33.23//8.57
1.697.554
17.1999
2.41/4.58
3D-CoCo [46]
33.1/20.7
25.00/44.79
ST3D [44]
35.92/20.19
15,87/16.73
30.6/15.6
13.21/18.23
ST3D++ [43]
35.73/20.90
14.87/20.76
Waymo NuScenes
GPA-3D [17]
37.25/22.54
22.88/30.06
35.47/21.01
36.18/46.41
DTS [8]
41.2/23.0
43.7/32.80
42.2/21.5
67.9/49.0
Ours
43.84/24.42
57.56/40.66
44.31/22.15
77. 77.88/52.34
Oracle
51,9/34.9
49,031.3
(a) Fround-Truth
(b) Bascline
DTS
Ours
Figure 4: Qualitative comparison of Baseline ST3D [44], DTS [8] and ours NuScenes to KITTI
adaptation scenario (top) and Waymo to NuScenes(bottom) adaptation scenarios.
4.4.2 Qualitative Result
Figure 4 compares the 3D detection results of the baseline [44], DTS [8] and our methods. Due to the conservative pseudo-label selection policy and absence of methods addressing variations in object size or foreground density, the baseline struggles to detect objects with comparably less common sizes or further away with different densities (red circles in 1st row (b)). Moreover, some dominant object shapes make the detector overfit, leading to false positive detection of road structure (red circles in 2nd row (b)). DTS [8] improves the inter-domain density variance problem presented in the baseline. However, it still encounters the overfitting problem to certain geometric shapes in the source domain, leading to same false positive detection of the road structure as the baseline (red circles in 2nd row (c)). Moreover, despite the training for foreground density invariant, the objects appearing sparse due to the distance remain false negative (red circles in 1st row (c)) DTS. The observation suggests that another factor, in addition to the foreground density, causes the inter-domain gap. On the other hand, our proposed method improves both false positive and negative detections, demonstrating more robust adaptation ability than DTS, which is the best-performing method.

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Table 2: Impact of each component in APBEV and APan on Waymo to NuScenes adaptation.
71,66
73:71
73
22-27
87,30
Group Latt Lrepel Exp. Up.
Afbey
Alon
67,9
67.9
62.3
(a)
35.92
20.19
(b)
39.48
22.66
50
54.1
(c)
40.82
23.14
(d)
41.14
23.61
30
(C)
41.64
24.55
23,9
25.5
25,9
25.7
24.5
(1)
44.47
25.91
Table 3: Impact of grouping methods and a on
W W
NuScenes to KITTI adaptation.
Proximity-base
GMM-baser
ng
AThey AD
APhey
Al'D
Figure 5: Impact of ng on three adaptation scenar- AP3D. Here W + K, K, and + NuScenes
o=0.4
79.82
64.77
81.37
66.95
0=0.6
81.02
65.63
82.29
67.52
N refer to Waymo and Waymo-NuSeenes adaptations.
a=0.8
81.61
66.20
82.78
67.93
0=0.99
81.95
66.83
81.80
66.86
Gme?
Doep
Grapp
Pi
-55
Figure 6: Comparison of DTS [8] (left), ours without explorative update (middle), and ours with explorative update (right) in Waymo > NuScenes with t-SNE visualization. Here, the foreground features extracted using ground-truth boxes using Frev for DTS and Febev for ours.
4.5 Ablations
Impact of Each Component Table shows the impact of each component in Waymo Waymo-NuScenne adaptation using Second IoU [42] in terms of A APBEv/APpp For this experiment, we progressively add each core component to the baseline (a) see how they affect the performance. As grouping ensures every group has similar attention during training and prevents only a few dominant object types from having high confidence scores, significantly improves the performance 9.91 1/662.29% from the baseline. From this result, Latt (c) and Lyepet (d) improve 3.39% 11% and 4. 4.20% respectively, suggesting that making the groups distinctive has more impact than making the intra- group cohesive. This is expected because, during the group- couup-qquiaaiiant RPN training, samples inside each group are already learned to be cohesive against background and samples from other groups, indirectly supervised to be close to each other. Nevertheless, when combined in (c), Latt and Lyepel improves 15.92%/21 229.%%%% from the baseline, demonstrating the synergy of the two losses. On top of this, the explorative update considerably boosts the performance by 23.80%/28.0 333%, proving that using samples found in target (pseudo-labes labels) for the update further reduces the inter-domain gap, as also be seen in Figure (a group of FN in the middle disappears in the right figure).
Impact of fly illustrated Figure 5 in AP for all adaptation scenarios presented in the main experiments. For this experiment, we include all components with the same hyper-parameters and only change ng to see the impact. As can be seen, increasing n, improves the performance, reaching the top around when n2 is set to 4. In principle, having many groups would make the group-equivarian RPN discover more object types while ensuring the same attention for each group during learning. However, in practice, the performances start decreasing when ny is set around 6, suggesting that having too many groups increases the risks of overfit problems due to a small number of objects in each group or underfit problems due to high complexity for learning Febev- We also find that the training result is most stable when n, is set 3.
Effect of GMM based Grouping and a are illustrated in Table 3 to compare: (1) Proximity-based grouping and (2) GMM-based grouping with varying a. For the proximity-based grouping, we discard and o from GMM, and determine the group of each sample the closest μ from the

---

samples using L2 distance while all other configurations stay the same. As can be seen, GMM-based grouping constantly shows better performances both APrey/AP's in all a. This is due to o and ø that preserve the characteristics of each group more compared to only le based on previously seen samples, improving the stability during learning with similar objects grouped together. Additionally, setting too large or small degrades the performances, as small a discourages the explorative update, while too large a could result in instability as the input j for training RPN constantly changes more. Also, setting a 0.6 results in 0.49/0.66 higher in A/Bev/APPs compared to setting suggesting the advantage from exploration surpasses the stability. Interestingly, unlike GMM-based grouping, setting higher constantly shows increasingly better performance with the proximity- based grouping, proving inherent instability without a and ø. Nevertheless, in nearly all the configurations of a, the GMM-baser grouping persistently outperforms the best pest-performing method, DTS [8].
0.99,
5 Conclusion and Limitations
In this paper, we present GroupExp-Di DA that learns object groups, which can be used to bridge the inter-domain gap with (1) less bias by the dominant objects in the available label sets and (2) consideration of multiple factors for creating inter-domain gaps in a data-driven manner. This is achieved by utilizing the group equivariant spatial features that connect the group feature and spatial features to be learned together with the existing detection loss function. Nevertheless, all methods, including ours, struggle to detect objects with extremely sparse foreground points (black circles in 2nd row), shown in Figure 4, because those objects do not contain distinctive features to being well-learned as groups due to extreme sparsity, which remains as one the challenges to be explored.
Acknowledgments
This work was supported by Innovate UK Grant No: 10107189 "ScanSpot: 3D-Modelling "Digital Twin" Data For New Insurance Products".
References
[1] H. Caesar. V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, "nuscenes: A multimodal dataset for autonomous driving." " in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, pp. 11 1261211166 631. 7
[2] Y. Chen, J. Liu, X. Zhang, X. Qi, and J. Jia, "Largekemell3d: Scaling up kemnlss in 3d sparse cnns," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 13 488 13 498. 3,6
[3] Z. Chen, Y. Luo, Z. Wang, M. Baktashmotlagh, and Z. Huang, "Revisiting domain-adaptive 3d object detection by reliable, diverse and class-balanced pseudo-labeling." in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 3714-3726. 1,3, 15
[4] K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, "CenterNet: Keypoint Triplets for Object Detection," " in IEEF/CVF International Conference Computer Vision (ICCV), 2019. 6
[5] L. Fan, Z. Pang. T. Zhang, Y.-X. Wang, II. Zhao, F. Wang, N. Wang, and Z. Zhang, "Embracing single stride 3d object detector with sparse transformer." in Proceedings of the IEEE/CVF conference computer vision and pattern recognition, 2022, pp. 8458-844 8468.
[6] A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite." CVPR, 2012.7
[7] A. W. Harley, Y. Zuo J. Wen*, A. Mangal, S. Potdar, R. Chaudhry, and K. Fragkiadaki, "Track, Check, Repeat: An EM Approach to Unsupervised Tracking." " in CVPR, 2021.
[8] Q Hu, D. Liu, and W. Hu, "Density- Insensiivvee Unsupervised Domain Adaption on 3D Object Detection," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 1, 3, 7, 8, 9, 10, 14
[9] x. Jin, K. Liu, C. Ma, R. Yang. F. Hui, and W. Wu, "Swiftpillars: High-efficiency pillar encoder for lidar- based 3d detection," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 3, 2024, pp. 2625-2633. 6,6
10

---

[10] M. Kolodiazhnyi, A. Vorontsova, A. Konushin, and D. Rukhovich. "Oneformer3d: One transformer for unified point cloud segmentation," in Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, 2024.
[11] X. Lai, Y. Yuan, R. Chu, Y. Chen, H. Hu, and J. Jia, "Musk-attention-frec transformer for 3d instance segmentation." " in Proceedings of the IEEE/CVF International Conference Computer Vision (ICCV). 2023.
[12] A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, "Pointpillars: Fast encoders for object detection from point clouds," in Proceedings of the IEEF/CVF conference on computer vision and pottern recognition. 2019, pp. 12 127-112 705. 1.3.6.7
[13] Lang. A. Manor, and S. Avidan, "Samplenet: Differentiable point cloud sampling." in Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recognition, 2020, pp. 7578-7588.
[14] V. V. Lehtola, H. Kaartinen, A. Nüchher, R. Kaijaluoto, A. Kukko, P. Litkey, E. Honkavaara, T. Rosnell, M. T. Vaaja, J.-P. Virtanen, et al., "Comparison of the selected otate-of-the-art 3d indoor scanning and point cloud generation methods," Remote sensing, vol. no. 8, p. 796, 2017.
[15] J. Li, S. Dong, L. Ding. and T. Xu, "Mssvt++; Mixed-scale sparse voxel transformer with center voting for 3d object detection," IEEE Iransactions on pattern amalysis and machine intelligence, 2023, 3
[16] Y. Li, Y. Chen, X. Qi, Z. J. Sun, and J. Jia, "Unifying voxel-based representation with transformer for 3d object detection," Advances in Neural Information Processing Systems, vol. 35, pp. 18 482-4888844 455, 2022. 3
[17] Z. Li, J. Guo, T. Cao, B. Liu, and W. Yang, "GPA-3D: Geometry-aware Prototype Alignment for Un- supervised Domain Adaptive 3D Object Detection from Point Clouds," in International Conference on Computer Vision (ICCV), 2023. 3, 5, 7,8
[18] T.-Y, Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal loss for dense object detection," in Proceedings of the IEEE international conference computer vision, 2017, pp. 2980-2988. 6
[19] K.-B. Park, M. Kim, S. Choi, and J. Y. Lec, "Deep learning-basee based smart task assistance in wearable augmented reality," Robotics and Computer-Integratee Manufacturing, vol. 63, 101887, 2020.
[20] X. Peng. X. Zhu, and Y. Ma. "CL3D: Unsupervised Domain Adaptation Cross-Lidar 3D Detection," Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 2023, 1, 2, 3,5
[21] C. Qi, H. Su, K. Mo, and L. J. Guibas, "Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2017. 3,4
[22] C. Qi. O. Litany, Hc, and L. Guibas, "Deep hough voting for 3d object detection in point clouds," " in proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 9277-9286.
[23] C. R. Qi, W. Liu, C. Wu, H. Su, and L. Guibas, "Frustum pointnets for 3d object detection from rgb-d data," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 918-927.
[24] C. R. Qi. L. Yi. H. Su, and L. J. Guibas, "Pointnet++: Deep hierarchical feature learning point sets in metric space," Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
[25] C. Saltori, S. Lathuiliére, N. Sebe, E. Ricci, and F. Galasso, "Sf-uda 3d: Source-free unsupervised domain adaptation for lidar- 3d object detection." " in 2020 International Conference on 3D Vision (3DV). IEEE, 2020, pp. 771-780. 3.3
[26] G. Shi, R. Li, and C. Ma, "Pillamett: Real-time and high-performance pislar-based 3d object detection," in European Conference on Computer Vision. Springer, 2022, pp. 35-52.
[27] S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, "Pv-renn: Point-voxel feature set abstraction for 3d object detection," in Proceedings of the IEEE/CVF conference computer vision and pattern recognition, 2020, pp. 529-901 538. 1,3
[28] S. Shi, L.. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, "PV-RCNN+: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection," arXiv: (ef::22022004466 2021. 3
[29] S. Shi, X. Wang, and H. Li, "Pointrenn: 3d object proposal generation and detection from point cloud," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 770-779.

---

[30] S. Shi, Z. Wang. J. Shi, X. Wang, and H. Li, "From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network," IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 8. pp. 2647-2664, 2020. 3,6
[31] S. Shin, S. Golodetz, M. Vankadari, K. Zhou, A. Markham, and Trigoni, "Sample, crop, track: Self- supervised mobile 3d object detection for urban driving lidar," in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. 1
[32] S. Shin, K. Zhou, M. Vankadari, A. Markham, and N. Trigoni, "Spherical mask: Coarse-to- to-nee 3d point cloud instance segmentation with spherical representation." " in Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), 2024.
[33] P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang. J. Shlens, Z. Chen, and D. Anguelov, "Scalability in perception for autonomous driving: Waymo open dataset," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020."7
[34] P. Sun, M. Tan, W. Wang, Liu, Xia, Z. Leng. and D. Anguelov, "Swformer: Sparse window transformer for 3d object detection in point clouds," in European Conference on Computer Vision Springer, 2022, pp. 426 666.442. 3
[35] O. D. Team, "Openpedet: An open-source toolbox for 3d object detection from point clouds." https: Peithub.com/opee Satmm//pen-mmmaaa/eePPPPCCCCC 2020.
[36] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, E. Kaiser, and I. Polosukhin, "Attention is all you need," " Advances in neural information processing systems, vol. 30, 2017.
[37] H. Wang, C. Shi, Shi, M. Lei, S. Wang. D. He, B. Schiele, and Wang, "Dsvt: Dynamic sparse voxel transformer with rotated sets." in Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, 2023, pp. 13 13552011 12.99......... 3
[38] Y. Wang. X. Chen, Y. You, L. E. Li, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L, Chao, "Train in germany, test in the usa: Making 3d object detectors generalize," in Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, 2020, pp. 11713-11 723. 1, 2, 7,
[39] Z. Wang. Y.-.. Li, X. Chen, H. Zhao, and Wang, "Uni3detr: Unified 3d detection transformer," Advances in Neural Information Processing Systems, vol. 36, 2024. 3
[40] Y. Wei, Z. Wei, Y. Rao, J. Li, J. Zhou, and J. Lu, "Lidar distillation: Bridging the beam-induced domain gap for 3d object detection," in European Conference Computer Vision. Springer, 2022, pp. 179-195. 3.3
[41] Q. Xu, Y. Zhou, W. Wang, C. R. Qi, and D. Anguelov, "Spg: Unsupervised domain adaptation for 3d object detection via semantic point generation," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 15 18444115555 1.3
[42] Y. Yan, Y. Mao, and B. Li, "Second: Sparsely embedded convolutional detection," Sensors, vol. 18, no. 10, p. 3337, 2018. 3, 7,9
[43] J. Yang, S. Shi, 7. Wang. H. Li, and X. Qi, "St3d++: adaptation on 3d object detection," arXiv preprint arXiv:2 108, 108662, 2021. 5, 8,
Denoised self-training for unsupervised domain
[44] J. Yang, S. Shi, Z. Wang, and Qi, "St3d: Self-training for unsupervised domain adaptation on 3d object detection," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. 1, 2, 3, 5, 6, 7, 8, 14, 15
[45] Z. Yang. Y. Sun, S. Liu, and J. Jia, "3dssd: Point- based 3d single stage object detector," in Proceedings of the IEFF/CVF conference on computer vision and pattern recognition, 2020, pp. 11 040-11 048.
[46] Z. Yihan, C. Wang, Y. Wang, H. Xu, C. Ye, Z. Yang, and C. Ma, "Leariing transferable features for point cloud detection via 3d contrastive co-training," Advances in Neural Information Processing Systems, vol. 34, pp. 21 493-21 504, 2021. 1, 3, 7,8
[47] B. Zhang and P. Wonka, "Point cloud instance segmentation using probabilistic embeddings." in Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8883 8892.
12

---

[48] G. Zhang, C. Junnan, G. Gao, Li, and X. Hu, "Hednet: A hierarchical encoder-decoder network for 3d object detection in point clouds," Advances in Neural Information Processing Systems, vol. 36, 2024. 3,
[49] J. Zhang, J. Huang, Z. Luo, G. Zhang, X. Zhang, and S. Lu, "Da-detr: Domain adaptive detection transformer with information fusion." in Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition. 2023, pp. 7877--23 798.
[50] W. Zhao, Y. Yan, C. Yang. Ye, X. Yang, and K. Huang, "Divide and conquer: 3d point cloud instance segmentation with point-wies wise binarization," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
[51] Y. Zhou and O. Tuzel, "Voxelnet: End-to-end learning point cloud based object detection," in Proceedingy of the IEEE conference on computer vision and pattern recognition. 2018, pp. 4490-4499.
13

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Appendix
B
Supplementary Material
In this supplementary material, we provide the following additional information:
Details of Self-Training Implementation
Additional Qualitative Results
Multi-Class Adaptation Results
B.1 Details of Self-Training Implementation
In the self-training framework [44], batch consists of an identical number of point cloud seans and labels from the source and target domains train the base 3D detector. For training, the ground-truths from source domain and pseudo-labels from the target domain are considered the same. For each cycle train our system for 2 epochs using I. in Eqn. 11 and collect the pseudo-label
of the self-training, sets to merge into Y using the model's inference. Here, the collection of the pseudo-labels takes place in the training of the target domain. Utilizing the new Y, we progressively train the detector for epochs again and iterate the same process. The confidence score of the detection to be considered 2 epochs.
as the pseudo label is 0.5, and aae
B.2 Additional Qualitative Results
Figure shows additional qualitative results on Waymo-KKIT' and qualitative results of previous works, only include the results that reproduce the reported results on their papers based on the published source codes for each dataset at the time of submission
For all the
Figure Qualitative comparison of Baseline ST3D [44], DTS [8] and on Waymo KITTI (b)
(c) DTS
(d)
adaptation scenario (top) and Waymo to NuScenes(bottoom) adaptation scenarios.
14

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B.3 Multi-class Adaptation
Table II shows the result of multi-class adaptation. As our method designed simple add-on that can be applied to general existing pipelines. we apply our pipeline on top of the multi-class adaption approach, REDB [3]. As can be seen, our GroupEXP- and REDB show synergy. improving the performance further.
Car
Pedestrian
Cyclist
Method
APBEv/APaD 3D
APREv/APaD
APREV/APPaD
Source Only
39.15/7.65
21.54/16.87
6.31/2.44
ST3D [44]
71.30/48.09
22.64/17.61
7.86/5.20
ReDB [3]
74.23/51.31
25.95/18.38
13.82/8.64
Ours (SA)
72.39/50.01
24.47/17.80
10.90/6.81
Ours+ReDB
78.12/52.18
26.47/18.51
14.92/9.13
Oracle
83,29/73.45
46.64/41.33
62.92/60.32
Table I: Comparison with multi-class adaptation setting for NuScenes - KITTI adaptation task.
Here, Ours (SA) refers the naive extension of single-class adaptation multi-class adaptation, and Ours + ReDB stands for the proposed method added on top of ReDB. Here, all of ours use three groups.
15

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NeurIPS Paper Checklist
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Answer: [Yes]
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The contribution is accurately reflected in the Abstract section.
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We follow the Code of Ethics.
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17


Reviews:
[{"review_id": "U7Vcvktms5", "reviewer": "Reviewer_CYB3", "summary": "The authors propose a new framework that leverages a grouping-exploration strategy to address the inter-domain gap observed in 3D object detection. The core of their approach is to divide the available labels into multiple clusters, ensuring equal learning attention across these groups using group-equivariant spatial features. This method helps prevent the imbalance caused by dominant object types and reduces false negatives by incorporating an explorative group update strategy during the learning process. The framework is designed to be an add-on that can easily integrate with existing detection systems. The proposed method shows substantial improvements in bridging the inter-domain gaps compared to existing approaches, demonstrated across datasets like NuScenes, Waymo, and KITTI.", "questions": "Please refer to the weakness part.", "limitations": "No", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The proposed grouping module and group-region correlation module are intuitive and technical sound.\n2. The author provides extensive ablation studies for the proposed group-extraction method.", "weaknesses": "My major concern lies in the generality of the proposed method. Since the point cloud of \"car\" object is large and unique, it's easier to group for cars. Can the proposed method generalize to other categories? I hope the author can further validate the proposed method from this perspective.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive comment on our work.  \n\nWe would like to let you know that the concern that you raised is addressed in the author rebuttal, which is visible to all reviewers.\nIn particular, Tables A1 and A2 in the uploaded rebuttal pdf show the experimental results for other categories for NuScenes $\\rightarrow$ KITTI adaptation tasks. Please feel free to use the discussion period if you have any additional questions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fUqLFVjGmj", "reviewer": "Reviewer_mHQJ", "summary": "This paper deals with the domain adaptive 3D detection based on point clouds, where labels are available for source domain and not for target domain. The authors noticed that previous works tried to model and minimize the domain gap in terms of one specific factor, like shape, density, etc., and proposed to cluster the features into different groups. During training, the cross-attention is performed between conventional point feature and the group feature to perform the group-aware detection. The authors combine the proposed method with the conventional self-training pipeline and empirically show that the proposed method can improve the domain adaptive detection performance.", "questions": "See the weakness part.", "limitations": "The authors adequately addressed the limitations.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper is generally easy to follow and understand.\n\n- The idea is generally reasonable. \n\n- Experiment results are good.", "weaknesses": "- In my understanding, the proposed method mainly enhances the point representations with combing the group feature (i.e., fusing the representations of similar points). As the authors discussed in the abstract and introduction, self-training may generate biased pseudo-label collection. I just wonder how the proposed method correct such a bias in self-training. \n\n- Some technical details are not clear enough. For example, is $\\mu$ directly updated by gradient back-propagation? Or is it calculated based on current features and updated using moving average? What is the \"closed gap\" in Table 1?\n\n- It is better to summarize the training process into an Algorithm table to help the reader better understand the details and pipeline of proposed framework. \n\n- What is the effect of $\\lambda_1$, $\\lambda_2$ and $\\lambda_3$ in Eq. (11)? How to choose them in practice? Lack hyper-parameter study.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive assessment of our work. In the following, we address the concerns point by point. Please feel free to use the discussion period if you have any additional questions.\n\n(1) How does the proposed method correct the bias in self-training?\nWe would like to let you know that we address the concern in the author rebuttal. The grouping and explorative group update encourage objects with similar features to be close to each other in the feature space. This leads each group to focus on objects with different features, whereas previous works are prone to be biased by objects with globally dominant features, as they are similar to having one group. As the self-training is designed to collect pseudo-label using highly confident detections, many groups with different focuses are able to find larger variation of objects with different features, alleviating the bias problem. Figure A1 demonstrates the concept on NuScenes. Here, we extract each object feature using ground truth object boxes with RoI pooling from $F_{bev}$ for DTS and $F_{cbev}$ for ours. The left figure shows that for all methods, the recall improves for the object with features similar to those of the group center.\nThe figures in the middle and right show that multiple groups are able to discover more objects with variety.\n\n(2) Hyperparameter selection is addressed in the main author rebuttal.\n\n(3) Training pipeline is added in the rebuttal pdf that we uploaded.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c3oMzJEguF", "reviewer": "Reviewer_5ZUx", "summary": "The performance of 3D object detection in large outdoor point clouds suffers in unseen environments due to inter-domain gaps. Existing domain adaptation methods, focusing on single factors like object size or shape, still leave substantial gaps. This work proposes a grouping-exploration strategy framework to address biased pseudo-label collection and multiple contributing factors. The framework clusters label sets to ensure balanced learning and introduces an explorative group update strategy to reduce false negatives. This method, applicable as a simple add-on to existing detectors, significantly bridges inter-domain gaps in datasets like NuScenes, Waymo, and KITTI.", "questions": "Regarding the weakness, could you provide the results for different adaptation tasks under the multi-class adaptation setting?", "limitations": "The limitation has been discussed, but potential solutions have not been provided.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The presentation is commendable (Figures 1-4 are particularly well-executed), and the writing is clear and easy to follow.\n+ A keen observation is highlighted in Figure 1: the baseline adaptation method mainly detects objects with features near the mean of the distributions, indicated by red circles.\n+ The proposed explorative update is well-motivated, aiming to cover a broader range in the target domain rather than baised to the source.", "weaknesses": "One of the baselines, the performance of REDB, is not fairly reported. REDB addresses more realistic scenarios where the 3D detector is trained and adapted for all object categories simultaneously. However, in Table 1, the adaptation focuses only on a single category: car. Therefore, reporting the performance of REDB in Table 1 is inappropriate. Additional experiments under a multi-class setting are necessary to compare the proposed GroupExp-DA with REDB, validating the effectiveness of GroupExp-DA in more realistic scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive assessment of our work. \n\nWe address the concern you raise in the main author rebuttal. In particular, Table A2 shows the experimental results for the multi-class adaptation setting.\n\nPlease feel free to use the discussion period if you have any additional questions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kuOcb0ViAI", "reviewer": "Reviewer_mXQC", "summary": "In this paper, the authors utilize a Gaussian Mixture Model (GMM) based grouping & exploration module on the object descriptors extracted from foreground points, then fed the group features to the Group-Correlation module and fuse with RPN to selectively detects objects similar to the individual group, which includes group-equivariant spatial feature to ensure equal learning attention for different object groups and explorative group update strategy to reduce false negative detections by redistributing available labels. The paper conducts domain adaptive 3D detection experiments on KITTI, NuScenes, and Waymo datasets, and ablation studies on the impact of each component, the impact of $n_g$, and the effect of GMM based Grouping and $\\alpha$.", "questions": "Have you tried different 3D detectors like CenterPoint and other recent SOTA 3D detectors? Is the performance gain of the proposed method still significant when the baseline is high? If it is, and even achieves SOTA, it will be helpful to prove the significance and the contribution of the proposed method.", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Originality: Apply GMM based grouping in domain adaptation for 3D object detection, which differs from previous methods.\nQuality: Compare to many previous methods, and achieve good performance. Good ablation studies.\nClarity: Images are clear and easy to understand.\nSignificance: Provide idea that use GMM for Group-Equivariant Features Learning in Domain Adaptive 3D Detection.", "weaknesses": "Lack of implementation details and the code is not provided, for example the detailed parameters and hyper-parameters of the Models, like the voxel size. It's also better to provide the model size or runtime of the model, to make sure the performance gain is not from the increment of the model size, and to make sure the model have an apple-to-apple comparison with the previous methods.\n\nLack of variety of the object categories, such as pedestrians, cyclists, and so on. The experiments in the paper mainly focuses on cars, it's better to include other types of objects such as pedestrians, cyclists, and so on. In addition, lack the variety of the visualization in the qualitative results.\n\nMinor:\nIn References, the authors of [39] is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive assessment of our work. In the following, we address the concerns point by point. Please feel free to use the discussion period if you have any additional questions.\n\n1) Lack of variety?\nWe address the multi-class adaptation in the main author rebuttal. In addition, as you suggested, we uploaded additional visualizations and performance with other base detectors in Figure A2 and Table A3.\n\n2) What are the voxel size, model size, and running speed? \nThe voxel size is set following the comparing methods ST3D and DTS, which depend on the adaptation task. Please refer to Table A4 in the rebuttal pdf for the model size and running speed.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear Reviewers,

We greatly appreciate your valuable and constructive reviews for our paper. Most of the reviews are very helpful. We thoroughly noted your feedback and will make sure to include all the discussions that we have during the rebuttal in the final version of the paper. We summarized the common questions in the initial reviews as follows. The other questions are answered in detail for individual reviewer's rebuttals. 

(1) How well does the proposed system generalize to other categories? 
In order to investigate the proposed system's generalization ability, we have conducted additional experiments on other categories, such as pedestrian and cyclist, as suggested. For the comprehensive experiment, we have conducted two experiments: (1) Single-class adaptation (2) Multi-class adaptation. Table A1 shows the performances of the single-class adaptation, which is the primary task of GroupExp-DA. Here, we keep all the parameters the same as the experiment for 'car' and only replace the target category. For both pedestrian and cyclist categories, despite the difference in performance depending on the category, the proposed GroupExp-DA consistently outperforms the current SOTA. We provide a more detailed analysis of the result in the individual reviewer's rebuttal.

Table A2 illustrates the performance of the multi-class adaptation setting. Here, Ours (SA) refers to a naive extension to multi-class, where the same number of groups (=3) is assigned for each category straight-forwardly for learning. On the other hand, Ours + ReDB adopts ReDB on top of ours, which is specialized for multi-class adaptation settings. The experimental results show that GroupExp-DA can be a simple add-on that can be successfully combined with multi-class adaptation methods to improve performance. We describe the result in more detail in the reviewer's rebuttal.

(2) How does the proposed system overcome bias from dominant objects during self-training?

GroupExp-DA overcomes the bias from self-training with grouping and explorative group updates. We first observe that the dominant features of objects in one domain cause inter-domain gaps and lead the detector to be biased because they outnumber other objects, as can be seen in Figure 1 of the main paper. GroupExp-DA attempts to address this imbalance problem by distributing objects into groups. Our key insight is that while grouping, each group finds locally dominant object features that are comparably ignored due to outnumbering globally dominant features in one domain. The learning attention is then equally divided to each group using group-wise object detection, alleviating the imbalance problem. The grouping improves the inter-domain gap because group-wise detection allows for focusing on objects with minor features in one domain, which can contain dominant features in other domains, improving the false negatives for collecting pseudo labels during self-training. Figure A1 in the rebuttal pdf demonstrates the concept in addition to Figures 3 and 6 in the main paper.





(3) How were the hyper-parameters, such as $\lambda$ selected? How sensitive are they? 
The hyper-parameters were selected based on the simple principle that the loss for detection and grouping contrastive learning ($\lambda_{1}$, $\lambda_{2}$) should have equal weights. This led us to select the hyper-parameters so that summing up $\lambda_{1}$ and $\lambda_{2}$ equals $\lambda_{3}$. Table A5 show the impact of hyper-parameter change in NuScenes to KITTI adaptation task. In most of the configurations, except for the case where the coefficients are unreasonably low, such as ($\lambda_{1}$=0.1,$\lambda_{1}$=0.1),  GroupExp-DA outperforms the current SOTA, DTS.

Meta-review: Hi,
This draft has been awarded two weak accept and 2 borderline accept. On the basis of positive reviews, this draft is being accepted for publication. 


regards

AC
Decision: Accept (poster)