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_cc_2024_Conference
Published: 2024-05-14 13:19:04

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.

Main paper: 
 introduction: 
 Learning 3D object detection [42, 12, 27] and segmentation [50, 11, 32, 10] in large outdoor point cloud scenes is of increasing importance due to 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 on 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-label set. 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.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 inter-domain gap. Typically, in one domain, a group of objects sharing common features outnumber other objects having different features, as addressed in existing works [38, 8, 17, 20] . While this may not cause a significant performance deterioration in the source domain, where the environment is similar, it could cause a 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 Figure 1 shows, a single factor cannot explain all the domain variation as an object's appearance is a 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 a 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, we introduce a data-driven grouping method that finds object groups with different characteristics. The groups are then progressively updated for adaptation, redistributing the available labels according to 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 a 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.3. To make the best use of the Group-Equivariant feature's selective detection ability depending on the input 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 effectiveness of our approach for bridging inter-domain gaps. 
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 [23, 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 a few methods that combine point-and voxel-based methods [27, 28, 39] . Currently, in terms of performance, voxel-based detectors dominate the point-cloud-based 3D detection for outdoor scenes. Therefore, we employ Second-IoU [42] and PointPillars [12] as the base detectors for our experiments, as they are most-widely used detectors that existing works are built on.Domain Adaptive Detection. The target of domain adaptive 3D detection is to mitigate the interdomain 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 a 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-labels by addressing the inter-domain gap caused by geometric shape with prototype learning [20, 17] , dense-to-sparse density variation using knowledge distillation [40] , a 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 on a single factor, such as density, shape, and size, to bridge the inter-domain gap. Instead, we attempt to see the inter-domain gap as a result of multiple factors combined and bridge the gap by finding inherent groups.3 Method 
framework overview: 
 Following the self-training-based unsupervised domain adaptation scheme [44, 43] for 3D detection, we are given point clouds X = X s ∪ X t and labels Y = Y s ∪ Y t as the initial set. Here, X s and Y s are point could and box labels of the known source domain. On the other hand, X t and Y t are the point cloud and initial pseudo label set in the unlabeled target domain. Y t is collected by the detector trained only on the source domain using X s and Y s . A label in Y consists of seven parameters defining a 3D box with three parameters for center (x, y, z), three parameters for size (l, w, h), and one parameter for vertical rotation θ. Our goal is to improve the detector's inter-domain adaptation by focusing on the pseudo-label collection. In particular, instead of treating all available objects in Y equivalently, we 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 as input and fuses with RPN to selectively detect objects similar to the individual group.Given a pair of point cloud x ∈ R np×3 and a label set y ∈ R nb×7 consisting of n p points and n b boxes from X and Y , respectively, this module aims to produce a learnable object descriptor F obj ∈ R nb×d obj during training. First, foreground points, P obj = {p k obj } nb k=1 are extracted using y from the input point cloud x. Our object descriptor extraction module then takes P obj as input and outputs object descriptors F obj using neural networks consisting of MLP and global max pooling, which are adopted from [21, 13] . The motivation behind the architectural choice is two-fold: (1) An arbitrary number of object points, p k obj , can be processed efficiently without involving comparably slow sampling techniques. ( 2) Global max pooling offers permutation invariant features, which helps F obj less prone to overfitting by certain permutations from viewing angles, etc. During training, F obj serves as the input for the progressive grouping process and is not used during inference.Determining similarities between objects is a 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 as covariance and weights, compared to the proximity-based methods. (2) The parameters that define groups can be efficiently updated with weighted linear combinations and are also differentiable for learning the groups in a data-driven manner. In the following sections, we 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 F obj from each pair and stack them. All of the stacked {F o obj } n total o=1 are then used for the initialization. Here, n total stands for the total number of pairs in X and Y. Specifically, we initialize n g groups using K-Means clustering on {F o obj } n total o=1 , where each cluster forms a group. After this, Maximum Likelihood Estimation is performed for each group to acquire parameters, such as mean µ ∈ R ng×d obj , covariance σ ∈ R ng×d obj ×d obj , and weight ϕ ∈ R ng that define a GMM-based group. This initialization is required only once before the training. In the following, all procedures are based on a single pair of scan and label, x and y, which can be extended to batch-wise operation.Determining Group for New Sample The probability P k→i of k-th sample belonging to i-th group is estimated as :EQUATIONwhere N (|, ) is the probability density function of multivariate Gaussian distribution that outputs the likelihood of a sample F k obj given mean µ and covariance ϕ. The group labels G ∈ R n b for all the n b samples in F obj are then acquired using P . That is, the group label G k for k-th sample is specifically determined as:EQUATIONExplorative Update during Training Typically, pseudo-label sets from the target domain are incorporated into the existing label set from the source domain and considered as the same label set Figure 3 : Conceptual diagram comparing pseudo-label generation processes of (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-label 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 μi ∈ R d obj , covariance σi ∈ R d obj ×d obj , and the group weight φi are acquired as follows:EQUATIONwhere F obj,i is subset of F obj that belong to i-th group using G in Eqn. 2 as:EQUATIONand n b,i is the number of samples in F obj,i . Similar to the update rule for prototype learning [20] , each group parameters are updated with linear combination as:EQUATIONwhere α is a coefficient that affects how conservatively each parameter is updated. After the update, µ i is considered as a representative of i-th group and utilized as a query input for generating groupequivariant 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 the groups found in the target domain so that the source labels are used for learning to find similar objects in the target domain, as shown in Figure 3 (b) and (c).For training, to ensure that the groups learn to be distinctive enough, we adopt inter-group repel [17] based on the contrastive loss.EQUATIONwhere cos(, ) is a function that calculates cosine-similarity between two inputs. In addition, to encourage similar features for group cohesion, intra-group attraction loss, L att is used:EQUATIONwhere 1[G k = i] is an indicator function that is 1 if G k = i and 0 otherwise. During the inference, only the learned µ is necessary for the following Group-Region Correlation.In a typical voxel-based 3D object detector's pipeline, voxelized points are fed into the backbone, which outputs the spatial feature F bev ∈ R H×W ×d bev , as shown in Figure 2 . F bev is then fed into RPN to make final box predictions. Given input group queries µ = {µ i } ng i=1 , µ i ∈ R d obj and F bev , Our Group-Region Correlation aims at producing the spatial features F cbev ∈ R ng×H×W ×d bev that Algorithm 1 Training Pipeline Require: a point-cloud x, (pseudo) labels y, Group parameters G 1: Extract obj. descriptors F obj using x and y (Sec 3.2) 2: Determine groups of F obj and update G (Sec 3.3) 3: Update G using F obj according to the determined groups (Sec 3.3) 4: Calculate L rep and L att as in Sec 3.3 5: Extract spatial feature F bev from the backbone (Sec 3.4) 6: Make group equivariant features for each group using G and F bev (Sec 3.4) 7: Predict boxes using shared RPN for each group equivariant feature 8: Calculate L det as in Eq. 10. 9: Calculate gradients using L rep , L att , and L det . 10: Update all modules' weights using the gradients are equivariant to the group query so that F cbev 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 to detect the corresponding objects.Group Equivariant Spatial Feature The aim of Group-Region Correlation module is to make spatial features F bev 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 F bev as key and value to encourage features from µ and F bev to cross-attend to generate necessary features. Intuitively, µ is compared to F bev to find the object that is similar to each group. For i-th group, the attended group-equivariant spatial features F i cbev ∈ R H×W ×d bev are acquired as:EQUATIONwhere cAttn(.) refers to the cross attention [36] that takes query, key, and value as input and outputs the cross-attended feature. Here, µ i is the i-th query in µ. The group-equivariant spatial features F cbev can then be fed into any existing RPN strcutures [42, 4, 12] to detect foreground objects for each group. The ground-truth boxes in y that belong to the i-th group are utilized as the foreground boxes for F i cbev to train the follwing RPN. Regional Proposal Network (RPN) Following the general architecture of RPN [42, 12] , our objectness and box regression heads take F cbev ∈ R ng×H×W ×D as input and predict objectness scores F cls ∈ R ng×H×W ×1 and box parameters F box ∈ R ng×H×W ×7 to form 3D boxes on the dense spatial grid corresponding to F cbev .For the training of i-th group, standard training losses for RPN based detection, L i det , are applied as existing works [42, 12, 30, 26, 2, 9, 48] . Specifically, given the box labels y i , F i cls and F i box are used for calculating first-stage box detection training loss, L det1 , as:EQUATIONwhere L f ocal stands for Focal Loss [18] and L box is box regression loss. Here, the foreground labels and regression targets for L f ocal and L box are calculated using y i depending on the individual base detectors' configurations. Similarly, for the second-stage box refinement training, F cbev is used with F cls and F box for RoI 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 L i det2 . The detection loss for all groups, L det , is then acquired by iterating over all groups as:EQUATIONApart from L rep and L att for grouping, our overall training losses are defined the same as the general RPN learning for detection.EQUATION) Using L we train our system following the self-training scheme [44, 43] . Further details can be found in Sec:B.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 is 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-beam LiDAR and four 200-beam LiDAR.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 a 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 -4 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 to the group learning. While the group equivariant RPNs are used for the pseudo label collection during self-training, only the baseline RPN is used for the final testing, ensuring the same execution speed as the existing pipeline. The feature dimensions d bev and d obj for F bev and µ are both set to 512 for cAttn. α for updating the group parameters is empirically set to 0.8. λ 1 , λ 2 , and λ 3 are set to 0.5, 0.5, and 1.0, respectively.We compare recent existing 3D domain adaptive detection methods, such as SN [38] , 3D-CoCo [46] , ST3D [44, 43] , GPA-3D [17] and DTS [8] with our proposed method. As our method is 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 of the oracle models, which refer to a fully-supervised model on the target domain directly as an upper bound. Following the most recent works [17, 8] , all methods are compared in three adaptation scenarios focusing on "car" class: (1) Waymo → NuScenes (2) NuScenes → KITTI (3) Waymo → KITTI.Following [44, 43, 17, 8] , we adopt Average Precision(AP) as our primary evaluation metric and evaluate our model on Bird-Eyes-View (BEV) IoU, AP BEV , and 3D Box IoU, AP 3D , with 40 varying recall points and 0.7 as the IoU threshold.Waymo → KITTI Table 1 (first task) shows the quantitative results of 3D detection in AP BEV and AP 3D . When using Second-IoU as detector [42] , our proposed method outperforms the baseline ST3D series for 4.75/11.88 in AP BEV /AP 3D , 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 1 (second task), our approach shows 0.97/5.80 performance gains in terms of AP BEV /AP 3D with Second-IoU [42] as the base detector. Compared with SOTA DTS [8] , 0.07/1.60 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 1 (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 AP BEV /AP 3D , respectively, with Second-IoU as the detector. Similarly, with PointPillars as the detector, 14.89/7.84 and 32.8/1.94 improvements are gained compared with the baseline and SOTA in terms of AP BEV /AP 3D . Here, the foreground features are extracted using ground-truth boxes using F bev for DTS and F cbev for ours.Impact of Each Component Table 2 shows the impact of each component in Waymo→NuScenes adaptation using Second IoU [42] in terms of AP BEV /AP 3D . For this experiment, we progressively add each core component to the baseline (a) to 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, it significantly improves the performance 9.91%/12.23% from the baseline. From this result, L att (c) and L repel (d) improve 3.39%/2.11% and 4.20%/4.19% respectively, suggesting that making the groups distinctive has more impact than making the intragroup cohesive. This is expected because, during the group-equivariant 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 (e), L att and L repel improves 15.92%/21.59% from the baseline, demonstrating the synergy of the two losses. On top of this, the explorative update considerably boosts the performance by 23.80%/28.33%, proving that using samples found in target (pseudo-labels) for the update further reduces the inter-domain gap, as can also be seen in Figure 6 (a group of FN in the middle disappears in the right figure).Impact of n g is illustrated in Figure 5 in AP 3D for all adaptation scenarios presented in the main experiments. For this experiment, we include all components with the same hyper-parameters and only change n g to see the impact. As can be seen, increasing n g improves the performance, reaching the top around when n g is set to 4. In principle, having many groups would make the group-equivariant RPN discover more object types while ensuring the same attention for each group during learning. However, in practice, the performances start decreasing when n g 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 F cbev . We also find that the training result is most stable when n g is set to 3.Effect of GMM based Grouping and α are illustrated in Table 3 to compare: (1) Proximity-based grouping and (2) GMM-based grouping with varying α. For the proximity-based grouping, we discard σ and ϕ from GMM, and determine the group of each sample as 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 in both AP BEV /AP 3D in all α. This is due to σ and ϕ that preserve the characteristics of each group more compared to only µ based on previously seen samples, improving the stability during learning with similar objects grouped together. Additionally, setting α too large or small degrades the performances, as small α discourages the explorative update, while too large α could result in instability as the input µ for training RPN constantly changes more. Also, setting α = 0.6 results in 0.49/0.66 higher in AP BEV /AP 3D compared to setting α = 0.99, suggesting the advantage from exploration surpasses the stability. Interestingly, unlike GMM-based grouping, setting higher α constantly shows increasingly better performance with the proximity-based grouping, proving inherent instability without σ and ϕ. Nevertheless, in nearly all the configurations of α, the GMM-based grouping persistently outperforms the best-performing method, DTS [8] . 
conclusion and limitations: 
 In this paper, we present GroupExp-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), as 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 of the challenges to be explored. 
 
 Appendix: Table II shows the result of multi-class adaptation. As our method is designed as a 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-DA and REDB show synergy, improving the performance further.  


Decision: 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

Reviews: review: paper_summary: paper_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.
strength_weakness: strength_weakness: 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.
questions: questions: Please refer to the weakness part.
limitations: limitations: No
paper_summary: paper_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.
strength_weakness: strength_weakness: - 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. 

- 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?

- 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. 

- 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.
questions: questions: See the weakness part.
limitations: limitations: The authors adequately addressed the limitations.
paper_summary: paper_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.
strength_weakness: strength_weakness: 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.
questions: questions: Regarding the weakness, could you provide the results for different adaptation tasks under the multi-class adaptation setting?
limitations: limitations: The limitation has been discussed, but potential solutions have not been provided.
paper_summary: paper_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 $\alpha$.
strength_weakness: strength_weakness: 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.

Lack 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.

Minor:
In References, the authors of [39] is missing.
questions: 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: limitations: Yes.

Comments: title: Re: rebuttal
comment: Dear Reviewer mHQJ,

We are pleased to learn that our responses to your concerns have positively influenced your evaluation of our work. As you suggest, we will make sure to include the content in the rebuttal to the final version of the paper.
Please let us know if you have any further questions.
Dear Reviewer mXQC,

We are delighted to hear that our responses to your concerns have contributed to a positive assessment of our work. As you suggest, we will make sure to include the content in the rebuttal to the final version of the paper.
Please let us know if you have any further questions.
Thank you for the detailed rebuttal and the additional experiments provided in Table A3 and A4. These results address some of my concerns. I appreciate your commitment to including these findings in the final paper. Based on the improvements and the clarifications provided, I will adjust my score to borderline accept. Please ensure that these experimental results are incorporated into the final version of the paper.
Thanks for the authors detailed response. I will raise my rating. Hope the authors could modify their paper accordingly to make it clearer.
Dear Reviewer mHQJ,

We sincerely appreciate your efforts in providing constructive and professional reviews. During the rebuttal period, we have tried our best to provide feedback and rebuttal to address your concerns. All of them will be involved in our final revised version.

If you have any further concerns, we are more than happy to discuss more before the author-reviewer interactive discussion period ends.
Thank you again for your insightful and meaningful comments.

Best regards,
Authors
Dear Reviewer mXQC,

We sincerely appreciate your efforts in providing constructive and professional reviews. During the rebuttal period, we have tried our best to provide feedback and rebuttal to address your concerns. All of them will be involved in our final revised version.

If you have any further concerns, we are more than happy to discuss more before the author-reviewer interactive discussion period ends.
Thank you again for your insightful and meaningful comments.

Best regards,
Authors of Paper 9774
Dear Reviewer CYB3,

We are pleased to hear that our responses to your questions have been well received and contributed to a positive assessment of our work. We will make sure to include the rebuttal in the final version of the paper. Please let us know if you have any further questions.
Thanks for the strong rebuttal. My concerns have been addressed, and I will raise my rating to weak accept. Please include the content in rebuttal to the next revision.
Dear Reviewer 5ZUx,

We are pleased to hear that our responses to your questions have been well received and contributed to a positive assessment of our work. Please let us know if you have any further questions.
Thanks for the response. Most of my concerns are addressed. I will raise to positive rating.
We would like to add more discussion on a few more details, as official comments are available for discussion during the whole author-reviewer discussion phase. Here are a few more details that we wish to further clarify based on the reviewer's questions and concerns.

**1) Generalization ability to other categories.**\
As mentioned in the main rebuttal, we have additionally conducted comprehensive experiments for other categories, such as pedestrians and cyclists, following the reviewer's comments, which are available in the uploaded PDF. Initially, we focused on only 'Car' category, as it is still challenging for domain adaptative detection that most existing works on single-class adaptation focus on.
As can be seen in both Tables A1 and A2, the proposed GroupExp-DA shows consistent improvements from the baselines, demonstrating its ability to generalize to other categories. 
Although the APs are still the lowest among all categories, we observe an interesting performance improvement from the current SOTAs for 'Cyclist' category, which are 4.58/%/7.06% for the single class adaptation and 8\% and 5.67\% for the multi-class adaptation settings. 
We think this might be because each group in GroupExp-DA focuses on objects with different features, which are beneficial for generalization when the number of available labels in the source domain is significantly smaller. 
For example, in NuScenes, the number of labels for 'cylist' class, which are the sum of 'bicycle' and 'motorist,' is 11,538, while 'car' and 'pedestrian' have 27,449 and 22,447 labels, respectively.
The imbalance is more significant in KITTI where the number of labels available for 'Cyclist' is 734 while 'Car' and 'Pedestrian' are 14,357 and 2,207, respectively. 

We appreciate the reviewer for the constructive and valuable comments on our paper that provided us with new insights. We will make sure to update the main paper based on the additional results acquired from your comments. Please let us know if you have any further comments or questions.
We would like to add more discussion on a few more details, as official comments are available for discussion during the whole author-reviewer discussion phase. Here are a few more details that we wish to further clarify based on the reviewer's questions and concerns.

**1) Multi-class adaptation experiment and ReDB.**\
As mentioned in the main rebuttal for all reviewers, Table A2 shows performances of ST3D, ReDA, GroupExp-DA without ReDA, and GroupExp-DA with ReDA for NuScenes to KITTI adaptation. For a fair comparison, we brought the result reported from the ReDB paper. For ours with ReDA implementation,
we simply add cross-domain examination, diversity sampling, and class-balanced self-training available from the GitHub repository of ReDB, which are proposed for multi-class adaptation settings. Other configurations also follow ReDB's configuration using Second-IoU as the base detector. 
Interestingly, for ReDB + GroupExp-DA, we observe that the performance improvement from the ReDB model is more significant for 'cyclist', which is 8\% and 5.67\% for $AP_{BEV}$ and $AP_{3D}$, compared to 1.1\% and 1.7\% for 'car', suggesting a potential synergy of the two methods for more challenging class, cyclist.
The straightforward explanation would be that the focuses of ReDB and GroupExp-DA are different in making improvements, which leads to additional performance boosts when they are both used together for improving different aspects. We are further investigating this to get more intuition. 

**2) New Table for multi-class adaptation setting.**\
We agree with the reviewer's point of view that ReDB needs to be compared differently from other baselines that focus on the single-class adaptation setting. We will separate ReDB from the current Table 1. Specifically, we will create another table focusing on the multi-class adaptation setting with proper performances of other baseline methods from the ReDB paper as a more challenging yet closer to real-world scenarios. Table 1 will remain for performance comparison with the single-class adaptation-based methods without ReDB. 



We appreciate the reviewer for the constructive and valuable comments on our paper. We will make sure to update the main paper based on the additional results acquired from your comments. Please let us know if you have any further comments or questions.
We would like to add more discussion on a few more details, as official comments are available for discussion during the discussion phase. Here are a few more details that we wish to further clarify based on the reviewer's questions and concerns.

**1) Clarification on overcoming the bias in self-training and Figure A1.**\
As mentioned in the rebuttal, our key insight is that each group distributes the focus of RPN to objects with different features, side-stepping the bias caused by globally dominant features of objects, as can be seen in Figure 1 of the main paper.
In order to further demonstrate this bias problem, we attempt to show that objects in the target domain with similar features as a dominant feature in the source domain are likely to be detected more and used as pseudo labels, causing bias in self-training as follows. 
To find the dominant feature and compare the similarity between the dominant feature and each object, we first acquire the object feature using the spatial feature, which is $F_{bev}$ for DTS and $F_{cbev}$ for ours, with RoI Pooling using ground-truth boxes in the source domain. The dominant feature is then acquired by averaging all the 
object features, assuming that outnumbering dominant features will naturally be reflected in the mean. Figure A1 (left) shows recall with respect to the distance between the dominant feature from the source domain and each object feature in the target domain (NuScenes) after self-training. As can be seen, it is clear that all methods
exhibit higher recall for the objects that are closer to the dominant features, the centers acquired by averaging, and the recall decreases for objects that have less similar features to the dominant features. Here, similar to the source domain, we acquired the object feature 
in the target domain by utilizing the ground-truth boxes and spatial features for the analysis.
Figure A1 (middle) shows the number of objects depending on the similarity between the center and object feature in the target domain. Based on the observation from Figure A1 (left), we can conclude that having more number of objects close to the center in Figure A1 (middle), such as R1 and R2 in x-axes, means the detector is less biased.
As can be seen, our grouping strategy leads to more samples in R1 and R2, suggesting that the proposed GroupExp-DA trains the base detector with fewer biases compared to the current SOTA, DTS, for domain adaptive detection. Also, the base detector trained with both the grouping and explorative update consistently shows better performance (green bar) than the base detector trained only with the grouping with an update using all available labels (orange bar), highlighting the contribution of the explorative group update for self-training. Figure A1 (right) shows how each group contributed to the formation of the green bar (Ours+Group.+Exp.Up.) in Figure A1 (middle).
  
**2) Is $\mu$ directly updated by gradient back-propagation?**\
$\mu$ is indirectly updated by gradient back-propagation. In a batch, it is the mean of object descriptors (MLP + Global max pooling in Sec. 3.2) that belong to the same group. This batch-wise mean $\hat{\mu}$ is linearly combined (moving average) to existing $\mu$ to preserve the gradient flow from the object descriptor extraction module in Sec 3.2 for learning.
 Therefore, since the group equivariant spatial feature uses $\mu$ for Regional Proposal Network (RPN), 
the object descriptor extraction module is updated using the gradient calculated from $L_{det}$ with additional group contrastive loss $L_{att}$ and $L_{repel}$  to produce better object descriptors, which are used to update \mu. This process is now more clearly summarized in the Algorithm table in the rebuttal pdf, as you kindly suggested. In addition, the contribution of each loss can also be found in Table 2 of the main paper.

**3) What is the closed gap in Table 1?**\
Closed gap is the metric proposed in ST3D to show how much performance gap is closed between Source Only model, which is trained only on the source domain and tested in the target domain, and Oracle model, which is trained on the target domain with the ground truth. 
It is calculated as follows: Closed Gap = (AP_{model} - AP_{source only}) / (AP_{oracle} - AP_{source only}). Here, AP_{model} is the Average Precision (AP) calculated using the tested model's prediction. Similarly, AP_{source only} and AP_{oracle} are AP predicted using the source-only and oracle models. We will clarify this in the main paper. We appreciate your comment.

**4) Algorithm.**\
The training pipeline is now summarized into an Algorithm table in the uploaded rebuttal pdf, as you kindly suggested. For the inference, only lines 5,6 and 7 in the table are used in the pipeline.

We are grateful for the reviewer's constructive comments on our paper. We will update the main paper based on the results from your comments. Please let us know if you have any further comments or questions.
We would like to add more discussion on a few more details, as official comments are available for discussion during the whole author-reviewer discussion phase. Here are a few more details that we wish to clarify based on the reviewer's questions and concerns.


**3) Have you tried different 3D detectors like CenterPoint and recent SOTA detectors? Did the proposed model go through apple to apple check?**\
In the main paper, we have reported the result with mainly Second-IoU [37] and PointPillars [9] as the base detectors for the fair comparisons with the existing works, where all the configurations, such as voxel size, learning rate, number of filters, thresholds, etc, for the adaptation stay the same, as mentioned in Sec. 4.2 of the main paper. 
As one of the other famous base detectors, we have included additional results with CenterPoint, as the reviewer suggested, and DSVT[32] in Table A3 for the NuScenes to KITTI adaptation task. We selected DSVT as one of the most recent base detectors due to the fact that it is designed for LiDAR only, which is compatible with our purpose and is one of the top-performing methods.
Specifically, Table A3 shows the apple-to-apple comparison of DTS and our proposed method given the same base detectors, CenterPoint and DSVT backbone, followed by CenterPoint Head.
Interestingly, using CenterPoint Head leads to worse results compared to anchor-based methods, such as Second-IoU, when we directly use the model only trained on the source domain to target without self-training. 
We think this is because the anchor-based methods inherently encourage the spatial voxel features (F_{bev}) to learn the bigger surrounding context with supervision to predict IoU between boxes, rather than center position-based supervision from CenterPoint.
Nevertheless, our proposed method constantly outperforms the SOTA method for domain adaptive detection, DTS, under the same setting as the base detectors. Also, changing the base detectors to the more recent method, such as DSVT, leads to more improvement for adaptation with around 1.89 gain in AP_{3D}, proving the applicability of our method.




**4) Performance Gain by model size?**\
Table A4 shows the runtime analysis for inference speed and model size on an NVIDIA A10 GPU. Here, on KITTI validation set, we measured the inference speed of three models, namely ST3D, DTS, and ours. As can be seen, the current SOTA DTS requires around 43% more memory while underperforming than GroupExp-DA, suggesting that the improvement made by our proposed method is not simply based on the increment of model size.
The inference speed of our method is among the slowest. However, we would like to note that the speed has comparably more room for improvement than the precision without sacrificing model size by using optimized software such as onnxruntime, tensorRT, etc.   



**5) More discussions on multi-class adaptation settings.** \
We have conducted comprehensive experiments designed for other categories of objects, such as pedestrians and cyclists. This can be found in the main author rebuttal for all reviewers. As you kindly suggested, we added more visualizations focusing on cyclists and pedestrians in KITTI validation set, which can be found in Figure A2.



**6) Code is not provided.**\
We will release the code and checkpoints as soon as the paper gets accepted.

**7) Missing authors of [39].**\
Thank you for letting us know about the reference issue. We will fix it in the final version.
 

Again, we thank the reviewer for constructive and valuable comments on our paper. We will make sure to update the main paper based on the additional results acquired based on your comments. Please let us know if you have any further comments or questions.