Voxel Proposal Network via Multi-Frame Knowledge Distillation for Semantic Scene Completion

Authors: lubo  wang
di  lin
kairui  yang
ruonan  liu
qing  guo
wuyuan  xie
miaohui  wang
lingyu  liang
yi  wang
ping  li
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-11 08:16:35

Abstract: Semantic scene completion is a difficult task that involves completing the geometry and semantics of a scene from point clouds in a large-scale environment. Many current methods use 3D/2D convolutions or attention mechanisms, but these have limitations in directly constructing geometry and accurately propagating features from related voxels, the completion likely fails while propagating features in a single pass without considering multiple potential pathways. And they are generally only suitable for static scenes and struggle to handle dynamic aspects. This paper introduces Voxel Proposal Network (VPNet) that completes scenes from 3D and Bird's-Eye-View (BEV) perspectives. It includes Confident Voxel Proposal based on voxel-wise coordinates to propose confident voxels with high reliability for completion. This method reconstructs the scene geometry and implicitly models the uncertainty of voxel-wise semantic labels by presenting multiple possibilities for voxels. VPNet employs Multi-Frame Knowledge Distillation based on the point clouds of multiple adjacent frames to accurately predict the voxel-wise labels by condensing various possibilities of voxel relationships. VPNet has shown superior performance and achieved state-of-the-art results on the SemanticKITTI and SemanticPOSS datasets.

Main paper: 
 introduction: 
 Understanding 3D scenes based on LiDAR point clouds is essential for tasks like autonomous driving. However, due to the limitations of LiDAR sensors and the occlusion of instances by themselves or other instances in the real world, including large-scale information in the point clouds poses a significant challenge to understanding 3D scenes.Semantic Scene Completion (SSC) aims to simultaneously infer a scene's occupancy and semantic information based on point clouds using deep learning. Several methods [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18] use convolutions to complete the partial scene. Some completion methods [19; 20; 21; 22] heavily rely on diverse attention mechanisms as the attention mechanism can capture spatial relationships and update the features. The diffusion model [23] also applies to the completion task. These methods have significantly improved the performance of static single-framebased semantic scene completion. However, they still suffer from extreme geometric incompletion due to the large-scale information loss of point clouds. Moreover, these methods ignore the regional distraction and voxel semantic uncertainty that arises from the information loss and the complex relative motion of instances in dynamic point cloud sequences.Our paper introduces a new method for completing from both Bird's Eye View (BEV) and 3D perspectives. We propose confident voxels that show possibilities for voxels and implicitly capture the uncertainty of voxel-wise labels. Our method, Voxel Proposal Network (VPNet), includes the Confident Voxel Proposal (CVP) and Multi-Frame Knowledge Distillation (MFKD). We present the overview architecture of VPNet in Figure 1 . The BEV branch completes from the BEV perspective using 2D convolutions to ensure global reasonableness and comprehensiveness of completion. The 3D branch consists of segmentation and completion subnetworks, which perform completion under the guidance of rich semantic contexts and optimize local details and accuracy of completion.In the 3D branch, CVP learns multiple arrays of offsets for occupied voxel coordinates and features to compute confident voxel coordinates and perform long-range feature propagation like [24] within its branches. Then, CVP uses the confident voxel coordinates to propose confident voxels and constructs confident feature maps, which suggest various possibilities of voxel semantic labels. Finally, we integrate the confident feature maps as augmented feature maps for completion using multi-branch fusion, which condenses the proposed possibilities from the inner-frame branches.VPNet has a multi-frame network that generates enhanced feature maps for multiple frames using CVPs. It condenses these feature maps into the branches of the CVP in the single-frame network, enabling each branch to create a similar semantic to the corresponding point cloud frame. VPNet condenses the combined enhanced feature map of multiple frames into the single-frame network, further improving the semantics. This process condenses the various possibilities in multi-frame to single-frame networks and affords the opportunity to learn to infer the lost details of each frame in contrast to other KD methods [25; 26; 27; 16] .We evaluate the effectiveness of VPNet on the SemanticKITTI [28] and SemanticPOSS [29] datasets, where we achieve state-of-the-art performances on the semantic scene completion task. 
semantic scene completion: 
 The current approaches for SSC rely on convolution, attention, or diffusion models. For example, SSCNet [1] utilizes dilated convolution to enhance the feature map, while LMSCNet [2] applies 2D U-Net and 3D segmentation heads for multi-resolution completion. ESSCNet [3] employs spatial group convolution and sparse convolution to group voxels, and UDNet [4] incorporates UD block in 3D U-Net to efficiently fuse encoder and decoder features. Furthermore, SSA-SC [5] uses a semantic segmentation network to assist completion from BEV, JS3CNet [6] employs dense 3D and graph convolution to link point cloud and voxel features, and Symphonies [19] adopts deformable crossattention to generate voxel features from the multi-scale depth and RGB images. Lastly, VPDD [23] utilizes the diffusion model to remove noise and complete the scene. These methods have geometry construction and feature propagation limitations as they assume a static perspective and disregard the semantic uncertainty in dynamic sequences. To address this, we propose a method that leverages confident voxels to model the semantic uncertainty and employs multi-frame distillation to enhance the uncertainty modeling.Knowledge distillation transfers knowledge from the teacher to the student network. Smaller3D [25] distills at different levels. PointDistiller [30] proposes local distillation. PVD [26] adopts super voxel partition to utilize geometric information better. S2M2-SSD [31] distills multi-modal knowledge to network with point cloud as input. CMKD [32] distills from the network with the point cloud as input to the network with the monocular image as input. 2DPASS [33] proposes multi-to single-modal distillation that fuses point cloud and image features and distills fused features to the student network. SMF-SSD [34] distills multi-frame knowledge to a single-frame network at three levels. M2SKD [35] performs distillation for difficult categories from multi-to single-frame. 3D-to-BEV [27] achieves distillation from 3D to BEV view. SDSeg3D [36] distills from data-augmented teacher to student without augmentation to improve robustness. In contrast, we adopt multi-to-single frame distillation to extract accurate semantic information.The information within adjacent point cloud frames is complementary. This understanding forms the basis of our research. M2SKD [35] and SMF-SSD [34] fuse aligned multi-frame point cloud for distillation, specially M2SKD [35] only fuses complex samples. TemporalLatticeNet [37] adopts LSTM and GRU to capture temporal relationships better. MarS3D [38] builds Motion-Aware Feature Learning to extract motion instance features. TemporalLidarSeg [39] and MemorySeg [40] employ a Memory mechanism to fuse the features with other frames. Meta-RangeSeg [41] uses Meta-Kernel [42] to aggregate spatial-temporal features. SpSequenceNet [43] proposes Cross-frame Global Attention and Local Interpolation to fuse features. P4Transformer [44] designs Point 4D Convolution to capture the spatial-temporal relationship. PST-Transformer [45] extract spatial-temporal features in a decoupled-joint manner. Moreover, SVQNet [46] splits historical points into voxel-adjacent neighborhoods and historical contexts to complete local and global information. While commendable, existing methods often struggle to fuse point cloud information efficiently. They cannot assign different weights to point clouds, highlighting the need for a more comprehensive solution. We construct a multi-frame network by fusing the feature maps with weighted fusion to guide the single-frame network by distillation.Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: We discuss the limitations of the work in "Limitations". Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. 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method overview: 
 We present VPNet and pipeline of CVP and MFKD in Figure 1 and 2. In single-frame network, given point cloud P i ∈ R Ni×4 , we process it with shared MLP and get 3D voxel feature map V i ∈ R L×W ×H×C and BEV feature map B i ∈ R L×W ×C (see Figure 1 (b)), N i is the number of points, C is the channel number of V i and B i . We pass B i through BEV completion branch and get completed BEV feature mapF bev i ∈ R L×W ×H×C ′ , C ′is the channel number of F bev i . Then, we pass V i through segmentation and completion subnetwork in the 3D completion branch. Given semantic embedded feature map S i ∈ R L×W ×H×C ′ produced by segmentation subnetwork, Confident Voxel Proposal (CVP) learns Q groups of offsets {D q i ∈ R Ji×3 | q = 0, 1, . . . , Q -1} for occupied voxel coordinates M i ∈ R Ji×3 and features U i ∈ R Ji×C ′ in S i to compute confident voxel coordinates and features, it builds confident feature maps {E q i | q = 0, 1, . . S i S i A i ′ A i stage-1 of MFKD stage-2 of MFKD ′ A k E i 0 E i Q-1 Figure 2:The pipeline of CVP and MFKD. The semantics feature maps are produced with a segmentation subnetwork in the 3D branch. fusion of {E q i | q = 0, 1, . . . , Q -1}, J i is the number of occupied voxels. After that, we pass A i through other parts of completion subnetwork as completed 3D feature map F 3d i ∈ R L×W ×H×C ′ (see Figure 1(b) ). Finally we fuse F bev i and F 3d i with BEV-3D Fusion to maintain final completion resultR i ∈ R L×W ×H×C ′′where C ′′ indicates the number of semantic categories.In multi-frame network, given point clouds {P i , . . . , P k }, We pass them through segmentation subnetwork and separate CVP of 3D branch and obtain augmented feature maps {A ′ i , . . . , A ′ k } (see Figure 2(a) ). Then we fuse them and get multi-frame fused augmented feature map A ′ ∈ R L×W ×H×C ′ . We regard A ′ as augmented feature map A i in a single-frame network, and the other modules of the multi-frame network are consistent with a single-frame network (see Figure 1(a) ).We set the branch number in the CVP of the single-frame network to be the same as the frame number in the multi-frame network. Moreover, we divide Multi-Frame Knowledge Distillation (MFKD) into two stages in Figure 2 , we caculate the difference between {A i , . . . , A k } and {E 0 i , . . . , E Q-1 i } correspondingly in stage-1 distillation to drive the branches in CVP of the single-frame network to learn the semantic feature distribution of corresponding frame and condense the various possibilities contained in the corresponding frame. We compute the difference between A ′ and A i in stage-2 distillation to drive CVP in the single-frame network to learn multi-frame fused semantics further.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 
architecture of vpnet: 
 This section details the dual-branch VPNet with the confident voxel proposal (CVP) and the multiframe knowledge distillation (MFKD).As Figure 1(b) illustrates, VPNet has a 3D completion branch and a BEV completion branch, and we utilize multiple feature fusion schemes to combine them to achieve improved completion results.Feature Initialization Given a point cloud P i = {(x p , y p , z p , r p ) | p = 0, 1, . . . , N i -1}. x p , y p , z p are the coordinates. r p is the reflectivity of point p, we update P i as P ′ i during voxelization. This update is represented as P ′ i = {(x p , y p , z p , δx p , δy p , δz p , r p ) | p = 0, 1, . . . , N i -1}, where δx p , δy p , δz p are the differences in each dimension between the coordinates and the voxel center that point p belongs to. Then, we initialize V i and B i as follows:EQUATIONwhere F is a shared MLP that extracts the point-wise features, M and M indicate selecting the maximum from points that are in the same voxel or column.The 3D completion branch comprises a segmentation subnetwork and a completion subnetwork. The segmentation subnetwork, adapted from Cylinder3D [47] , captures semantic embedded feature map S i from V i . These feature maps are fed into the completion subnetwork, which synthesizes the completed 3D feature map F 3d i . The completion subnetwork includes the proposed CVP module and several 3D dense convolution kernels of varying sizes.In parallel, the BEV completion branch utilizes a 2D U-Net architecture. It reconstructs the scene from BEV. The BEV feature map B i is processed through this branch to produce the completed BEV feature map F bev i . With sum operation, we compress features extracted by the 3D segmentation subnetwork's encoder blocks along the height axis. We integrate the compressed feature maps to corresponding levels of BEV encoder blocks. This establishes an early fusion of 3D and BEV features that enhances the global perception capabilities of the 3D branch and the spatial analysis capabilities of the BEV branch. We utilize BEV-3D Fusion to generate the final completion result R i as:EQUATIONwhere I is a convolution layer that increases the feature channels of F bev i , R is a reshape operation that expands the height dimension from channels and C is concatenation along the channel dimension. This establishes the later fusion of 3D and BEV completion branches.We propose confident voxels by offset learning and feature propagating from occupied voxels. We take q th branch as an example to describe the details of CVP.Offset Learning As illustrated in Figure 3 , the segmentation subnetwork extracts a sparse semantics embedded feature map S i , from which we initialize the occupied voxel coordinates M i = {(x j , y j , z j ) | j = 0, 1, . . . , J i -1} and occupied voxel features as U i ∈ R Ji×C ′ . In Figure 3 (a), we initialize random noise Z q i ∈ R Ji×Cz , C z is the channel number of noise Z q i and Z q i ∼ N (0, 1). We computes a groups of offsets D q i ∈ R Ji×3 for each coordinate in M i as:EQUATIONFi is the shared MLP in CVP. M i allows the model to consider the geometric information of the partial scene, U i introduces rich semantic context. The random noise Z q i drives the voxel coordinates M i away from the initial position and ensures the robustness of offset learning. By sampling random noise across multiple branches of the CVP module, we generate various offset sets, enabling the inference of multiple semantic possibilities for the voxels. During offset learning, we employ occupied voxel coordinates M g of completion ground truth as supervision.Voxel Proposal As shown in Figure 3 (b), we propose coordinates that contain decimals by adding offsets D q i to initial coordinates M i and compute confident voxel coordinates G q i with rounding operation on proposed coordinates. We formulate the feature propagation process as:EQUATIONwhere W is the operation of updating feature U i according to the offsets D q i that makes the features that propagate farther be pruned more. It ensures the reliability of feature propagation, I is feature interpolation that computes the feature of the voxel center from proposed points in the same voxel, and Y q i is the confident voxel features after propagation. We construct confident feature map E q i as:EQUATIONwhere D is confident voxels with coordinates G q i and confident voxel features Y q i . DDCM is a modified Dimension-Decomposition based Context Modeling [47] module that refines the features after propagation and reconstructs the semantic context. E i 0 E i Q-1 ... ...  i 0  i Q-1 W i 0 W i Q-1 A iFigure 4 : Architecture of the multi-branch fusion.As Figure 4 indicates, we adopt the weighted fusion strategy [48] and modify it to fuse the branches in CVP and compress the possibilities in branches. We compute the weights W q i ∈ R 1×C ′ as:EQUATIONÃ is local average pooling that compresses the local representation for each branch. We flatten the pooled feature then, and J q i is the fully connected layer for q th branch. We fuse the branches in CVP according to the weights and get an augmented feature map A i .We construct a multi-frame network that proposes confident voxels and generates an augmented feature map for each frame with CVPs. We utilize MFKD to distill the semantic knowledge of augmented feature maps into a single-frame model in two stages to condense the voxel possibilities.Multi-Frame Network As illustrated in Figure 1 (a), we align the point cloud frames {P i , . . . , P k } to the coordinate of the current frame P i . We input the aligned point clouds into the 3D completion branch separately. We get multi-frame augmented feature maps {A i , . . . , A k }. We fuse them as A ′ with the above-mentioned weighted fusion strategy. We regard A ′ as A in the single-frame network to build the multi-frame network. We input {V i , . . . , V k } into 3D completion branch and only input B i into BEV completion branch. We construct a multi-frame network that leverages multi-frame point clouds to model voxel semantic uncertainty with multiple CVPs from a 3D perspective. E i 0 E i Q-1 ′ A i ′ A k ... ... ... ...Figure 5 : The overall architecture of MFKD. MFKD constructs two stages of distillation between CVPs of multiframe networks and CVPs of the single-frame network.We obtain augmented feature maps {A i , . . . , A k } in multi-frame network and confident feature maps{E 0 i , . . . , E Q-1 i} in single-frame network, we calculate the difference L s1 between the corresponding feature maps withEQUATION) where Â is a local average pooling function with kernel size s × s × s to construct super voxels to meet the sparsity of features, and E is the Kullback-Leibler divergence function. We build stage-1 distillation to drive the branches in CVP to simulate the knowledge learned by multi-frame CVPs and condense possibilities of the corresponding frame to branch in CVP.Training Losses To improve the accuracy of semantic uncertainty modeling and reconstruct the scene details, given fused augmented feature map A ′ in the multi-frame network and augmented feature map A i in the single-frame network, we calculate the difference L s2 between them as:EQUATIONWe avoid utilizing a super voxel partition here to prevent the blurring of features. Thus, we build stage-2 distillation to drive CVP in the single-frame network to simulate the knowledge after multi-frame CVPs. Finally, we achieve multi-frame distillation by joint stage-1 and -2 distillation as:EQUATIONWe formulate total loss in the single-frame and multi-frame networks as:EQUATIONwhere k = i. L com is completion loss. L seg is segmentation loss. L geo is geometry loss between proposed coordinates and coordinates ground truth. Here, we utilize Chamfer Distance [49] . α, β, γ and δ are weights of losses. We set α = 1.00, β = 0.10, γ = 0.01 and δ = 0.50 during distillation.5 Experiments 
implementation details: 
 We implement VPNet with PyTorch3 and train it on A6000 GPUs with a mini-batch of 8 for 80 epochs; we use Adam [50] optimizer with an initial learning rate of 0.001. We set feature map channel number C ′ = 32, random noise channel number C z = 4, CVP branch number Q = 3, and super voxel partition kernel size s = 4.We evaluate VPNet on SemanticKITTI [28] and SemanticPOSS [29] According to SSCNet [1] , we evaluate VPNet on Scene Completion (SC) with intersection-over-union (IoU), on Semantic Scene Completion (SSC) with IoU of each semantic category and mean of all semantic categories' IoU (mIoU).In the ablation study, we conduct experiments on SemanticKITTI [28] validation set.We evaluate the impact of the BEV completion branch, segmentation subnetwork, completion subnetwork without CVP, and completion subnetwork with CVP. As In the third row, we use the BEV branch and segmentation subnetwork. Under the augmentation of 3D semantics, we get (58.3% IoU and 24.5% mIoU) which are much higher than the performances of the BEV branch and 3D branch separately.In the fourth row, we add a completion subnetwork without CVP to the network; this method produces (59.1% IoU, 24.9% mIoU) that improves the IoU with 0.8%. This proves the effectiveness of joint completion from different perspectives. The 3D branch riches the details (in the semantic aspect), while the BEV branch completes the scene coarsely and with higher completeness (from the geometric aspect).We integrate CVP into the dual-branch network and achieve (59.3% IoU, 25.6% mIoU), a significant improvement of 0.7% mIoU compared to the network without CVP. This incremental improvement underscores the value of modeling the uncertainty of voxel semantics under the guidance of geometry for completion, marking a step forward in our understanding and application of these techniques.Internal Study of CVP We analyze the components of CVP in Table 2 . In Table 2 (a), we assemble CVP with random noise Z q i with different channel numbers. We get the best performance with C z = 4. When less than 4, the learning of offsets is insufficient, and the semantic possibility learned from the voxel proposal is lacking. When more significant than 4, the random noise introduces too much meaningless information that adversely impacts the network's performance.We propose confident voxels with multiple branches, so we analyze the impact of branch number Q in Table 2 (b). The network performance improves when the branch number is increased, and we get the best completion performance when we set the branch number to Q = 3. However, when we set Q = 4, we get similar performance (59.2% IoU, 25.6% mIoU) with Q = 3 as too many branches bring distractions to the network so we set Q = 3 during training.The fusion strategy of multiple branches influences the performance of voxel semantic uncertainty modeling; we construct CVP with different fusion strategies and show the results in Table 2(c). Here, we set branch number Q to 3. We compare the weighted fusion scheme with addition, concatenation, and average. These standard methods of feature fusion are weak in uncertainty modeling. Among the compared strategies, addition gets the best performance (59.3% IoU, 25.4% mIoU), but the weighted fusion we utilize still outperforms it with 0.2% mIoU. This demonstrates that weighted fusion models the voxel semantic uncertainty more accurately. Internal Study of MFKD As we build CVP with Q = 3, we implement a multi-frame network with three frames to build the distillation relationships between frame and branch in CVP correspondingly, and we present the results in Table 3 . We get the best performance (61.1% IoU, 26.8% mIoU) with frames t/t+2/t+4, where t is the frame we use to train the single-frame. We get (60.2% IoU, 26.3% mIoU) with frames t/t+1/t+2 as the adjacent frames contain insufficient supplementary information, and frames with larger intervals like t/t+3/t+6 have less guidance for modeling the uncertainty of semantics. IoU mIoU KD [51] 58.9 25.3 PVKD [26] 59.1 25.7 DSKD [16] 59 We conduct experiments using different distillation stages in Table 4 (a). With stage 1 without super voxel partition, we get similar results (59.5% IoU, 25.5% mIoU) with the single-frame network, as ordinary distillation distracts the offset learning due to the neglect of sparsity. We add the super voxel partition to stage-1 distillation, and this method produces (59.3% IoU, 25.9% mIoU). We also build MFKD with stage-2 distillation only and get (59.6% IoU, 25.8% mIoU) that proves voxel-wise guidance like stage-2 is helpful to the semantic uncertainty modeling. We get better performance (59.6% IoU, 26.1% mIoU) with stage-1 with super voxel partition and stage-2 distillations that provide more accurate guidance. And we compare MFKD with other distillation methods in Table 4 (b) where MFKD performs better than others and this proves the effectiveness of MFKD.We compare our method with state-of-the-art methods on SemanticKITTI online testing set in Table 5 .We present the visualization comparison in Figure 6 . Our method outperforms other methods and demonstrates competitive performance. VPNet produces (60.4% IoU, 25.0% mIoU) that with 1.6% IoU and 1.5% mIoU improvement than SSA-SC [5] when training with single-frame without MFKD. It achieves (60.7% IoU, 25.6% mIoU) with 0.3% IoU and 0.6% mIoU improvement with MFKD. VPNet outperforms other methods in most of the semantic categories, and this demonstrates its effectiveness. In the first and fifth rows of Figure 6 , VPNet achieves more complete geometry than other methods. It captures semantics more accurately (see the second and fourth rows). In the third and fifth rows, VPNet achieves complete and precise completion results for the distance of the scene thanks to the novel multi-frame distillation approach. We also validate the effectiveness of our method on the SemanticPOSS validation set and compare it with some representative methods in Table 6 , and our method produces better completion performance (57.3% IoU, 23.3% mIoU) than other methods. Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [Yes] Justification: We present the code of CVP module in "Core code" of "Appendix / supplemental material".Guidelines:• The answer NA means that paper does not include experiments requiring code.• Please see the NeurIPS code and data submission guidelines (https://nips.cc/ public/guides/CodeSubmissionPolicy) for more details. • While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark). • The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https: //nips.cc/public/guides/CodeSubmissionPolicy) for more details. • The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc. • The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why. • At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable). • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
conclusion: 
 The recent progress in semantic scene completion has been achieved using the geometry and semantics of point clouds. Our paper introduces a dual-branch nework called VPNet with a confident voxel proposal that generates confident voxels through offset learning and multi-frame knowledge distillation that distills the possibilities from multi-frame to single-frame network. Our method has shown competitive performance on the SemanticKITTI and SemanticPOSS datasets.Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes] Justification: We provide the details (learning rate, optimizer, batch size, number of epoch, and so on) of training process in "Implementation Details" of "Experiments", and describe the datasets and metrics in "Datasets and Metrics" of "Experiments".Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
broader impacts: 
 VPNet enhances 3D perception capabilities by directly restoring geometric structures through voxel coordinate offset learning and conducting precise semantic feature propagation, thereby improving the ability for semantic scene completion. This work has inconspicuous negative societal impacts.Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [No] Justification: We run all experiments three times, and report the mean value of the results.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions). • The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
limitations: 
 The method encounters limitations in fine-grained geometric shape learning due to a single round of offset learning and feature propagation. An iterative process can solve this limitation. 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] Justification: The semantic scene completion tasks in our work do not involve large pretrained models and such, and do not carry the risk of being misused to the extent of causing adverse effects. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: In our paper, we primarily engage with public datasets, we have cited them properly and set the license in the website of the OpenReview. Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. • If this information is not available online, the authors are encouraged to reach out to the asset's creators. 
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 Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?Answer: [NA] Justification: This paper does not release new assets.Guidelines:• The answer NA means that the paper does not release new assets.• Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc. • The paper should discuss whether and how consent was obtained from people whose asset is used. • At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file. 
crowdsourcing and research with human subjects: 
 Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?Answer: [NA] Justification: The paper does not involve crowdsourcing nor research with human subjects.Guidelines:• The answer NA means that the paper does not involve crowdsourcing nor research with human subjects. • Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. • According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. 
institutional review board (irb) approvals or equivalent for research with human subjects: 
 Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?Answer: [NA] Justification: The paper does not involve crowdsourcing nor research with human subjects.Guidelines:• The answer NA means that the paper does not involve crowdsourcing nor research with human subjects. • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. • We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. • For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review. 
 
 Appendix: 1. Claims Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? Answer: [Yes] Justification: The contributions of this paper are 1) proposing confident voxels along with their semantic label possibilities by learning offsets from occupied voxel coordinates/features, and 2) distilling knowledge across multiple point cloud frames to enhance voxel labeling. We describe them in "Abstract" and "Introduction". Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.https://pytorch.org/ 


Decision: The paper presents valuable contributions to semantic scene completion. After the rebuttal, none of the reviewers have major concerns and the AC agrees that the paper can be accepted.

Reviews: review: paper_summary: paper_summary: This paper focuses on semantic scene completion. The authors propose a novel voxel proposal network and combine multi-frame knowledge distillation technique to reconstructs the scene geometry and implicitly models the uncertainty of voxel-wise semantic labels by presenting multiple possibilities for voxels. The proposed method has been proven effective on SemanticKITTI and SemanticPOSS datasets.
strength_weakness: strength_weakness: - My biggest concern is that the author's motivation for using MFKD is not clearly explained. Why use it to predict voxel-wise labels.
- Figure 3 is too complicated to understand.
questions: questions: Can authors open source code？
limitations: limitations: Please refer to the weaknesses.
paper_summary: paper_summary: The author introduced the Voxel Proposal Network (VPNet), a dual-branch semantic scene completion method with two key innovations.
First, the Confident Voxel Proposal (CVP) module, which includes offset learning and voxel proposal, generates a confident feature map based on the semantics-embedded feature map, enabling completion in the 3D branch. Second, the Multi-Frame Knowledge Distillation (MFKD) module distills semantic knowledge from each augmented feature map of the multi-frame network into the branches of the single-frame network in two stages, enhancing completion performance.
strength_weakness: strength_weakness: 1. Lack of Comparison with State-of-the-Art Methods: The performance of VPNet is not compared with current state-of-the-art methods such as SCPNet. Specifically, (mIoU) achieved by VPNet is far lower than that of SCPNet. Or authors can argue and clarify clearly the protocol differences that render the comparison invalid.

2. SCPNet has already implemented multi-frame distillation to enhance performance. Thus, VPNet's use of Multi-Frame Knowledge Distillation (MFKD) is not readily qualified as a core contribution in the title. Or authors can dig into the effects of multi-frame distillation in different frameworks like SCPNet and [A].

[A] MonoOcc: Digging into Monocular Semantic Occupancy Prediction, ICRA 2024
questions: questions: It is recommended to answer questions whether there is a protocol compatibility with SCPNet and whether there are unique insights about multi-frame distillation in this study over SCPNet and MonoOcc.
limitations: limitations: No social impact limitations to be addressed.
paper_summary: paper_summary: To directly construct scene geometry and accurately propagate features from relted voxels, the paper proposes VPNet with a voxel proposal mechanism to identify confident voxels for completion and a multi-frame knowledge distillation scheme to fuse information from multi-sweep LiDAR. VPNet achieves better performance than other methods for semantic scene completion on SemanticKITTI and SemanticPOSS.
strength_weakness: strength_weakness: 1. For clarity, the paper uses massive symbols to elaborate technical details which is hard to follow. I think there could be some abstraction.
2. For motivation, the paper claims that existing methods have problem with directly constructing scene geometry and accurately propagating voxel features, but I do not see why this is true and how VPNet is better in these aspects.
questions: questions: Please give some explanations about the weaknesses.
limitations: limitations: The authors discuss the limitations of VPNet in the appendix but do not provide possible solutions.
paper_summary: paper_summary: This paper introduces Voxel Proposal Network which achieves semantic scene completion from both voxel and BEV perspectives. Beginning with confident voxel proposals, the information is propagated to other voxels, aiming to handle dynamic aspects. Multi-Frame Knowledge Distillation is also incorporated to accurately predict the voxel-wise labels.
strength_weakness: strength_weakness: 1. The motivation of the proposed components should be provided. At the beginning of the abstract, this paper mentioned that most previous methods use 3D/2D convolutions or attention mechanisms, having limitations in directly constructing geometry and accurately propagating features from related voxels. However, how can the proposed method tackle this problem？The authors are required to give a straightforward analysis.
2. The writing of this paper needs improvement. The author employs a single branch with multiple subnetworks for single-frame input, which seems processing the input sequentially. But line117 describes it using the “branch”, which can easily be misinterpreted as parallel. Besides, to my understanding, the multi-frame branch distill the knowledge of different timestamps to the results from different blocks, aiming to enhance the performance of feature propagation. It would better to give a overview before introducing the specific design of the network components.
3. The com. w/o CVP and com w/ CVP are confusing in the Table 1.
4. Comparison with other knowledge distillation algorithms (at least three algorithms) and the proposed MFKD is necessary, as done in the Table 4 in the SCPNet [1].
5. SCPNet also distills knowledge from multi-frame inputs to single-frame network, more analysis on the differences between MFKD and SCPNet is necessary.

Overall, the writing of this paper should be improved. Presenting motivation will help the readers understand the method better.
questions: questions: Please see the weakness.
limitations: limitations: The authors adequately addressed the limitations and broader impacts. (line 464 - line 476)

Comments: title: Thanks for your suggestion!
BA
Sincerely Request Your New Comment Again
Sincerely Request Your New Comment Again
Thanks for Your Review
Thanks for Your Review
Noted
Sincerely Request Your New Comment
Sincerely Request Your New Comment
Thanks for your suggestion!
comment: Dear Reviewer Rcs7,

We thank you again for your valuable comments, which will significantly help us to polish our paper. We will follow your suggestion to add the comparison with SCPNet to the experiment section,  and add the insights of proposing MFKD and comparison with SCPNet/MonoOCC on distillation to the introduction section. 

Best,

Authors of Paper ID 4361
I have updated the recommendation to BA.

I have two questions. They are answered.

The authors should incorporate SOTA methods and clatification with SCPNet/MonoOCC into the manuscript. The current version that states MFKD as a new contribution without mentioning SCPNet/MonoOCC is not appropriate.
Dear Reviewer Rcs7,

Again, please allow us to extend our sincere thanks to you, for your time and efforts of reviewing our paper. As the deadline for the authors' response is approaching, we sincerely request your further comment on our primary response. This will definitely give us a valuable chance to address the questions unsolved.

Best,

Authors of Paper ID 4361
Dear Reviewer 2ocQ,

Again, please allow us to extend our sincere thanks to you, for your time and efforts of reviewing our paper. As the deadline for the authors' response is approaching, we sincerely request your comment on our primary response. This will definitely give us a valuable chance to address the questions unsolved.

Best,

Authors of Paper ID 4361
Dear Reviewer 8T9a,

Thank you again for your review. We are pleased to see that the questions raised by you are solved.

Best,

Authors of Paper ID 4361
Dear Reviewer Zma5,

Thank you again for your review. We are pleased to see that the questions raised by you are solved.

Best,

Authors of Paper ID 4361
I have read the rebuttal. The arguments are noted that in an enhanced setting the method can compare with SCPNet and the distillation differs from SCPNet and Monoocc in some ways. Now chasing 3DV deadline. Will update the score after a careful checking later: (1) the soundness of the new experiment; (2) the scientific value of the distillation setting. Since my two questions are both answered, I do not have further questions for now.
Dear Reviewer Rcs7,

We thank you again for your valuable comments, which significantly help us to polish our paper. Could we kindly know if the responses have addressed your concerns and if further explanations or clarifications are needed? Your time and efforts in evaluating our work are appreciated greatly.

Best,

Authors of Paper ID 4361
Dear Reviewer 2ocQ,

We thank you again for your valuable comments, which significantly help us to polish our paper. Could we kindly know if the responses have addressed your concerns and if further explanations or clarifications are needed? Your time and efforts in evaluating our work are appreciated greatly.

Best,

Authors of Paper ID  4361
Thank the authors for their reply. The authors have addressed my concerns, so I have update my score.
Thanks, I've updated my score.
Dear Reviewer 8T9a,

We thank you again for your valuable comments, which will significantly help us to polish our paper. We will follow your suggestion to add the insights of proposing the multi-frame knowledge distillation and confident voxel proposal to the introduction section. We will also add overviews of these components to help readers to better understand our method.

We are releasing the code package, including training and testing with 2D/3D convolution, via this anonymous GitHub repository: https://github.com/anonymous-github-VPNet/VPNet. Per your request, we are preparing a thorough README to explain how to install the necessary dependency and code package and configure the training/testing process.

Best,

Authors of Paper ID 4361
I appreciate the authors' response to my questions. Current manuscript lacks the valuable insights of the authors, which are more important for other researchers. It would better to update the overall manuscript in the revision. I'm willing to raise my score to weak accept later. Besides, I have the last question. As responded to the Reviewer Rcs7, VPNet achieves better performance than the SCPNet when using 3D convolutions. Will the authors release this part of code? (including training and evaluation)