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 2024 poster (2024)
Keywords: Semantic Scene completion，Voxel Proposal，Knowledge Distillation
Primary area: machine_vision

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.

Voxel Proposal Network via Multi-Frame Knowledge Distillation for Semantic Scene Completion
Lubo Wang!
Di Lin
Kairui Yang!
Ruonan Liu² f
Qing Guo
Wuyuan Xie
Miaohui Wang*
Lingyu Liang 6 Yi Wang
Ping Li?
'College of Intelligence and Computing, Tianjin University Shanghai Jiao Tong University
*IHPC and CFAR, Agency for Science, Technology and Research, Singapore
Shenzhen University 'Pazhou Lab South China University of Technology "The Hong Kong Polytechnic University wanglubo@t:u..duu cn fi.lin@tju edu.cn cn
Abstract
Semantic scene completion is a difficult task that involves completing the geometry and semantics of scene from point clouds in 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 Bird's-Eye 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-we results on the SemanticKITTI and SemanticPOSS datasets.
Introduction
Understanding 3D scenes based on LiDAR point clouds 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 the point clouds poses 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 [ 3
4
5
6 7 8 9
10 [2]
14
15|
16:
[8 use convolutions complete the partial scene. Some completion heavily rely on diverse attention mechanisms as the attention mechanism
17
methods 19:
20
21 22
can capture spatial relationships and update the features. The diffusion model
23 also applies the
"Co-first authors.
1 Conresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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completion task. These methods have significantly improved the performance of static single-frame based 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 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- Fraii--remee Knowledge Distillation (MFKD). We present the overview architecture of VPNet Figure !l 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 create similar semantic 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 other KD methods 25 26 27 16].
We evaluate the effectiveness VPNet on the SemanticKITTI (28) and SemanticPOSS [29] datasets, where we achieve state-of-the of-the-arttttttttt performances on the semantic scene completion task.
2
Related Work
2.1 Semantic Scene Completion
The current approaches for SSC rely on convolution, attention, or diffusion models. For example, SSCNet 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 44 incorporates UD block in 3D U-Net to efficiently fuse encoder and decoder features. Furthermore, SSA-SC (5] uses 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 cross- attention 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 method that leverages confident voxels to model the semantic uncertainty and employs multi-frame distillation to enhance the uncertainty modeling.
2.2 Knowledge Distillation
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 single-fram fngle-frame network at three levels. M2SKD (35

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(a) multi--frame network
BEV-3 3D Fusion
feature initialization
CVP
point cloud P. P.
CVP
completion
completion result
RM
MFKD
segmentation
completion
voxel feature map V,
confident voxel proposal (CVP)
BEV 3D fusion
point cloud
completion result R,
BEV feature map B,
BEV completion
(b) single-frame network
Figure 1: The architecture of VPNet. It consists of BEV and 3D completion branches. CVP in the 3D branch proposes confident voxels present possibilities for voxels and model the semantic uncertainty of voxels implicitly. Moreover, we construct multi-frann filim--famm network and employ MFKD enhance the accuracy uncertainty modeling. We represent free voxels transparent.
performs distillation for diffieult categories from multi- single-frame. 3D-to-BEV 27 achieves distillation from 3D to BEV view. SDSeg3I) (36) distills from aata-auumented teacher to student without augmentation improve robustness. In contrast, we adopt multi-to-single frame distillation to extract accurate semantic information.
2.3 Point Cloud Sequence Learning
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. TemporalLatticeNee [37] adopts
LSTM and GRU to capture temporal relationships better. MarS3D [38 builds Motion-Awan Feature
Learning to extract motion instance features. TemporalLidarSes 33 and MemorySeg 40 employ
Memory mechanism to fuse the features with other frames. Meta-RangeSeg [1 uses Meta-
Kernel 42 to aggregate spatial-temporal features. SpSequenceNet 43 proposes Cross-frame Global
Attention and Local Interpolation to fuse features. P4Traanffrmmrr
designs Point 4D Convolution
to capture the spatial-temporal relationship. PST-Transformer 45 extract spatial-temporal features in 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 multi-frame network by fusing the feature maps with weighted fusion to guide the single-frame network by distillation.
3 Method Overview
We present VPNet and pipeline of CVP and MFKD in Figure T and In single-frame network. given point cloud P; E IR N,4, we process with shared MLP and get 3D voxel feature map V, R and BEV feature map B, B (see Figure b)), N, is the number of points, C is the channel number of V, and B, We pass B; through BEV completion branch and get completed BEV feature map hheve E X xxC" C' is the channel number of Fbev Then, pass V, through segmentation and completion subnetwork in the 3D completion branch. Given semantic embedded feature map S; JR4 xxxXxxxxC produced by segmentation subnetwork, Confident Voxel Proposal (CVP) leams Q groups of offsets (D/ E RJx33
0, 1....... Q 1} for occupied voxel coordinates M, E A and features U, Jxxx in S; to compute confident voxel coordinates and features, it builds confident feature maps { 1-=00 0, 1,..., Q 1} with confident voxels and S, with Q branches in CVP. And CVP produces an augmented feature map A; 424 xxxx' by

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semantics embeded feature map
confident feature maps
augmented Teature maps
augmented feature map
CVP
multi-branch luston
multi frrmm network
CVP
multi-branch fusion
single le se--rrume network
CVP
multi-branch fusion
stage-1 of MFKD stage-2 of MFKI)
Figure 2: The pipeline of CVP and MFKD. The semantics feature maps are produced with segmentation subnetwork in the 3D branch.
Ju is the number of occupied voxels. After that, we pass A, through other parts of completion subnetwork as completed 3D feature map F E I.xxxxxee (see Figure Tb)). Finally we fuse Flev and F with BEV-3D Fusion maintain final completion result R. E R Xxxxxxx" where C" indicates the number of semantic categories.
fusion of {E 0,
In multi-frame network, given point clouds (P....., P&). We pass them through segmentation subnetwork and separate CVP of 3D branch and obtain augmented feature maps {A,.... A (see Figure
2 a)).. Then fuse them and get multi-frame fused augmented feature map A' e We regard A' as augmented feature map A, in single-frame network, and the other modules the multi-frame network are consistent with single- frame network (see Figure aa).
RLXW x
We set the branch number in the CVP of the single-frane network to be the same as the frame number in the multi-frame network, Moreover, we divide Multi-Framm Knowledge Distillation (MFKD) into two stages Figure 2 we caculate the difference between {A...... Ax } and (E...... EP-1 correspondingly in stage-1 distillation to drive the branches in CVP of the single-frane 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, in stage-2 distillation to drive CVP in the single-frame network to learn multi-frams fused semantics further.
4 Architecture of VPNet
This section details the dual-branch VPNet with the confident voxel proposal (CVP) and the multi- frame knowledge distillation (MFKD).
4.1
Dual-branch Completion Network
As Figure (b)) illustrates, VPNet has 3D completion branch and BEV completion branch, and utilize multiple feature fusion schemes to combine them to achieve improved completion results.
Feature Initialization Given point cloud P; {xxp Mp: Zp, rp) p 0. ...., N₁ - 1}. Tp. Up- Ep are the coordinates. Tp is the reflectivity of point p. we update P, as P during voxelization. This update is represented as P (xxp Up: Fp. ōurpp ōyy. ōzp, rp) 1p 0, o,... N₂ 1}. where 8xpp. Syp. 82p are the differences in each dimension between the coordinates and the voxel center that point belongs to. Then, initialize V, and B, follows:
(V,, = (M.()(F(P)).
where F is a shared MLP that extracts the point-wise features, M and M indicate selecting the maximum from points that in the same voxel or column.
Dual-branch Completion The completion branch comprises a segmentation subnetwork and completion subnetwork. The segmentation subnetwork, adapted from Cylinder3D [47], captures

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offiet learning
(b) voxel proposal
occupied voxel coordinates M,
rounding
confident voxe! coordinates G
semantics embeded feature map S,
rundom noise Z
mip
offset D
confident feature map E.
occupied voxel features C,
confident voxel features Y
feature interpolation
addition
concatenation
weighting
Figure 3: Branch of confident voxel proposal (CVP), we divide into two steps: (a) offset learning and (b) voxel proposal.
semantic embedded feature map S; from V,. These feature maps are fed into the completion subnetwork, which synthesizes the completed 3D feature map F 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, is processed through this branch to produce the completed BEV feature map Fhev, With sum operation, subnetwork's encoder blocks along the height axis. We integrate the compressed feature maps to corresponding levels of BEV encoder blocks. This establishes 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,
compress features extracted by the 3D segmentation
R: C(R(I(F) F
where I is convolution layer that increases the feature channels of F R. 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.
4.2 Confident Voxel Proposal
We propose confident voxels by offset learning and feature propagating from occupied voxels. We take 9t branch an example describe the details of CVP.
Offset Learning As illustrated in Figure 3 the segmentation subnetwork extracts a sparse semantics embedded feature map S,, from which initialize the occupied voxel coordinates
M. {Ir 2y) j o,, }} and occupied voxel features U, R4xx'"_ In Figure 3 a), we initialize random noise Z R.xxC., C2 is the channel number of noise Z and
Z No (0, 1). We computes groups of offsets D R2x3 for each coordinate in M,
D = =(C U., Z!)).
33)
F; is the shared MLP in CVP. M, allows the model to consider the geometric information of the partial scene, U, introduces rich semantic context. The random noise Z drives the voxel coordinates M, 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, of completion ground truth as supervision.
Voxel Proposal As shown in Figure 3b), b), we propose coordinates that contain decimals by adding offsets D initial coordinates M, and compute confident voxel coordinates G with rounding operation on proposed coordinates. We formulate the feature propagation process as:
Y = I(W(U,, D!)),
where Wi is the operation of updating feature U, according to the offsets D that makes the features that propagate farther be pruned more. It ensures the reliability of feature propagation, I is feature
5

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interpolation that computes the feature of the voxel center from proposed points in the same voxel, and Y is the confident voxel features after propagation. We construct confident feature map E as:
E (D(), YJ) + S.),
where is confident voxels with coordinates G; and confident voxel features Y: DDCM is modified
based Context Modeling [47] module that refines the features
after propagation and reconstructs the semantic context.
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
weights
confident feature maps
E²
W E BIXC'
To
as:
Wee
augmented feature map A,
teJ
w JA Σ q=0
)
(6)
addition
local avgpooling
multiplication
Á is local average pooling that com- presses the local representation for each branch. We flatten the pooled feature then, and J." is the fully connected layer for 9th branch. We fuse the branches in CVP according to the weights and get augmented feature map A,.
Figure 4: Architecture of the multi-branch fusion.
4.3 Multi-Frame Knowledge Distillation
We construct 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-Frams Network As illustrated in Figure a)), we align the point cloud frames .P.,... P the coordinate of the current frame P;. We input the aligned point clouds into the 3D completion branch separately. We get mulli--rame augmented feature maps {A,.... Ax}. We fuse them as A' with the abbve-mentioned weighted fusion strategy. We regard A' as A in the single-frame network to build the multi-frame network. We input (V..... V into 3D completion branch and only input B, into BEV complction 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.
Multi-frame Distillation
We obtain augmented feature maps {A,...., As} in multi-frame net- work and confident feature maps (E....., E E........E-1) in single-frame network, we calculate the difference L.1 between the corresponding feature maps with
stage-1
stage-2 augmented feature map
multi- multi-fram frome Deeweek
augmented map Aj
feature map Ai
augmented
mullt frame
supervoxel
Cu
supervexd partition
single single- frame NC Notwork
Sen--trancch Faskun
Q-1 L.1 -= > (CA(EE))AAA....
confident feature map
confident
auggmented map
feature map gi
(7)
Figure 5: The overall architecture of MFKD. MFKD con- structs two stages of distillation between CVPs of multi- frame networks and CVPs of the single-frame network.
where Á is local average pooling function with kernel size s X s x to construct super voxels to meet the sparsity of features, and E is the Kull- back Leibler divergence function. We build stage-1 1 distillation to drive the branches in CVP to simulate the knowledge learned by multi-fram CVPs and condense possibilities of the corresponding frame to branch in CVP.
Training I 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

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feature map A; in the single-frama network, we calculate the difference L22 between them as:
L.2 - E=,
(8)
We avoid utilizing super voxel partition here to prevent the blurring of features. Thus, we build stage- 2 distillation 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:
Lkd L. + Le2-
(9)
We formulate total loss in the single-frame and multi-frame networks as:
C = aCoo + BLeeg + yyeo + 8Ckd:
(10)
where k = i. Lcom is completion loss. Lee is segmentation loss. Lgeo is geometry loss between proposed coordinates and coordinates ground truth. Here, we utilize Chamfer Distance [49]. ar, 8, ? and are weights of losses. We set a 1.00, 3 = 0.10, 0.01 and = 0.50 during distillation.
5 Experiments
5.1 Implementation Details
We implement VPNet with Py PyTorch and train on A6000 GPUs with a mini-batch of 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 C₂ 4, CVP branch number Q and super voxel partition kernel size = 4.
5.2 Datasets and Metrics
We evaluate VPNet on SemanticKITTI 28 and SemanticPOSS [29] datasets, composed of real outdoor point cloud sequences. SemanticKITTI contains 22 sequences with 19 categories, 11/1/10 sequences for trainingvalldatiiononniin testing. SemanticPOSS contains six sequences divided into 11 categories; it contains 5/1 sequences for training/validattion (testing).
According SSCNet [1, we evaluate VPNet on Scene Completion (SC) with intersection-over (IoU), on Semantic Scene Completion (SSC) with IoU of each semantic category and mean of all semantic categories' IoU (mloU).
5.3
Ablation Study of VPNet
In the ablation study, conduct experiments SemanticKITTI
28 validation set.
Analysis of Network Framework We evaluate the impact of the BEV completion branch, segmen- tation subnetwork, completion subnetwork without CVP, and completion subnetwork with CVP. As Table illustraees in the first and second rows, we use the BEV branch and 3D branch without CVP separately and get (56.4% loU, 22.2% mloU) and (50.3% IoU, 23.6% mloU).
In the third row, we use the BEV branch and segmentation subnetwork. Under the augmentation of 3D semanties, we get (58.3% IoU and 24.5% mloU) which are much higher than the performances of the BEV branch and 3D branch separately. In fourth row, we add a completion subnetwork without CVP to the network; this method produces (59. IoU, 24.9% mloU) that improves the loU with 0.8%. This proves the effectiveness of joint com- pletion from different perspectives. The 3D branch riches the details (in the semantic
Table 1: Impact of -ual-branch network components. "seg." means 3D segmentation subnetwork and "com." means 3D completion subnetwork.
BEV
seg,
w/o CVP
com
w/ CVP
com.
IoU
mloU
X
56.4
22.2
X
/
X
50.3
23.6
/
X
X
58.3
24.5
/
V
X
59.1
24.9
X
59.3
25.6
https://pytorch.orgg

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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% mloU), a significant improvement of 0.7% mloU compared 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 step forward in our understanding and application of these techniques.
Internal Study CVP
We analyze the components of CVP in Table 2 Table a we assemble CVP with random noise Z with different channel numbers. We get the best performance with C₂ = 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, we analyze the impact of branch number Q Table The network performance improves when the branch number is increased, and we get the best completion performance when set the branch number to Q 3. However, when we set Q = 4, we get similar performance (59. .2% IoU, 25.6% mloU) with Q = as too many branches set Q 3 during training.
bring distractions to the network
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 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% mloU), but the weighted fusion we utilize still outperforms it with 0.2% mloU. This demonstrates that weighted fusion models the voxel semantic uncertainty more accurately.
Table Internal studies on random noise (a), branch number (b) and fusion strategy (c) CVP.
(a) Channel number of noise.
(b) Branch number in CVP.
(c) Fusion of branches in CVP.
noise C,
IoU
mloU
branch Q
IoU
mloU
fusion strategy
loU
mloU
0
59.0
25.0
59.1
24.9
addition
59.3
25.4
2
59.1
25.4
59.0
25.1
concatenation
59.2
25.2
4
59.3
25.6
59.2
25.4
6
58.8
25.3
59.3
25.6
average
59.0
25.3
8
58.5
24.9
59.2
25.6
weighted fusion
59.3
25.6
Internal Study of MFKD As build CVP with Q = 3, we implement multi-frame net- work with three frames to build the distillation relationships between frame and branch in CVP correspondingly, and present the results in Ta- ble We get the best performance (61.1% loU, 26.8% mloU) with frames t/t+2/t+4, where is the frame we use to train the single-frame. We get (60.2% IoU, 26.3% mloU) with frames t/t+1/t++ as the adjacent frames contain insufficient supplementary information, and frames with larger intervals like 1/++3/t+6 have less guidance for modeling the uncertainty of semantics.
Table 3: Frames in multi- network.
frames
IoU
mloU
t/t/t
59.5
25.6
t/111 /42
60.2
26.3
1/442/t4
61.1
26.8
1/t43/t+6
61.4
Table Internal studies on stages of MFKD (a) and comparison with other distillation methods (b).
(a) Stages of MFKD. "voxel" means common voxel partition, "super-" (b) Comparsion with other distillation methods.
means super voxel partition.
stage-1 I (voxel)
stage-1 (super-)
stage-2
IoU
mloU
IoU
mloU
59.3
25.6
KD 51
58.9
25.3
X
59.5
25.5
PVKD 26
59.1
25.7
X
/
59.3
25.9
DSKD 16
59.3
25.8
X
X
59.6
25.8
X
59.6
26.1
MFKD (Ours)
59.6
26.1

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Input
LMSCNet
SSA-SC
VPNet (Ours)
Ground Truth
bicycle car
motorcycle
truck other-vchicle M person traffic-sign
bicyelist
motorcyclist trunk
parking road
sidewalk
other-ground
building = pole
fence
terrain
vegetation
Figure 6: Completion results of different methods on SemanticKITTI validation set.
We conduct experiments using different distillation stages in Table a). With stage without super voxel partition, we similar results (59.5% IoU, 25.5% mloU) 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% mloU). We also build MFKD with stage-2 distillation only and get (59.6% IoU, 25.8% mloU) that proves voxel-wise guidance like stage-2 is helpful to the semantic uncertainty modeling. We get better performance (59.6% loU, 26. I mloU) with stage-1 I with super voxel partition and stage-2 distillations that provide more accurate guidance. And we compare MFKD with other distillation methods in Table (b) where MFKD performs better than others and this proves the effectiveness of MFKD.
5.4 State-of-the-art Comparison
We compare our method with state-of ofte-oof-hhe-rr the-art methods on SemanticKITTI online testing set in Table 5 We present the visualization comparison Figure Our method outperforms other methods and demonstrates competitive performance. VPNet produces (60.4% IoU, 25.0% mloU) that with 1.6% IoU and 1.5% mloU improvement than SSA-SC [ when training with single-frame without MFKD. It achieves (60.7% IoU, 25.6% mloU) with 0.3% IoU and 0.6% mloU improvement with MFKD.

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Table 5: Comparison of VPNet with other works on SemanticKITTI online testing set.
other ground
manonyele
other vehicle
munorcylii
a.dewalk
building
bieyde
.ood
car
track
trunk
pole
Medkdd
SSCNe:
0
29.8
9.5
27.6
12.0
15.6
8.0
20.9
10.4
1.8
0.0
8.0
0.1
25.8
11.9
18.3
0.0
0.0
0.0
14.4
7.9
3.7
SSCNe.ta tull
50.0
16.1
51.2
30.3
27.1
6.4
34.5
24.3
122
0.5
0.5
4.3
35.3
18.2
29.0
0.3
0.3
0.0
19.9
13.1
6.7
133D
+
29.8
9.5
28.0
17.0
15.7
4.9
23.2
10.7
2.4
0.0
0.0
0.2
24.7
12.5
18.3
0.0
0.1
0.0
13.2
7.0
3.5
ISaDIDNe H3
25.0
10.2
27.5
18.5
18.9
6.6
22.1
5.0
2.3
0.3
0.0
4.0
19.5
12.9
20.2
233
0.6
6.0
15.8
7.6
7.0
LMSCNe: M
55.3
17.0
64.0
33.1
24.9
3.2
36.7
29.5
2.5
0.0
0.0
0.1
40.5
1940
30.8
040
0.0
6.
20.5
15.7
0.5
LMSCNe-SS
56.7
176
66.8
34.7
29.0
46
36.1
50.9
15
0.0
08
41.3
19.9
32.1
0.0
21.3
150
0.6
Lecal-DIFs [S]
57.7
22.7
67.9
42.
40.1
11.4
404
548
44
36
24
43
42.3
26.5
59
25
13
29.0
21.3
17.5
Net
23.8
39.
34.9
14.1
33.3
73
14.4
12.7
196
51
30,4
18.9
15.9
SSA-SC
23.5
722
43.7
10.9
3655
57
139
74
25.6
26
30.7
14.5
VPNet outperforms other methods in most of the semantic categories, and this demonstrates its effectiveness. In the first and fifth rows of Figure 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 with some representative methods in Table 6 and our method produces better completion performance (57.3% ToU, 23.3% mloU) than other methods.
Table Comparison of VPNet with other works on SemanticPOSS validation set.
saaticc sign
mloU
13 people
2. eddrr
plans
Sence
Method
1 CUE
SSCNet
1
41.7
12.1
10
34.4
4.8
34.3
25.6
10
SSA-SC
0.5
4.3
11.1
Ours (wlo MFKD)
36.9
22.4
151
04
1.7
10
499
464
9.4
28.0
43.2
45.0
51.9
Oues (W/ MEKD)
$7.3
23.3
14.6
11
2.
26
5.4
47.0
14.2
30.6
43.3
44.0
30.5
Conclusion
The recent progress in semantic scene completion has been achieved using the geometry and se- mantics of point clouds. Our paper introduces duualbbanch nework called VPNet with a confident voxel proposal that generates confident voxels through offset learning and distillation that distills the possibilities from multi-frame to single-frame network. Our method has shown competitive performance on the SemanticKITTI and SemanticPOSS datasets.
knowledge
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 socictal impacts.
8 Limitations
The method encounters limitations in fine-grained geometric shape learning due to single round of offset learning and feature propagation. An iterative process can solve this limitation.
Acknowledgement
This work was supported in part by the Key Science and Technology Program of the Ministry of Emergency Management of the People's Republic of China (2044EMST010102) Guangdong- Kong Joint Funding for Technology and Innovation Grant (2023A0505010021,, in part by the Hong Kong Polytechnic University Grant (P0048387), in part by the National Research Foundation, Singapore, and DSO National Laboratories under the Al Singapore Programme (AISG2- GC-2023-008), in part by Career Development Fund (CDF) of Agency for Science, Technology and Research (C233312028), in part by the National Research Foundation, Singapore, Infocomm Media Development Authority under its Trust Tech Funding Initiative (DTC-RGC-04).
part by the
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The contributions of this paper are 1) proposing confident voxels along with their semantic label possibilities by learning offsets from occupied voxel coondinates/features, and 2) distilling knowledge across multiple point cloud frames to enhance voxel labeling. We describe them in "Abstract" and "Introduction".
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Answer:
[NA]
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We describe the pipeline of VPNet in "Method Overview" and discuss the design of the major modules within the network in "Architecture of VPNet". We conduct ablation studies on detailed modules of the network with public datasets in "Ablation Study of VPNet" of "Experiments", and we provide the details of training process in "Implementation Details" of "Experiments". These can facilitate the reproducibility of experimental results.
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Justification: The semantic scene completion tasks in our work do not involve large pre- trained models and such, and do not carry the risk of being misused to the extent of causing adverse effects.
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20


Reviews:
[{"review_id": "FgfDxAWmpo", "reviewer": "Reviewer_Zma5", "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.", "questions": "Can authors open source code？", "limitations": "Please refer to the weaknesses.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The proposed method achieves state-of-the-art results on SemanticKITTI and SemanticPOSS datasets.\n- The Voxel Proposal Network sounds interesting.", "weaknesses": "- My biggest concern is that the author's motivation for using MFKD is not clearly explained. Why use it to predict voxel-wise labels.\n- Figure 3 is too complicated to understand.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your valuable comments on the motivation and figure. Below, we respond to your questions point-to-point.\n\n**Q1: My biggest concern is that the author's motivation for using MFKD is not clearly explained. Why use it to predict voxel-wise labels?**\n\n**Answer:** Here, we introduce the motivation of MFKD for semantic scene completion. Suppose multiple frames of point clouds are available rather than a single frame only. In that case, people can leverage these sequential point clouds to complement each other and infer a complete 3D shape with semantic labels represented by voxels or point clouds. **Though this strategy is a natural technical choice, using multiple frames needs a complex model for learning the knowledge, including the visual pattern of a single frame and the temporal relationship between objects across various frames, substantially increasing the model complexity and inference time**. MFKD belongs to the family of knowledge distillation, where we transfer the knowledge learned by a complex Teacher model to a simpler Student model. Specifically for semantic scene completion, MFKD can distill the knowledge of multiple frames learned by the Teacher model into the Student model that only takes input as a single frame to infer the complete 3D shape quickly and accurately.\n\nThe existing methods like SCPNet and MonoOcc with MFKD generally compute the visual feature of a single frame, where they fuse multiple frames’ features to capture the temporal information. They distill the knowledge of fused frames into the Student model. **During the above distillation process, the Student model misses the opportunity to learn to infer the lost details of each frame, which means the Student model only utilizes the available information of multiple frames**. We propose a novel MFKD scheme, which distills the knowledge of multiple frames and each frame’s inference of the lost details (named the Confident Voxel Proposal in our paper) into the Student model. The confident voxel proposals of different frames, which show consistent shapes, likely play as the overlapping objects across frames. They provide richer information for completing the 3D shape.\n\nOur paper follows the conventional protocol of semantic scene completion to represent the 3D shape as voxels for a fair comparison with other methods. MFKD can also help to predict point-wise labels in the 3D point cloud, which will be investigated in our future work.\n\n\n**Q2: Figure 3 is too complicated to understand.**\n\n**Answer:** We follow your comment to simplify Figure 3, the simplified figure is now included as Figure 1 of the rebuttal PDF.\n\n**Q3: Can authors open source code?**\n\n**Answer:** We release the code package via this anonymous Github repository **https://github.com/anonymous-github-VPNet/VPNet**.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LvgBVJYE40", "reviewer": "Reviewer_Rcs7", "summary": "The author introduced the Voxel Proposal Network (VPNet), a dual-branch semantic scene completion method with two key innovations.\nFirst, 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.", "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": "No social impact limitations to be addressed.", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "This manuscript has clear structure, well-benchmarked qualitative and quantitative results.", "weaknesses": "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.\n\n2. 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].\n\n[A] MonoOcc: Digging into Monocular Semantic Occupancy Prediction, ICRA 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your valuable comments on our paper's experiments and core contribution. Below, we respond to your questions point-to-point.\n\n**Q1: 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.**\n\n**Answer:** SCPNet utilizes many 3D convolution based subnetwork to complete the 3D shape, which makes the completion model complex while we only utilize simple convolution for refinement of CVP.  And as SCPNet indicates, this improve the mIoU greatly. Though SCPNet conducts the sparse convolution to save a portion of computation, the dense occupancy of voxel-wise features still makes the computation very expensive. Table 1 of the rebuttal PDF compares SCPNet (the first row) with our VPNet (the second row) concerning the model parameters (M), GPU memory (GB), inference speed (FPS), IoU, and mIoU. VPNet outperforms SCPNet regarding IoU, requiring fewer model parameters and much faster inference time. Because IoU indicates the accuracy of predicting the object occupancy, VPNet with a higher IoU shows a more robust power of inferring the geometry of the 3D shape than SCPNet.\n\nIn contrast to IoU, we use mIoU to measure the accuracy of predicting the voxel-wise semantic labels of the 3D shape.  SCPNet yields a higher mIoU than VPNet, because 3D convolution better captures the semantic context of the objects. Though using 3D convolution with expensive computation contradicts our intention of using knowledge distillation to save model parameters and computation, we equip VPNet with more 3D convolutions after extracting point features and voxelization like SCPNet for a fair comparison. Table 1 of the rebuttal PDF shows that VPNet with 3D convolution (the third row) successfully outperforms SCPNet regarding mIoU, and it keeps similar model parameters with SCPNet.\n\n\n**Q2: 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 MonoOcc.**\n\n**Answer:** Our MFKD contains core contributions that are different from those of SCPNet and MonoOcc. The existing methods like SCPNet and MonoOcc with MFKD generally compute the visual feature of a single frame, where they fuse multiple frames’ features to capture the temporal information. They distill the knowledge of fused frames into the Student model. **During the above distillation process of the existing methods, the Student model misses the opportunity to learn to infer the lost details of each frame, which means the Student model only utilizes the available information of multiple frames**. We propose a novel MFKD scheme, which distills the knowledge of multiple frames and each frame’s inference of the lost details (named the Confident Voxel Proposal in our paper) into the Student model. The confident voxel proposals of different frames, which show consistent shapes, likely play as the overlapping objects across frames. They provide richer information for completing the 3D shape. Please also see our response to **Q1** of Reviewer Zma5 for more details on the motivation for using MFKD in semantic scene completion.\n\nFurthermore, a CVP explicitly reconstructs the lost details of the 3D shape. **Our approach provides multiple CVPs for each frame, ensuring a more comprehensive shape reconstruction of the lost details**. In contrast to other methods, CVPs facilitate feature propagation between the visible parts and the confident locations with object occupancy, thereby significantly improving the completion result. Please also see our response to **Q2** of Reviewer 2ocQ for the discussion on the importance of distilling the knowledge of CVPs for semantic scene completion.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HkO2uq7eak", "reviewer": "Reviewer_2ocQ", "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.", "questions": "Please give some explanations about the weaknesses.", "limitations": "The authors discuss the limitations of VPNet in the appendix but do not provide possible solutions.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. For originality, VPNet predicts offsets to propose confident voxels which is distinct from existing methods.\n2. For quality, the paper conducts extensive experiments on two benchmarks for semantic scene completion, together with ablation studies.", "weaknesses": "1. For clarity, the paper uses massive symbols to elaborate technical details which is hard to follow. I think there could be some abstraction.\n2. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your valuable comments on our paper's symbols and motivation. Below, we respond to your questions point-to-point.\n\n**Q1: For clarity, the paper uses massive symbols to elaborate technical details which is hard to follow. I think there could be some abstraction.**\n\n**Answer:** We recognize that complex symbols stem from multiple levels of superscripts and subscripts. We will trim the redundant superscripts and subscripts to simplify these symbols and equations.\n\n\n**Q2: 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.**\n\n**Answer:** Please see our response to the second common question. The lost parts of the 3D shape lack sufficient information to infer the geometry and semantic labels. Thus, the completion model needs to regard the visible parts of the 3D shape as the cue to infer the lost parts. The existing models based on the deep networks typically use the convolution or attention scheme to learn the implicit correlation between the visible and lost parts from the training data. During the inference phase, the learned correlation helps the completion model to propagate the features of the visible parts to the underlying locations of the lost parts, whose geometry and semantic labels are inferred. Note that the lost details of the incomplete 3D shape are undetermined during the inference phase. **If the above feature propagation occurs between the visible parts and the locations without object occupancy, the inference of geometry and semantic labels likely fails**.\n\nWe propose the Confident Voxel Proposal (CVP) to address the limitations of existing models. A CVP can be regarded as an explicit reconstruction of the lost details of the 3D shape. **What sets our approach apart from the existing methods is that we provide multiple CVPs for each frame, ensuring a more comprehensive shape reconstruction of the lost details**. These CVPs facilitate feature propagation between the visible parts and the confident locations with object occupancy, thereby significantly improving the completion result. Table 3 of the rebuttal PDF compares VPNet with and without the explicit learning of CVPs. This is done by using/removing the geometry loss $\\mathcal{L}_{geo}$ in Eq.(10) of the paper. VPNet, with the explicit learning of CVPs, achieves IoU and mIoU better than the counterpart without the explicit learning of CVPs (see the first and second rows of Table 3 of the rebuttal PDF).\n\n\n**Q3: The authors discuss the limitations of VPNet in the appendix but do not provide possible solutions.**\n\n**Answer:** In the appendix, we introduce VPNet's limitations, which stem from inaccurate semantic segmentation and offset learning. These limitations lead to unreliable feature propagation, leaving much room for improving the completion result. We can equip VPNet with a more accurate segmentation subnetwork to alleviate the above limitations. Furthermore, we can iteratively conduct the offset learning and feature propagation, where the offset learning can be based on a more refined 3D shape. The extract computation during inference brought by the iteration can be saved by knowledge distillation.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OOc2C0GjaD", "reviewer": "Reviewer_8T9a", "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.", "questions": "Please see the weakness.", "limitations": "The authors adequately addressed the limitations and broader impacts. (line 464 - line 476)", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Reconstructing from the reliable occupied voxels and propagating information from these voxels to others is a good solution for the semantic scene completion. \n2. information distillation from multi-frames is also a normal strategy to improve the performance.  \n3. The ablation experiments in this paper are quite comprehensive. \n4. The code provided in the appendix clearly demonstrates the details of the CVP module.", "weaknesses": "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.\n2. 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.\n3. The com. w/o CVP and com w/ CVP are confusing in the Table 1.\n4. 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].\n5. SCPNet also distills knowledge from multi-frame inputs to single-frame network, more analysis on the differences between MFKD and SCPNet is necessary.\n\nOverall, the writing of this paper should be improved. Presenting motivation will help the readers understand the method better.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your valuable comments on our paper's motivation, writing, and experiment. Below, we respond to your questions point-to-point.\n\n**Q1: 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?**\n\n**Answer:** The lost parts of the 3D shape lack sufficient information to infer the geometry and semantic labels. Thus, the completion model needs to regard the visible parts of the 3D shape as the cue to infer the lost parts. The existing models based on the deep networks typically use the convolution or attention scheme to learn the implicit correlation between the visible and lost parts from the training data. During the inference phase, the learned correlation helps the completion model to propagate the features of the visible parts to the underlying locations of the lost parts, whose geometry and semantic labels are inferred. Note that the lost details of the incomplete 3D shape are undetermined during the inference phase. **If the above feature propagation occurs between the visible parts and the locations without object occupancy, the inference of geometry and semantic labels likely fails**.\n\nWe propose the Confident Voxel Proposal (CVP) to address the limitations of existing models. A CVP can be regarded as an explicit reconstruction of the lost details of the 3D shape. **What sets our approach apart from the existing methods is that we provide multiple CVPs for each frame, ensuring a more comprehensive shape reconstruction of the lost details**. These CVPs facilitate feature propagation between the visible parts and the confident locations with object occupancy, thereby significantly improving the completion result. Table 3 of the rebuttal PDF compares VPNet with and without the explicit learning of CVPs. This is done by using/removing the geometry loss $\\mathcal{L}_{geo}$ in Eq.(10) of the paper. VPNet, with the explicit learning of CVPs, achieves IoU and mIoU better than the counterpart without the explicit learning of CVPs (see the first and second rows of Table 3 of the rebuttal PDF).\n\n**Q2: 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 an overview before introducing the specific design of the network components.**\n\n**Answer:** We will follow your suggestion to clarify the branches and add an overview of each network component, briefly describing its input and output.\n\n**Q3: The com. w/o CVP and com. w/ CVP are confusing in the Table 1.**\n\n**Answer:** By using \"com. w/o CVP\", we mean the 3D branch (represented by pink blocks) in Figure 1(a) works without the subsequent CVPs, while  \"com. w/o CVP\" means the 3D branch with CVPs. We will add this clarification to the manuscript.\n\n**Q4: Comparison with other knowledge distillation algorithms (at least three algorithms) and the proposed MFKD is necessary.**\n\n**Answer:** We follow your advice to compare the proposed MFKD with other knowledge distillation algorithms without changing the other parts of our framework. We compare MFKD with KD [Statistics 2015], PVKD [CVPR 2022], and DSKD [CVPR 2023] in Table 2 in the PDF along with the rebuttal. As shown in Table 2 of the rebuttal PDF, KD hurts the completion result because it only distills the features of the global scenes rather than proposals. PVKD and DSKD adopt distribution-based constraints but lack optimization before fusing voxel proposals across frames. In contrast, MFKD overcomes the above issues and achieves better completion results.\n\n\n**Q5: SCPNet also distills knowledge from multi-frame inputs to single-frame network. More analysis on the differences between MFKD and SCPNet is necessary.**\n\n**Answer:** Our MFKD contains core contributions different from those of SCPNet. The existing methods like SCPNet and MonoOcc with MFKD generally compute the visual feature of a single frame, where they fuse multiple frames’ features to capture the temporal information. They distill the knowledge of fused frames into the Student model. **During the above distillation process of the existing methods, the Student model misses the opportunity to learn to infer the lost details of each frame, which means the Student model only utilizes the available information of multiple frames**. We propose a novel MFKD scheme, which distills the knowledge of multiple frames and each frame’s inference of the lost details (named the Confident Voxel Proposal in our paper) into the Student model. The confident voxel proposals of different frames, which show consistent shapes, likely play as the overlapping objects across frames. They provide richer information for completing the 3D shape. Please also see our response to **Q1** of Reviewer Zma5 for more details on the motivation for using MFKD in semantic scene completion.\n\nFurthermore, a CVP explicitly reconstructs the lost details of the 3D shape. **Our approach provides multiple CVPs for each frame, ensuring a more comprehensive shape reconstruction of the lost details**. In contrast to other methods, CVPs facilitate feature propagation between the visible parts and the confident locations with object occupancy, thereby significantly improving the completion result. Please also see our response to **Q2** of Reviewer 2ocQ for the discussion on the importance of distilling the knowledge of CVPs for semantic scene completion.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We sincerely thank all reviewers for their constructive comments. We provide a rebuttal PDF containing extra figures and tables to answer the common questions raised by reviewers. **The detailed explanations can be found in the point-to-point response to every reviewer**.

**Q1: Too complicated figure.**
- Reviwer Zma5: "Figure 3 is too complicated to understand."
- Reviewer 2ocQ: "For clarity, the paper uses massive symbols to elaborate technical details which is hard to follow. I think there could be some abstraction."

**Answer:** In Figure 1 of the rebuttal PDF, we provide a simplified Figure 3. We recognize that complex symbols stem from multiple levels of superscripts and subscripts. We will trim the redundant superscripts and subscripts to simplify these symbols and equations.


**Q2: Motivation of multi-frame knowledge distillation (MFKD) and comparison with other distillation methods.**
- Reviwer Zma5: "My biggest concern is that the author's motivation for using MFKD is not clearly explained."
- Reviewr Rcs7: "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 MonoOcc."
- Reviewer 8T9a: "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. SCPNet also distills knowledge from multi-frame inputs to single-frame network, more analysis on the differences between MFKD and SCPNet is necessary."

**Answer:** In Table 1 of the rebuttal PDF, we compare VPNet with the state-of-the-art SCPNet in terms of the model parameters (M), GPU memory (GB), inference speed (FPS), IoU, and mIoU. In a fair setting, VPNet outperforms SCPNet. In Table 2 of the rebuttal PDF, we compare our MFKD with other knowledge distillation methods, KD [Statistics 2015], PVKD [CVPR 2022], and DSKD [CVPR 2023], where MFKD outperforms other methods.


**Q3: Confident Voxel Proposal (CVP) for geometry construction and feature propagation.**
- Reviewer 2ocQ: “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.”
- Reviewer 8T9a: “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.”

**Answer:** Table 3 of the rebuttal PDF compares VPNet with and without the explicit learning of CVPs. This is done by using/removing the geometry loss $\mathcal{L}_{geo}$ in Eq.(10) of the paper. VPNet, with the explicit learning of CVPs, achieves IoU and mIoU better than the counterpart without the explicit learning of CVPs (see the first and second rows of Table 3 of the rebuttal PDF).

Meta-review: 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.
Decision: Accept (poster)