Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
Authors: Meng Wang
Fan Wu
Ruihui Li
Qin Yunchuan
Zhuo Tang
Kenli Li
Venue: NeurIPS 2025 poster ()
Keywords: Semantic Scene Completion; autonomous driving
Primary area: applications

Abstract: 3D Semantic Scene Completion (SSC) provides comprehensive scene geometry and semantics for autonomous driving perception, which is crucial for enabling accurate and reliable decision-making. However, existing SSC methods are limited to capturing sparse information from the current frame or naively stacking multi-frame temporal features, thereby failing to acquire effective scene context. These approaches ignore critical motion dynamics and struggle to achieve temporal consistency. To address the above challenges, we propose a novel temporal SSC method FlowScene: Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance. By leveraging optical flow, FlowScene can integrate motion, different viewpoints, occlusions, and other contextual cues, thereby significantly improving the accuracy of 3D scene completion. Specifically, our framework introduces two key components: (1) a Flow-Guided Temporal Aggregation module that aligns and aggregates temporal features using optical flow, capturing motion-aware context and deformable structures; and (2) an Occlusion-Guided Voxel Refinement module that injects occlusion masks and temporally aggregated features into 3D voxel space, adaptively refining voxel representations for explicit geometric modeling.
Experimental results demonstrate that FlowScene achieves state-of-the-art performance, with mIoU of 17.70 and 20.81 on the SemanticKITTI and SSCBench-KITTI-360 benchmarks.

Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
Meng Wang
Fan Wu Ruihui Lift
Yunchuan Qin'
Zhuo Tang
Kenli Li
College of Computer Science and Electronic Engineering, Hunan University
State Key Laboratory Advanced Design and Manufacturing Technology for Vchicle, Human University {willem, wufan, liruihui, ginyunchuan, ztang, 1k1)@hnu. edu.
Project Page: http://ghhhh..ommmiiilloooosssss
Optical FLow
Occluston Mosk
Figure Given the temporal RGB images as input, our method can perform temporal modeling based on the corresponding optical flow and occlusion mask, and predict semantic scene completion for all voxels in 3D space.
Abstract
3D Semantic Scene Completion (SSC) provides comprehensive scene geometry and semantics for autonomous driving perception, which is crucial for enabling accurate and reliable decision-making However, existing SSC methods are limited to capturing sparse information from the current frame or naively stacking multi- frame temporal features, thereby failing acquire effective scene context. These approaches ignore critical motion dynamics and struggle to achieve temporal consistency. To address the above challenges, propose novel temporal SSC method FlowScene: Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance. By leveraging optical flow, FlowScene can integrate motion, different viewpoints, occlusions, and other contextual cues, thereby significantly improving the accuracy of 3D scene completion. Specifically, our framework introduces two key components: (1) a Flow-Guided Temporal Aggregation module that aligns and aggregates temporal features using optical flow, capturing motion- aware context and deformable structures; and (2) an Occlusion-Guidee Voxel Refinement module that injects occlusion masks and temporally aggregated features into 3D voxel space, adaptively refining voxel representations for explicit geometric modeling. Experimental results demonstrate that FlowScene achieves state-of- the-art performance, with mloU of 17.70 and 20.81 on the SemanticKITTI and SSCBench KITTI-360 benchmarks.
Introduction
One of the key challenges in autonomous driving is 3D scene understanding, which involves inter- preting the spatial layout and semantic properties of objects within the scene. The ability to perceive and accurately interpret 3D scenes is essential for making safe and informed driving decisions. Recently, the 3D Semantic Scene Completion (SSC) task (29 27 has gained significant attention in autonomous driving, it enables the joint inference of geometry and semantics from incomplete observations.
"Equal contributed. +Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Stack
View
Encoder
Transformation
(a) Previous temporal nethod.
Flow Estination
Flow&Mask
Occlusion Gu ded
Image Encoder
View
Transformation
(b) Our method optical flow guide temporal SSC.
Figure 2: Our method uses optical flow guide temporal SSC versus the previous temporal SSC method.
27 42 rely on input RGB images along with corresponding 3D data to predict volume occupancy and assign semantic labels. However, the dependence on 3D data often requires specialized and costly depth sensors, which can limit the broader applicability of SSC algorithms. Recently, many researchers [ 47 14 have investigated camera-based approaches to reconstruct dense 3D geometric structures and recover semantic information, offering a more accessible alternative.
Most existing SSC methods 26
46
6 15
typically rely on the limited observations available in the current frame to recover 3D geometry and semantics. Later, some researchers [17 [3 22 38
Previous camera- samera-based SSC methods 12
stacked historical temporal features or aligned features with estimated camera poses to enrich contextual information, as shown in Figure 2[). However, these direct temporal modeling methods overlook the scene motion context, fail to achieve temporal consistency, and inherently limit the increase of effective contextual cues. Based on these limitations, asked: How can accurately identify the correlation between historical frames and the current frame to guide temporal SSC modeling?
In this paper, we propose novel temporal SSC method: FlowScene, Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance. As shown in Figure 2(b). FlowScene uses optical flow to guide temporal modeling, injecting various types of information into the SSC model, such as motion, different viewpoints, deformation, texture, geometric structure, lighting, and occlusion. As shown in Figure T] the corresponding optical flow and occlusion masks are generated from the historical and current frame images, allowing for the further derivation of scene geometry and semantic structure. The positions and semantics of the car, tree trunk, vegetation, and pole within when they mutually occluded. Specifically, Temporal Aggregation module to effectively enhance temporal and
the red box in Figure [ are more accurate, we introduce the motion cues by incorporating motion and contextual information from previous frames. Furthermore, we design the Occlusion- Eeclusion-Guided Guided Voxel Refinement module, which leverages aggregated features and occlusion masks refine 3D voxel predictions for explicit geometric modeling. To evaluate the performance of FlowScene, we conduct thorough experiments on SemanticKITTI [I] and SSCBench- KTTTI360 [19 [6] Our method achieves state-of-the-ar performance. The main contributions of our work are summarized as follows:
We introduce FlowScene, novel approach to 3D SSC that incorporates optical flow guidance capture and model temporal and spatial dependencies across frames.
We propose the flow-guided temporal aggregation module, which effectively enhances temporal and motion cues by incorporating motion and contextual information from previous frames.
We design the ceclusion-guided voxel refinement module, which leverages aggregated features and occlusion masks refine 3D voxel predictions, enabling explicit geometric modeling and improving the accuracy of scene reconstruction in occluded regions.
We evaluate FlowScene on the SemanticKITTI and SSCTeechh-KITT11366 benchmarks, achieving state-of-the-arr performance. Our method surpasses the latest methods in both semantic and geometric analysis, demonstrating the effectiveness of optical flow-guided temporal modeling in SSC tasks.

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2
Related Work
3D Semantic Scene Completion. The vision-based 3D Semantic Scene Completion (SSC) solution
has received widespread attention in the field of autonomous driving perception. MonoScene 2 was the first infer dense 3D semantics from a single RGB image. TPVFormer 9 introduced a
ii-perspecttvee view (TPV) representation, extending BEV with two vertical planes. OccFormer 4
proposed dual-path transformer to encode voxel features, while VoxFormer [7 introduced a
two-stage pipeline for voxelized semantic scene understanding. SurroundOce 40 employed 3D
convolutions for progressive voxel upsampling and dense SSC ground truth generation. OctOce 24
utilized an actree-based representation for semantic occupancy prediction, while NDCScene 43 re-
defined spatial encoding by mapping 2D feature maps to normalized device coordinates (NDC) rather than world space. MonoOcc [48] enhanced 3D volumetric representations using image-conditionel cross-attention mechanism. H2GFormer 39 introduced a progressive feature reconstruction strategy to propagate 2D information across multiple viewpoints. Symphonize [2 extracted high-level in- stance features to serve as key-value pairs for cross-attention. HASSC 38 proposed a self-distillation framework to improve the performance of VoxFormer. Stereo-based methods, such as BRGScene (14) leveraged stereo depth estimation to resolve geometric ambiguities. MixSSC (36 fused forward projection sparsity with the denseness of depth-prior backward projection. CGFormer [44] utilized a context-aware query generator to initialize context-dependent queries tailored to individual input images, effectively capturing their unique characteristics and aggregating information within the region of interest. HTCL [13] decomposed temporal context learning into two hierarchical steps: cross-frame affinity measurement and affinity-based dynamic refinement. VLScene (37) leveraged vision-language models to extract high-level semantic priors for SSC.
Optical Flow for Visual Perception. Optical flow estimation, fundamental task in computer vision, aims to establish dense pixel-wise correspondences between consecutive frames. FlowNet [3] [1] introduced the first CNN-based end-to-end flow estimation pipeline, leveraging hicrarchical
pyramid structure. PWC-Net [I further refined this approach by incorporating multi-stage warping to handle large-displacemmnt motion. RAFT 34 introduced an iterative, recurrent architecture that refines residual flow predictions in fully convolutional manner. GMFlow 41 reframed optical flow as global matching problem, directly computing feature similarities to establish correspondences. Beyond motion estimation, optical flow has been leveraged to enhance various vision tasks. FlowTrrack (50) used optical flow to enrich feature representations and improve tracking accuracy. FGFA [49 employed flow-gun flow-guided feature aggregation for end-to-end video object detection. LoSh (45) utilized flow-based warping to propagate annotations across temporal neighbors, thereby boosting referring video object segmentation. DATMO (30) introduced moving object detection and tracking framework tailored for autonomous vehicles. DeVOS [4] incorporated optical flow into scene motion modeling, using it as prior for learnable offsets in video segmentation.
3 Methodology
3.1 Preliminary
Problem Setting. Given a of RGB images 1 = (T-i)) where is the number of historical temporal images, the objective is to jointly infer the geometry and semantics of 3D scene. This scene represented as a voxel grid depth in 3D space, respectively. Each voxel in the grid is assigned unique semantic label from the set C' E (Co, C1, ) where Cy represents empty space and the remaining classes {Ci.... CMr} correspond to specific semantic categories. Here, M denotes the total number of semantic classes. The goal is to leam transformation Y ((T,) that closely approximates the ground truth Ý
XxxxXxx(xx++)) where X, Y, Z represent the height, width, and
3.2 Overview
We illustrate our method in Figure 3 First, we use the lightweight image encoder RepVil [35] and FPN 20 to extract the current image features, F., and the historical temporal features, Fiemp We then apply the pre-trainee optical flow estimation model [41] (Section (.3) to generate Flomt-i-tty fwd Flow buo 1 The historical temporal features,
bidirectional optical flow, Flow = (Flow tii
Fimmp warped using Flowood to obtain Fwarp = { 1F---1 JE The bidirectional optical
3

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FlowScene
Flow-Guided Tenporal Aggregation
Favs
Inage Encoder
d
Flow Estimation
Voxel Encoder
Exbedding
Fay
Fg
Cross-Attention
FloMus
Floh
Siuilarity
Vegtor
V warp
Occlusion-Guided
Refinement
Firr
Occlusion -uidded Voxel Refinement
Flow-Guiden
Temporal Aggregation
1 mazi
Verfon
View Transfortnatioon
Mei gight
Figure The FlowScene framework is proposed for temproal 3D semantic scene completion.
flow is then used for occlusion detection through a forward and backward consistency check to obtain the cumulative mask, M E 0, 11xxx Subsequently, Fwarp, Ft. and M are passed into the FGTA module (Section 3.4) perform optical flow-guided temporal feature aggregation in the 2D image feature space, resulting the aggregated feature Fagg- Next, we apply the LSS view transformation [25] project Fr, Fagg. and M into the 3D voxel space, obtaining V., Vagg> and Vmask respectively. the subsequent OGVR module (Section (35), the two voxel features are fused based the occlusion information, yielding the refined voxel features, Vfine- Finally, Viine passes through the voxel encoder, then undergoes upsampling and linear projection to output the dense semantic voxels, Y.
3.3 Optical Flow Estimation
Flow-Guided Warping. Given a reference image frame and historical frames T the flow field Flow"-t-i F(1,, T+-s) is estimated by flow network F (e.g., GMFlow [41)). The feature map from the historical frame is warped to the reference frame according to the flow. The warping function is defined
Et-i-t = Warp(Fi-1 Flow
(1)
where Warp(-) is bilinear warping function applied to all locations of each channel in the feature map, and pl-i-t represents the feature map warped from frame I i to worp
Occlusion Detection. First, note that there is relative motion between almost all frames in
an autonomous driving scenario, which results in pixels in the current image that do not have corresponding matching pixels in the historical frames; these are referred occluded areas. To detect occlusion, as shown in Figure we use the commonly employed forward and backward which is implemented as:
consistency check technique 32 23
M CC(FFow' FFoo'
where CC(-) denotes the forward and backward consistency check function, and we have included detailed explanation in the Technical Appendix. For non-oceluded pixels, the forward optical flow should be the inverse of the backward optical flow of the corresponding pixel in the second frame. A pixel is marked as occluded if the mismatch between the two flows exceeds a predefined threshold. Thus, we define the occlusion flag whenever the constraint violated and 0 otherwise.
3.4 Flow-Guided Temporal Aggregation
Previous SSC methods either stacked historical frame features or estimated camera poses to align features, aiming to complement the current frame. However, this direct temporal modeling approach overlooks the scene motion context, fails achieve temporal consistency, and inherently limits the ability to leverage additional effective cues. To better incorporate time- and motion-relate cues,

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Fage Fe
Bachward Flow
Forword Flox
Surfong Toxe!
Forward Backward Consistency
Depth Béns
Figure 4: Occlusion detection is performed using forward-backwart consistency detection.
Figure 5: Projecting the occlusion mask into 3D voxel space with depth bins.
propose a flow-ge now-guided temporal aggregation module in 2D space. This module leverages optical flow information to align and aggregate temporal features along the motion path. As illustrated on the right side of Figure
Specifically, guided by optical flow, the historical frame features are warped to the reference frame. Features from different frames provide multiple information for the same object instance, such as motion, different viewpoints, deformations, textures, geometric structures, various lighting and occlusions. First, we assign different weights different spatial locations, while ensuring that the spatial weights remain the same across all feature channels. At position P, if the warped feature F (P) close to the feature F2 (P). it is assigned a larger weight. Otherwise, smaller weight assigned. Inspired by FGFA [49], we use the cosine similarity [1] to measure the similarity between the warped features and the reference frame features:
W (P) Mummilarity(FF.rp" (.))..
(orrp
Then, we use the similarity weights to aggregate these feature maps to enhance the scene motion context features. The aggregation feature Fagg is obtained as:
F Fagg
Σ Wiiiss Flarppt.
(4)
The non-oceluded regions in the historical frames usually have richer texture and feature information, which may be missing in the current frame due to visual occlusion. To address this, we enhance the current frame features, effectively fuse spatiotemporal information through the neighborhood cross-attention mechanism [7]. We selectively use non-occluded features from historical frames to prevent injecting unreliable or distorted information caused by occlusions inaccurate flow. Including occluded regions can introduce noise and harm completion quality, First, we select reliable non-occluded region features in the historical frames based on the occlusion mask. The reference features F; are used as query, and the warp features F Fwarp of the non-occluded regions serve as key and value. The specific operations are as follows:
(5) where NCC.(--) is the neighborhood cross attention mechanism. After these operations, F, fuses the non-occluded region information from both the current and historical frames, providing more stable and accurate features that enhance the perception of dynamic scenes and occluded regions.
F = =CCA(F... (1- (1-M) - Fwarp),
3.5
Occlusion-Guided Voxel Refinement
After passing through the FGTA module, time- and motion-related cues are injected into the image features F, and the aggregate features Fagg- However, for the 3D voxel space, there is lack of explicit geometric modeling. To incorporate occlusion and optical flow information into the 3D space, introduce the occlusion-guided voxel refinement module. This module enhances the semantic completion ability of the occluded region by employing a weighted strategy of the occlusion mask. As shown in the right side of Figure 3
Specifically, as shown in Figure 5] we follow the LSS view transformation paradigm and use depth bin D assignment to project F,, F "agg- and M into the 3D voxel space to obtain Vr, Vagg- and V Vmask- respectively,
V PooelPooling( Fr @ D);
Vagg VoxelPooling( Fagg @ D):
(6)
V Vmaak VoxelPooling M @ D),
5

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where @ is the dot product operation, VoxelPooling is the voxel pooling operation. First, we use Vmskk to distinguish occluded and non-occluded regions in the 3D voxel space. For the non-occluded region, we use the aggregate features Vags that fuse multiple cues. Subsequently, since the information from the corresponding position in the historical frame may be inaccurate due to occlusion, we use the voxel features from the current frame to update the occluded area to supplement the latest environmental information. Finally, by constructing a weighted matrix, we normalize the fused voxel features to ensure that there is no mutation at the boundary between the occluded and non-occluded areas, thereby improving the smoothness of the features. The specific operation as follows:
(1 Vmaak).. Vagg Vi
Viine
(1 Vmask) +
Finally, Vinn enters the sparse voxel encoder for feature extraction, and then performs linear prediction to output dense semantic voxels Y.
3.6 Training Loss
In the FlowScene framework, we adopt the scene-class affinity loss Leeat from MonoScene (2] to optimize precision, recall, and specificity concurrently. The scene-class affinity loss is applied to semantic and geometric predictions, in conjunction with the cross-entropy loss weighted by class frequencies. Besides, the intermediate depth distribution for view transformation supervised by the projections of LiDAR points, with the binary cross- cross-entropy loss La following BEVDepth [18]. The overall loss function formulated as follows:
Asem Seat + Ageeo scal AceLce + daLo,
(8)
where several \ are balancing coefficients.
4 Experiments
To assess the effectiveness of our FlowScene, we conducted thorough experiments using the large outdoor datasets SemanticKITTI [ SSCBIecc-KKTTII336 [16 Information about datasets, metrics, and detailed implementation details is provided in the Technical Appendix, where additional experiments and analysis are also provided.
Table 1:
Quantitative results on the SemanticKITTI hidden test set. The best and the second best results are in bold and underlined, respectively. The "S" and "T" denote single-frame images, and temporal images, respectively.
olber-vehicle
parkingg
building
truck
Mocdle
mobocyrle
vegetatiom
trunk
Mey
Eonce
pole
dgga
Methods
Vemmes
Inpor
Toll
MoneScone
CVPR 2023
18.80
3.30
0.50
3.30
TPV/ormer 0
CVPRR
2.60
2.40
2.90
11.26
OccFormer
2023
31.50
12.32
Sympheeiz
CVPR 20134
70
10.00
2.00
7.30
15.04
BRCSSeese
DICAF/2024
8.40
27.00
2.90
0.50
7.30
15.36
O0Fumeer
NIPS 2024
41
64
54
34.10
12
10
1.20
30
24.50
11:00
3
0
18.70
8.70
9.30
16.63
RorForme-T
CV23
54
33.50
1.90
60
24
0.00
5.70
13.41
H2GFomnes
54
AAA12024
T
43.52
57.90
30.
30,00
6.90
25.00
23.70
5.20
0.60
1.20
5.00
25.20
10.70
25.80
1.10
0.10
0.00
14.00
7:50
9.30
14,60
HASSSC
35
CVPR 2024
T
12.8
55
30
23.00
2.90
1.90
1.50
4.90
24.80
9.50
1
3.00
0.00
14.30
7.00
7.10
14.38
SGN 22
T1P2024
T
43.71
57
29.70
25.60
60
5.50
07
25.00
1,50
0.90
0.70
3.60
26.
90
12.00
26
0.00
0.30
0.00
14.77
9100
6.40
14.39
HTCL,
L3
ECCV 2004
46.25
23
64.40
54.80
33,80
40
75.00
27.30
5.20
1.80
2.20
25.
30
10.30
80
31.20
1.10
3
0.90
21.00
9.00
8
17.09
Ours
45.20
64
10
35.00
33.70
13.09
27.70
25.30
10,00
4.20
3.10
7.00
26.30
10.00
30.20
3,00
5,30
1
20.20
8.50
9.30
17.70
4.1 Main Results
Quantitative Results. As shown in Table 1 we compare FlowScene with the latest public methods
on the SemanticKITTI dataset, including approaches that use single-image immge input (S) and temporal
image input (T). Temporal methods, such as VoxFormer [17], H2GFormer [39], HASSC [38]. and SGN [22], utilize additional historical 5-frame input, while HTCL 13 uses a 3-frame historical input. In contrast, FlowScene uses only 2 historical frames as input, achieving the highest mloU for the overall semantic metric and the highest IoU for the completion metric. Compared to the best-performing HTCL with temporal input, FlowScene improves the mloU and IoU by 0.61 %

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Table 2: Quantitative results the JSCBench-KITTTLE test set. The best and the second best
results are in bold and underlined, respectively.
bicyele
motocydl
other-vehiek
person
purking
side walk
other-grnd
building
vegotaation
tCrrain
traf-sdga
other-struct.
other-abj
car
roudd
Fonce
pole
Methods
Prec.
Rec.
IoU
mloU
MonoSoene
0.58
28
VoxFormer
58.52 53,44
38.76
17.84
1.16
0.89
4.56
2.06
47001
9.67
27.21
2.89
31
4.97
28
14.69
6.51
6.92
3.79
2.43
11,91
TPVFemer
59.32
55.54
40.22
21.56
1.09
1.37
8.06
2.57
2.38
52.99
11.99
11.07
3.78
34,83
4.50
30.08
17.52
7.46
5.86
5.48
2.70
13.64
OccFormer
59.70
55,31
40.27
22.58
0.66
0.26
9.89
3.82
2.77
54.30
13
51.53
3.55
36.42
4.80
31.00
19.51
7.77
8.51
6.95
4.60
13.81
IAMSSC
41.80
1.76
7.01
Symphomies
69.24
54,88
44.12
30.02
1.85
5,90
25.07
12.06
8.20
54.94
13.83
32.76
6.93
35
8
38.33
11.52
14.01
9.57
14.44
11.28
18.58
CGFormee
48.07
29.85
3.42
3.96
17.59
6.79
6.63
63885
17.15
40.72
5.53
42.73
8 22
38
80
24.94
16
17.45
10.18
6.77
20.05
69.70
and 0.97%, respectively. When compared the best CGFormer, which uses single-frame input, FlowScene achieves improvements of 1.07% in mloU and 0.79% in loU. Additionally, our method achieves the best second-best results in most categories, outperforming or closely matching other methods. These results demonstrate the superiority of FlowScene in both geometry and semantics, effectively utilizing optical flow motion information and achieving temporal consistency.
To demonstrate the diversity of our model, we conducted experiments on the SSCBench- -ITTTI 360 dataset, shown in Table 2 It is worth noting that our method has huge advantage in the performance of potential moving objects (such as car, truck, other-vehicle, person, etc.)
Moreover, Table B illustrates the performance of FlowScene across three distance ranges (12.5m, 25. 35.6m, 51.2m) on the SemanticKITTI validation set. evident that our approach significantly outperforms state-of-the-ar methods at every tested distance. Furthermore, as shown in Table 4 we compare the inference time and number of parameters of our method with other state-of-the-ar methods on the SemanticKITTI validation set. The inference time of MonoScene is optimal because of its FLoSP feature projection method. But, FlowScene achieves state-of-the-art performance with mloU of 18.1 13%, while utilizing only 52.4M parameters. Additionally, FlowScene processes the extra 2-frame temporal image input with lower inference time, further demonstrating its efficiency and superior mloU performance.
Table 3: Comparison of different ranges on Se- manticKITTI validation set.
Table 4: Comparison of inference time and num- ber of parameters.
mloU(%) 25.6m
Method
Input
mloU(())
Times(s)
Params(M)
Venues
12.8m
51.2m
MonoScenc
T
12.96
0.281
132,4
MonoScene
CVPR 2022
12.25
11.30
OccFormer
13.58
203.4
VoxPormesT
CVPR 2023
21.55
18.42
13.35
VoxFormer
T
13.35
0,307
$7.9
HASSC-T
CVPR 2024
24.10
14.74
Symphonize
14.89
0.319
59.3
H2GFormes-T
AAAI
23.43
20.37
14.29
BRGScene
15.43
0,285
161.4
BRGScene
UCA4'2024
23.27
21.15
15.24
HTCL
17.13
0.297
181.4
SGN-T
TIP'2024-
25.70
22.02
15.32
Ours
18.13
0.301
52.4
VLScene
AAA1'2025
26.51
24.37
17.83
Ours
27.63
24.65
18.13
Qualitative Visualizations. To intuitively demonstrate the performance of FlowScene, Figure presents qualitative results for VoxFormer-T, BRGScene, and our method on the SemanticKITTI validation set. The first column displays the input reference image and the corresponding optical flow. It is evident that optical flow is particularly sensitive the perception of moving objects, such as cars and cyclists. Compared to BRGScene, our method more effectively captures the location and details of mutually occluded objects in the scene (e.g., the arrangement of multiple cars in the second row). In comparison to VoxFormer-T, FlowScene maintains better temporal consistency, as shown by the car parked the roadside in the blue box in the third row. Overall, our method demonstrates superior geometric and semantic visualization.
4.2 Analysis of Static and Dynamic Objects
To better understand the performance of our method across different types of semantic categories, we conduct a detailed analysis by separating static and dynamic object classes the autonomous driving datasets. For SemanticKITTI, define dynamic objects as the classes that are likely to
7

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InageSFlow
VoxFormer-1
BRGScene
Ours
Ground Truth
bicycle
notocycle
other-veh.
bicylist
building
vegetation
terrošn
car
truck
person
sotorcyclist
parking
other-grad
Fence
trunk
pole
traf.-sign
Figure 6: Qualitative results the SemanticKITTI validation set.
Table 5: Analysis of Static and Dynamic Objects
SemanticKITTI Static
SSCBench KITTI-360
Method
Dynamic
All-mloU
Dynamic
Static
All-mloU
MonoScene
3.81
16.36
11.08
5.21
15.87
12.13
VoxFormer
4.45
19.91
13.41
4.69
12.19
11.91
Symphonie
5.71
21.83
15.04
13.85
20.94
18.58
CGFormer
5.48
24.75
16.63
11.37
24.38
20.05
Ours
7.50
25.29
17.70
14.87
23.78
20.81
involve motion: car, truck, bicycle, motocycle, other-vehicle, person and bicylist as dynamic objects and others as static objects. For CECCCBnnc-KKTTT---- we classified car, bicycle, motocycle, truck, other-vehicle and person as dynamic objects and others static objects. T22 prreeentt presents a comparative evaluation of our method against several prior state-of-the-nn approaches, reporting mean IoU (mloU) separately for dynamic objects, static objects, and the overall average.
Our method clearly outperforms all baselines in both datasets. Notably: On SemanticKITTI, our model achieves 7.50% mloU for dynamic objects-the the highest among methods, surpassing CGFormer (5.48%) and Symphonie (5.71%). For static objects, we also lead with 25.29% again outperforming CGFormer (24.75%). On SSCBench -IIncch-.KTI166.... our method obtains 14.87% mloU on dynamic objects, significantly ahead of CGFormer (11.37%) and Symphonie (13.85%). Although CGFormer slightly surpasses us on static objects (24.38% vs. 23.78%). our method achieves the highest overall score of 20.81 % mloU. These results demonstrate that FlowScene plays crucial role handling motion and temporal variation, making especially effective in recognizing and completing dynamic objects. Unlike previous approaches that rely on frame stacking or static assumptions, our model adapts to motion patterns, improving semantic consistency across time.
Figure 7 iillssraaee the qualitative results of dynamic object modeling in a challenging real-world scenario. As shown, the cyclist in motion is effectively captured by the optical flow module, which accurately estimates the movement across frames. By leveraging this motion information, our model performs kinematic compensation, aligning temporal features and preserving object structure throughout the sequence. This results in more coherent and complete 3D semantic reconstruction of the dynamic scene. further support this case, we provide a supplementary video that offers an intuitive, trame-by-frane visualization of the temporal alignment and dynamic object modeling. This additional material highlights the superior capability of our method to handle complex motion compared to static-assie assumption basclines.

---

Figure 7: Dynamic object modeling case visualization results.
Table 6: Ablation study for Architecture Components on SemanticKITTI validation set. OFE: Optical
Flow Estimation; FGTA: Flow-Guided Temporal Aggregation; OGVR: Occlusion-Guided Voxel
Refinement; FGW: Flow-Guided Warping; OD: Occlusion Detection; TA: Temporal Aggregation; OCA: Occlusion Cross-Arie Attention; Ve: reference voxel features; Vagg: aggregation voxel features; "maski voxel occlusion mask.
Variants
FGW
OFE
OD
TA
FGTA
OCA
V,
V
OGVR
Vinsk
IoU(%)
mIo[((S)
Params(M)
Baseline
43.98
15.89
47.4
44.13
16.21
52.1
V
44.56
16.67
$2.1
44.42
17.08
$2.3
44.68
17.18
52.4
44.72
17.63
52.4
45.01
18.13
52.4
4.3
Ablation Studies
We conduct extensive ablation experiments for FlowScene on the Semantickitti validation set. Specif- ically, we analyze the impact of different architecture component variations Table
Optical Flow Estimation (OFE). The baseline model removes all components, using only the current image and two frames of historical images as input. After passing through the image encoder, all features are stacked together. Variant in Table 6 uses Lood-eidded Warping to align the temporal features to the reference moment, achieving 0.32% mloU improvement (Variant vs. Baseline). Additionally, Variant 2 incorporates Occlusion Detection to obtaín occlusion mask, which guides the interaction of non-occluded areas the 2D feature space, boosting the mloU score by 0.22% (Variant 2 vs. Variant 1).
FloweGuided Temporal Aggregation (FGTA). Table Variants 3, 4, and represent different configurations of the FGTA module: removing the FGTA module (Variant 3), removing the Occlusion Cross-Attento (Variant 4), and removing the Temporal Aggregation component (Variant 5). Variant 4 adaptively assigns weights to aggregate historical features, resulting in a 0.56% mloU improvement (Variant vs. Variant 3). Variant uses Occlusion Cross-Attention to facilitate interaction between the current feature and the non-occluded areas in the historical frame, enhancing the texture and contextual information of the current frame's 's features, further boosting the mloU by 0.41 % (Variant 5 vs. Variant 3).
Occlusion- Voxel Refinement (OGVR). In Table Variant 6 represents the removal of the OGVR module, while Variant uses convolution fusion to concatenate V2 and Vagg- Even with this simple fusion strategy, a 0.45% mloU improvement is achieved (Variant vs. Variant 6). Variant 8 represents our final full model. Compared to Variant 7, the mask-based refinement strategy further improves the mloU metric. It is worth noting that the OGVR module incurs no additional parameter overhead.

---

Table 7: Ablation study for temporal alignment strategy.
Table 8: Ablation study for different number of temporal
inputs.
Method
loU(S)
mloU(%)
Temporal Inputs
IoU(%)
mloU (%)
Times(s)
Stack
43.98
15.89
t-1
t-3
14
t-5
Foxmorme--T
44.15
13.35
v
44.63
17.74
0.290
HTCL. [S]
45.51
17.13
V
45.01
18.13
0.301
Flow- GuwdGuided [Ours]
45.01
18.13
V
44.72
18,30
0.314
44.66
17.68
0.328
44.53
17.55
0.344
Table Ablation study for optical flow
Table 10: Ablation study for image backbone networks. Acc represents the classification accuracy of each pre- trained model ImageNet [28]
networks.
Method
Iol
mloU((%
Params(M)
PWC-Nat+
31
Method
[oU(%)
mloU(%)
Params(M)
Acc( (b)
RAFT [4]
44.12
17.56
FlowFormer
10
44.33
17.74
18.2
ResNet50 g
44.12
16.98
25.6
79.3
GMPlow
41
45.01
EfficiietNeeB7
33
44.31
17.63
63.8
84.4
RepVit-M2.3 35
45.01
18.13
22.4
83.3
Overall, compared to the baseline, our method achieves significant improvements in both completion and semantic metrics (+2.24% mloU, +1.03% IoU).
Temporal Alignment Strategy and Temporal Inputs. Table presents the results of an ablation study comparing different temporal alignment strategies used for fusing features across frames. Our Flow-Guided strategy delivers the best mloU of 18. 18,1%, showing that optical flow-guided alignment is highly effective for preserving fine-grained semantic consistency, particularly for dynamic scenes. As shown Table g we evaluate the performance of temporal inputs with different numbers of frames. We observe that, the number of frames increases, the time overhead also increases. However, the mloU metric does not grow linearly, as the quality optical flow prediction decreases when the time interval between frames is longer. As result, inputs with 4 or 5 frames (t-4 and t-5) lead to reduced effectiveness. Considering both the experimental metrics and the time overhead, we use 2 frames as the input for our FlowScene method.
presents the performance of different optical flow networks. We compare several ttate-of-the-art methods, including PWC-Net (3T) RAFT (34, and FlowFormer (10) along with our setting, GMFlow [41), which is highlighted in the last row. Our setting achieves the highest IoU of 45.01% and mloU of 18.13% outperforming all other methods in both metrics. These results suggest that GMFlow effectively captures motion cues and integrates them into the semantic scene completion task, providing superior performance over the other optical flow networks tested, with significantly fewer parameters.
Optical Flow Networks.
Image Backbone Networks. Table 10 examines the impact of different backbone networks on the performance of FlowScene. The study compares EfficientNe1B7 [33], ResNet50 [8), and Rep M2.3 [35] (our setting). Our method, using Rep V2t-M..3,, achieves the highest IoU 45.01% %% and mloU of 18. 13%, surpassing both EfficientNeiB7 (44.31% IoU, 17.63% mIoU) and ResNet50 (44. IoU, 16.98% mloU). RepVit- Vit-M2.3, though achieving the best performance, maintains a relatively low parameter count of 22.4M. In comparison, EfficientNetBB has much higher parameter count of 63.8M, while ResNet50 is more parameter-efficient at 25.6M. Rep V2t- offers a good balance between performance and parameter count, making it ideal choice for backbone network. Different image encoders have significant improvements over the baseline, demonstrating the effectiveness of our entire model.
Conclusion
In this paper, we propose a novel temporal SSC method FlowScene. Specifically, we introduce Flow-Guided Temporal Aggregation module that aligns and aggregates temporal features using optical flow, capturing motion- motion-aware aware context and deformable structures. In addition, we design an Occlusion-Guides Voxel Refinement module that injects occlusion masks and temporally aggregated features into 3D voxel space, adaptively refining voxel representations for explicit geometric modeling. Experimental results demonstrate that FlowScene achieves SOTA performance on the ITTa and SSCBench- KITTI-360 benchmarks.
10

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Acknowledgments and Disclosure of Funding
This work was supported in part by the State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle under Grant 72365001, in part by the National Natural Science Foundation of China(Grant Nos. 62225205, 62472162, 62473137), in part by the National Key Researchand Development Program of China 2002222FF33000001, in part by the Major Science and Technology Research Projects of Hunan Province (Grant Nos. 2024QK2010, 2024QK2009).
References
[1] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In ICCV, puges 9297-9307, 2019.
[2] Anh-Quan Cao and Raoul de Charette. Monoscene: Monocular 3d semantic scene completion. In CVPR, 2022.
[3] Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2758-2766, 2015.
[4] Volodymyr Fedynyak, Yaroslav Romanus, Bohdan Hlovatskyi, Bohdan Sydor, Oles Dobosevych, Igor Babin, and Roman Riazantsev. Devos: Flow-guided deformable transformer for video object segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 240-249, January 2024.
[5] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012.
[6] Yuxiao Guo and Xin Tong. View-volume network for semantic scene completion from single depth image. In IJCAL, pages 726-732, 2018.
[7] Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6185-6194, June 2023.
[8] Kaiming He, Xiangyu Zhang, Shaoging Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[9] Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, and Jiwen Lu. Tri-perspective view for vision-based 3d semantic occupancy prediction. In CVPR, pages
2023.
[10] Zhaoyang Huang. Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li. Flowformer: transformer architecture for optical flow. In European conference on computer vision, pages 668-685. Springer, 2022.
[11] Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia. Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 2462-2470, 2017.
[12] Haoyi Jiang, Tianheng Cheng. Naiyu Gao, Haoyang Zhang, Tianwei Lin, Wenyu Liu, and Xinggang Wang. Symphonize 3d semantic scene completion with contextual instance queries. In CVPR, pages 20258-20266 2024.
[13] Bohan Li, Jiajun Deng, Wenyao Zhang, Zhujin Liang, Dalong Du Xin Jin, and Wenjun Zeng. Hierarchical temporal context learning for camera-based semantic scene completion. In European Conference on Computer Vision, pages 131 131-148. Springer, 2024.
[14] Bohan Li, Yasheng Sun, Xin Jin, Wenjun Zeng. Zheng Zhu, Xiaoefeng Wang, Yunpeng Zhang, James Okae, Hang Xiao, and Dalong Du. Stereoscene: Bev-assisted stereo matching empowers 3d semantic scene completion. arXiv preprint arXiv:2303.13950 2023.
11

---

[15] Jie Li, Kai Han, Peng Wang, Yu Liu, and Xia Yuan. Anisotropic convolutional networks for 3d semantic scene completion. In CVPR. pages 3351-3359, 2020.
[16] Yiming Li, Sihang Li, Xinhao Liu, Moonjun Gong, Kenan Li, Nuo Chen, Zijun Wang. Zhiheng Li, Tao Jiang, Fisher Yu, Yue Wang, Hang Zhao, Zhiding Yu, and Chen Feng. Sscbench: Monoc- ular 3d semantic scene completion benchmark in street views. arXiv preprint arXiv:2306.0900) 2023.
[17] Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. Voxformer: Sparse voxel transformer for camera- based 3d semantic scene completion. In CVPR, 2023.
[18] Yinhao Li, Zheng Ge. Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. In AAAL, volume 37, pages 1477-1485, 2023.
[19] Yiyi Liao, Jun Xie, and Andreas Geiger. KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. TPAMI, 2022.
[20] Tsung-Yi Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In CVPR, pages 2117-2125, 2017.
[21] Chunjie Luo, Jianfeng Zhan, Xiaohe Xue, Lei Wang, Rui Ren, and Qiang Yang. Cosine normal- ization:
Using cosine similarity instead of dot product in neural networks. In Artificial Neural Networks and Machine -nriing-CCANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings. Part I 27, pages 382-391. Springer, 2018
[22] Jianbiao Mei, Yu Yang, Mengmeng Wang, Junyu Zhu, Jongwon Ra, Yukai Ma, Laijian Li, and Yong Liu. Camera- based 3d semantic scene completion with sparse guidance network. IEEE Transactions on Image Processing, 2024.
[23] Simon Meister, Junhwa Hur, and Stefan Roth. Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
[24] Wenzhe Ouyang, Xiaolin Song, Bailan Feng, and Zenglin Xu. Octoce: 3d occupancy prediction with octree. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 4369 4377, 2024.
High-resolution
[25] Jonah Philion and Sanja Fidler. Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In ECCV, pages 194-210. Springer, 2020.
[26] Christoph B Rist, David Emmerichs, Markus Enzweiler, and Dariu M Gavrila. Semantic scene completion using local deep implicit functions on lidar data. TPAMI, 44(10):2055-2118, 2021.
[27] Luis Roldao, Raoul de Charette, and Anne Verroust- Lmsenet: Lightweight multiscale 3d semantic completion. In 3DV, pages 111-119, IEEE, 2020.
[28] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma. Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211-1222 1-252, 2015.
[29] Shuran Song, Fisher Yu, Andy Zeng. Angel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic scene completion from single depth image. In CVPR, pages 1746-1754. 2017.
[30] Mohammadreza Alipour Sormoli, Mehrdad Dianati, Sajjad Mozaffari, and Roger Woodman. Optical flow based detection and tracking of moving objects for autonomous vehicles. IEFE Transactions on Intelligent Transportation Systems, 2024.
[31] Deqing Sun, Xiaodong Yang, Ming- Yu Liu, and Jan Kautz. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8934-8943, 2018.
12

---

[32] Narayanan Sundaram, Thomas Brox, and Kurt Keutzer. Dense point trajectories by gpu- accelerated large displacement optical flow. In European conference on computer vision, pages 438 451. Springer, 2010.
[33] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, pages 1105-6114 14. PMLR, 2019.
[34] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16, pages 402-419. Springer, 2020.
[35] Ao Wang, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding. Repvit: Revisiting mobile enn from vit perspective, 2023.
[36] Meng Wang, Yan Ding, Yumeng Liu, Yunchuan Qin, Ruihui Li, and Zhuo Tang. Mixsse: Forward-backwe mixture for vision-based 3d semantic scene completion. IEEE Transactions on Circuits and Systems for Video Technology, 2025.
[37] Meng Wang. Huilong Pi, Ruihui Li, Yunchuan Qin, Zhuo Tang, and Kenli Li. Vlscene: Vision- language guidance distillation for camera- based 3d semantic scene completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 7808-7816, 2025.
[38] Song Wang, Jiawei Yu, Wentong Li, Wenyu Liu, Xiaolu Liu, Junbo Chen, and Jianke Zhu. Not all voxels are equal: Hardness-aware semantic scene completion with self-distillation. In CVPR. pages 14792-14801, 2024.
[39] Yu Wang and Chao Tong. H2gformer: corizontallto-global voxel transformer for 3d se- mantic scene completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 5722-5730, 2024.
[40] Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, and Jiwen Lu. Surroundoce: Multi-camera 3d occupancy prediction for autonomous driving. In ICCV, pages 21729-21744 2023.
[41] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8121-8130, 2022.
[42] Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui. Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion. In AAAI, volume 35, pages 3101-3109, 2021.
[43] Jiawei Yao, Chuming Li, Keqiang Sun, Yingiie Cai, Hao Li, Wanli Ouyang, and Hongsheng Li. Ndc-scene: Boost monocular 3d semantic scene completion in normalized device coordinates space. In ICCV, pages 9455-9465, 2023.
[44] Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si- Yuan Cao, and Hui-Liang Shen. Context and geometry aware voxel transformer for semantic scene completion. In Advances in Neural Information Processing Systems, 2024.
[45] Linfeng Yuan, Miaojing Shi, Zijie Yue, and Qijun Chen. Losh: Long-short text joint prediction network for referring video object segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14001-14010, June 2024.
[46] Jiahui Zhang, Hao Zhao, Anbang Yao, Yurong Chen, Li Zhang, and Hongen Liao. Efficient semantic scene completion network with spatial group convolution. In ECCV, pages 733-749, 2018.
[47] Yunpeng Zhang, Zheng Zhu, and Dalong Du Oceformer: Dual-path transformer for vision- based 3d semantic occupancy prediction. arXiv preprint MiKiv:2304.05316 2023.
[48] Yupeng Zheng, Xiang Li, Pengfei Li, Yuhang Zheng, Bu Jin, Chengliang Zhong, Xiaoxiao Long, Hao Zhao, and Qichao Zhang. Monooce: Digging into monocular semantic occupancy prediction. arXiv preprint arXiv:2403.0887 2024.
13

---

[49] Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, and Yichen Wei. Flow-guided feature aggregation for video object detection. In Proceedings of the IEEE international conference on computer vision, pages 408 417, 2017.
[50] Zheng Zhu, Wei Wu, Wei Zou, and Junjie Yan. End-to-end flow correlation tracking with -patial-tempora attention. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 548-557, 2018.
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Technical Appendices and Supplementary Material
This technical appendices consists of the following sections:
In Section A
we provide information of datasets, metrics and detailed implementation
details.
In Section
B
provide experiments results and analysis.
In Section we analyze the limitations of our approach, directions for future work, and the broader impacts of this work.
We also include video in the supplementary material.
A Experimental Setup
The SemanticK[TTi(0] dataset includes dense semantic scene completion annotations and lahels voxelized scene with 20 semantic classes. It consists of 10 training sequences, validation sequence, and testing sequences. RGB images are resized to 1280 x 384 for input processing. The BTBBench-KITTI-3601166 [9] dataset contains 7 training sequences, validation sequence, and I testing sequence, covering 19 semantic classes in total. The RGB images are resized to 1408 x 384 for input processing.
Datasets.
Metrics. We use intersection over union (loU) to evaluate the scene completion performance. To assess the effectiveness of our 3D Semantic Scene Completion method, we focus on the mean IoU (mloU). A higher IoU value reflects accurate geometric predictions, while higher mIoU value indicates more precise semantic segmentation.
Training Details. We use RepVit
[35 and FPN 20 to extract features for all images. The number of historical temporal frames is set to 2. We use and freeze the GMFlow [1] optical flow estimation model to obtain optical flow information. We use the LSS paradigm for 2D-3D projection. The neighborhood wross-attention range is
and the number of attention heads is set 8. Finally, the final outputs of SemantiKITTI is 20 classes, and SSCBench-KKIT have the scene size 51. 1m x 51.2m x 64m with the voxel grid size of 256 256 x 32. By default, the model is trained for 25 epochs. We optimise the process, utilizing the Adam W optimizer with an initial learning rate of le-4 and a weight decay of 0.01. We also employ multi-step scheduler to reduce the learning rate. All models are trained two A A100 Nvidia GPUs with 80G memory and batch size 4.
is 19 classes. All datasets
Implementation of Flow Consistency Check.
Variable Definition:
Forward Flow: (Flow
Maps pixels from the current frame ( (1) to the previous frame (4-1 )
For a pixel (x, It), the corresponding location in (I_t 1) is:
It-1 24 Floww
Backward Flow: (Flow--1-1t,
Maps pixels from the previous frame (1+-1) to the current frame ( (4, For a pixel 2,-1 E eltt- 1), the corresponding location in [ is: x + Flow Flowl-1--t
x/
2. Consistency Check
The forward-backward consistency check verifies whether pixel mapping is valid by ensuring round-trip correspondence.
Round-trip Mapping
15

---

A pixel in (1) is mapped to It-1) using forward flow, and then mapped back using backward flow: oy xy + Flow (xx) + Flow"-1-t (2+ ((-tt-1(x.)))
Define the consistency residual as:
( Floww>-l- + Flow'-1+1 + 1(-+)1(
If the norm (z+))))) is small (below a threshold), the mapping is considered consistent.
3. Occlusion Mask
Pixels with high inconsistency are typically considered occluded or unreliable. Occlusion Mask (M(x))
1
if >
(occluded)
(x)
otherwise
(non-occluded)
Where (7) is a predefined threshold.
Implementation FGTA and OGVR module. Algorithms and deecriie details of the two key components proposed in this work: Flow-Guided Temporal Aggregation (FGTA) and Occlusion-Guide Voxel Refinement (OGVR).
describe the implementation
AlgorithmD I outlines the inference procedure for FGTA, Given the current and historical image frames [T- and their corresponding features {Ft-s) the algorithm first estimates forward and backward optical flows between the current frame I and each historical frame I Each historical feature map F,-; is warped to the reference frame using flow-guided warping. Cosine similarity between the warped features and the current frame feature F; is computed to assign adaptive weights, which emphasize temporally consistent and visually similar regions. Simultaneously, an occlusion mask is constructed through bidirectional flow consistency check to identify unreliable regions. The weighted historical features are aggregated into Fagg. and a Neighborhood (NCA) operation is applied to further refine F;, focusing only non-occluded regions. This process enhances temporal consistency and robustness to motion and occlusion artifacts.
Algorithm 2 describes the inference procedure for the OGVR module. The goal is to refine the voxel features 3D space by integrating information from the aggregated feature volume Vagg- the current frame's voxel features V,, and the occlusion mask volume Vmask- Non-occluded regions are updated using features from Vagg. while occluded regions are filled using the current frame's 's voxel features V,, which are more reliable in such areas. A per-voxel weight map is maintained to track the number of valid sources contributing to each voxel. The final voxel feature representation Viineis obtained by normalizing the fused features with the accumulated weights, ensuring smooth transitions the boundaries between occluded and non-occludee non-occluded regions. Importantly, this refinement process is lightweight and introduces no additional parameter overhead.
Together, these modules enable FlowScene to effectively align temporal information and reason across occlusions in both 2D and 3D spaces, leading to improved semantic and geometric scene understanding in dynamic environments.
B
More Results
B.1
Reproduce SOTA Method using Different Image Encoder
To ensure a fair and consistent comparison across methods, we re-implemented several state-of- the-art Semantic Scene Completion models using unified experimental setup. Specifically, we replaced the original image encoders used in existing methods with a lightweight and efficient Backbone-RepVii
35 while keeping all other architectural and training settings unchanged.
As shown in Table II the results demonstrate that Rep significantly reduces the number of parameters across all models, often without degrading performance and in some cases, even improving it. This confirms the generalizability and efficiency of RepViT an image encoder for SSC tasks.
16

---

Algorithm Inference algorithm of flow-guided temporal alignment
1: Inputs: Images [--i)==0- Features (Fr-i)eo
2: M = Zeros
>> init occlusion mask
3: for i 1 1oto010 N do
4: Plow"-t-i F'ow --itt + F(11,11
compute dual optical flow D flow-guided warp > compute similarity weight D get the features corresponding to the weights b compute occlusion mask
5: Fwarp
Warp(Fi-1 Flow i)
6: W - Similarity(Fh.r)
Fi)
7: Fago [i] + W F warp 8: M <- CC((Foow ) UM
9: end for
10: Fagg + SSUM(Fagg))
11: Fi NCA(F, (1 M) Fworp)
12: Outputs: Faggs Fe; M
Algorithm 2 Inference algorithm occlusion- voxel refinement
1: Inputs: Aggregated voxel feature Vagg current voxel feature V,, occlusion mask Viaak
2: Initialize weight + 0 with shape Ve
Initialize Viine f with shape as Vi
>> fuse non-occluded regions
4: Vyine Viine Vagg (1- Vinas))
5: weight + weight + Vmakk)
fuse occluded regions
6: Vjine f- Vine + V/
7: weight + weight
> normalize result
8: weight +- max((eight)) x 10-6)
9: Viine Vfine/ weight
10: Outputs: Refined voxel feature Vyine
B.2 Standard Errors and Standard Deviations Results
We conducted a statistical analysis on the SemanticKITTI validation set and report the weighted mean IoU (W-mloU), weighted standard deviation (W-SD), and weighted standard error (W-SE) across semantic categories. As shown, our method not only achieves the highest mloU and W-mloU, but also demonstrates W-SD and W-SE compared other strong baselines. This indicates that our performance improvements are statistically meaningful and stable across classes.
B.3 More Quantitative Results
To provide more thorough comparison, provide additional quantitative results of semantic scene completion on the SemanticKITTI validation set Table [3 The results further demonstrate the effectiveness of our approach in enhancing 3D scene perception performance. Compared with the previous state-of-the-ae methods, FlowScene is superior to other HTCL (13) in semantic scene understanding, with 1. 1.00% increase in mloU. In addition, compared with Symphonize 12, huge improvements are made in both occupancy and semantics. IoU and mloU enhancement are of great significance for practical applications. It proves that we are not simply reducing certain metric to achieve semantic scene completion.
B.4 Failure Case
We provide two failure cases in Figure
B.5 More Visualizations Results
We show visualization examples on the Semantickitti validation set, as shown in Figure 9 From left to right are the input image, the corresponding optical flow and occlusion mask, the front view
17

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Table 11: Reproduce SOTA method using different image encoder.
Method
Backbone
mloU(%)
Params(M)
EfficientNeeB7
12.96
132.4
MonoScene
RepViT
12.59
91.0
ResNer50
13.35
57.9
VoxFormer
RepViT
13.62
54.7
FfficientNeeB7
15.43
161.4
BRGScene 14
RepViT
16.13
120.0
HTCL EfficienNNeB87
17.13
181.4
RepViT
16.86
140.0
EfficientNeBB7
17.44
88.8
VL.ccene 37
RepViT
17.83
47.4
EfficientNetB7
17.63
93.8
Ours
ResNet50
16.98
55.6
RepViT
18.13
52.4
Table 12: Standard Errors and Standard Deviations Results
Method
mIoU("))
W-mloU(1)
W-SD(()
W-SE(L)
Ours
17.70
33.11
13.80
6.50
HTCL (BCCV'2024)
17.08
32.64
14.17
6.67
CGFormer (N2P5)2024)
16.63
31.91
14.43
6.80
BRGSeene (UCCA1202)
15.35
30,22
14.01
6.60
TPVFormer (ICCV'2023)
12.32
24.44
14.34
6,75
MonoScene (CV2))2022)
11.08
22.58
14.38
6.77
SSC, and the top view SSC. Due the motion information brought by the optical flow, the location information of the scene objects is more accurate and the layout is more reasonable. We report the performance of more visual comparison results on the SemanticKITTI validation set Figure TO] method performs more fine-grained segmentation the scene and maintains clear segmentation boundaries. For example, in the segmentation completion result of cars, we predict clear separation of each car. In contrast, other methods show continuous semantic errors for occluded cars. In addition, our flow can effectively deal with the problem of mutual occlusion between different objects. Finally, provide video in the appendix to show the performance more intuitively.
We compare with VoxFormer [] and BRGScene [14]. In general,
C Discussions
C.1 Limitations
Flowscene shows strong performance on the benchmark with an improved number of parameters. This is beneficial for deploying real-world autonomous driving applications. But the inference time of the model needs to be improved. While optical flow is effective, it depends on pretrained flow model, potentially limiting performance in degraded visual conditions.
C.2 Future Works
Semantic scene completion in multi-camera settings is also worth attention, which our future work. Meanwhile, the legal challenges of autonomous driving as well as privacy and data security risks are still topics of debate. Finally, the robustness of semantic scene completion is also an issue worth exploring.
C.3 Broader Impacts
FlowScene enhances 3D geometry perception by aligning temporal features through optical flow information, thereby improving the ability of temporal semantic scene completion. This work has non-obvious negative social impact.
18

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Table 13: Quantitative results the SemanticKITTI validation set. The best and the second best results are in bold and underlined, respectively.
othervebicck 10 (0.20%
1000 115 1153
1220)
gred (0.50%)
buitding (14 (000
trudk (0.160))
bieyede (C.U5%)
mostey de (0:0550
wygetaation (9% 330
tronn 30.31
TOr 09 1993
(3922)
Methods
Published
Inputs
IoU
mlol
MonsSem:
H
CVPR 2022
5
35.86
56.52
36.72
14.27
0.46
14.19
23.26
698
0.61
0.45
1.45
17.89
2.81
29.64
186
1.20
0.00
5.84
4.14
2.25
11.05
23.81
OzdFormer 9
ICCV 2023
8
35.50
58.85
26.88
19.61
0.31
14.40
25.09
25.53
0.1
1.19
8.52
19.63
3.90
32.62
2.76
282
0.00
5.61
6.26
2.56
13.46
Symphenize
CVPR
VotRormesTT
CVPR22025
44.15
53.57
26.52
19.60
0.42
19.54
26.54
7.26
0.56
7.81
26
6.30
33.06
193
197
0.00
7.31
9.15
4.94
13.35
H2GFomme
AAAU22044
HASSC
CVP4
64.58
55.30
29.50
25.90
11.30
23.10
23.00
2.90
190
4,90
24.80
9.80
26.50
1.40
3,00
0.00
14.30
7.00
7.10
14.74
HTCL
h
ECCV 2004
45.51
63,70
32.45
23.27
0.14
24.13
34,30
20.72
3,99
11.99
26
96
8.79
37.73
2.56
2.70
0.00
11.22
11.49
6.95
17.15
32,10
1:31
33.33
36.73
7.05
Image
Ground Truth
FlowScene
Figure 8: Failure cases.
Input Images
Flow&Mask
Front View
Top View
Figure 9: Qualitative results on the SemanticKITTI validation set.
19

---

Voxformer-T T
BRGScene
Ours
Ground Truth
Figure 10: Qualitative results on the SemanticKITTI validation set.
20

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27


Reviews:
[{"review_id": "2GGdOKYqBB", "reviewer": "Reviewer_r9BF", "summary": "In this work, the authors propose a 3D Semantic Scene Completion (SSC) method tailored for autonomous driving perception tasks. Unlike existing approaches that fuse temporal information through simple stacking or attention mechanisms, the proposed method leverages estimated optical flow to align features across frames more effectively. Specifically, the authors introduce Flow-Guided Temporal Aggregation (FGTA) to aggregate historical features in the 2D space, and Occlusion-Guided Voxel Refinement (OGVR) to refine features in the 3D voxel space. Experimental results on the SemanticKITTI and SSCBench-KITTI-360 benchmarks demonstrate that the proposed method achieves state-of-the-art performance.", "questions": "I have a few questions about the details:\n\n1. In the OGVR, it seems that the 2D features are projected to 3D space through the depth bins. But I do not quite get the definition or acquirement of the depth bins. Are they acquired through other depth estimation models? Or just frustum?\n\n2. During the updating of $F_t$ in Eq.5, only the non-occluded information in historical frames is introduced for fusion. Won't the other regions help the completion? Besides, I'd also appreciate it if the authors can provide more details about the flow consistency check used in this work.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The motivation for introducing optical flow to guide temporal feature fusion is well-justified and conceptually sound.\n\n2. The writing is clear, and the diagrams are well-designed and easy to follow, which helps in understanding the methodology.\n\n3. Experimental results on benchmark datasets convincingly demonstrate the proposed method's superiority over existing approaches.\n\nWeaknesses:\n\n1. While the integration of optical flow into the framework is reasonable, it inherently depends on the quality of flow estimation from pre-trained networks. As shown in Table 7, aggregating more historical frames will instead degrade the performance, likely due to inaccuracies in flow estimation. This outcome contradicts intuitive expectations. It may be beneficial to explore joint optimization of the flow estimation and the SSC framework to mitigate this limitation.\n\n2. The additional computational cost introduced by flow estimation may result in slower inference compared to other methods. However, this trade-off might be acceptable depending on the application context.", "was_revised": true, "final_justification": "The rebuttal has addressed my concerns. I also hold my opinion that this work introduces a reasonable and valuable framework. Therefore, I will keep my rating.", "rebuttal": "*We sincerely thank the reviewer for the valuable feedback and insightful suggestions. Below, we address the identified weaknesses and questions in detail.*\n\n\n\n------\n\n> **[Weakness 1] While the integration of optical flow into the framework is reasonable, it inherently depends on the quality of flow estimation from pre-trained networks. As shown in Table 7, aggregating more historical frames will instead degrade the performance, likely due to inaccuracies in flow estimation. This outcome contradicts intuitive expectations. It may be beneficial to explore joint optimization of the flow estimation and the SSC framework to mitigate this limitation.**\n\n**[Answer]** *We agree that the performance gain of our approach depends to some extent on the quality of the flow estimates. As observed in Table 7, incorporating more than two historical frames can introduce temporal drift and noise due to increased difficulty in long-range flow estimation. To address this, we empirically determine that using **two historical frames** strikes the best balance between **inference times and flow accuracy**. Furthermore, our **flow network (GMFlow)** is lightweight yet strong **(Table 8)** .*\n\n*Besides, we have designed our system to be **robust to moderate flow errors** by incorporating:*\n\n1. ***Cosine-Similarity Weights Selection**, in 2D features, we use the cosine similarity between the current frame feature $F_t$ and the warped historical frame feature $F_{warp}$ to design the weight. When the warp historical frame feature $F_{warp}$ is close to the current feature $F_t$, a larger weight is assigned, otherwise a smaller weight (i.e. when **the optical flow error is large**) is assigned **(see Sec. 3.4).***\n\n2.  ***Consistency Check,** we use the forward and backward consistency check of the optical flow  to query unreliable areas and assign lower weights in 3D space **(see Sec. 3.3 and 3.5).***\n\n\n\n------\n\n> **[Weakness 2] The additional computational cost introduced by flow estimation may result in slower inference compared to other methods. However, this trade-off might be acceptable depending on the application context.**\n\n**[Answer]** *While flow estimation introduces overhead, our framework remains efficient. As shown in Table 4, our method achieves a competitive inference time of **0.301s**, outperforming or matching many temporal methods such as OccFormer (0.338s), HTCL (0.297s), and Symphonize (0.319s). This is due to our use of **lightweight GMFlow** and the **RepViT encoder**, which also reduce model size (see Tables 8, 9 and 10). We believe this trade-off is acceptable, especially given the significant performance gains in dynamic object modeling (Table 11).*\n\n\n\n------\n\n> **[Question 1] In the OGVR, it seems that the 2D features are projected to 3D space through the depth bins. But I do not quite get the definition or acquirement of the depth bins. Are they acquired through other depth estimation models? Or just frustum?**\n\n**[Answer]** *The **depth bins** used in the OGVR module follow the **Lift-Splat-Shoot (LSS, ECCV'2020)** paradigm. We do not rely on an explicit depth estimation model. Instead, we use **discretized frustum-based depth bins**, where the depth distribution is implicitly learned using a soft depth volume and supervised via LIDAR projected depths.*\n\n\n\n------\n\n> **[Question 2] During the updating of $F_t$ in Eq.5, only the non-occluded information in historical frames is introduced for fusion. Won't the other regions help the completion? Besides, I'd also appreciate it if the authors can provide more details about the flow consistency check used in this work.**\n\n**[Answer]** \n\n1. *We selectively use **non-occluded features** from historical frames in Eq. (5) to prevent injecting unreliable or distorted information caused by occlusions or inaccurate flow. Including occluded regions can introduce noise and harm completion quality. Our ablation (Table 5, Variant 5 vs. 4) confirms that this selective strategy improves mIoU.*\n2. *For **occlusion detection**, we use a **forward-backward flow consistency check** (Section 3.3, Figure 4). A pixel is marked as occluded if the discrepancy between its forward and backward flow exceeds a threshold. The resulting mask is used both in the FGTA module and for guiding voxel refinement in 3D space.*\n\n*Besides, we will add details about the consistency check in the Technical Appendix of the paper.*\n\n**Following are the details about flow consistency check:**\n\n### 1. Variable Definition: \n\n**Forward Flow**: \\( $Flow^{t \\rightarrow t-1}$ \\)\n\n- Maps pixels from the current frame \\( $I_t $\\) to the previous frame \\( $I_{t-1}$ \\). \n\n- For a pixel \\(${x}_t \\in I_t $\\), the corresponding location in \\($I\\_{t-1}$\\)  is:\n\n  $${x}_{t-1} = {x}_t + Flow^{t \\rightarrow t-1}({x}_t)$$\n\n**Backward Flow**: \\($ Flow^{t-1 \\rightarrow t}$ \\)\n\n- Maps pixels from the previous frame \\($ I_{t-1} $\\) to the current frame \\( $I_t$ \\).\n\n- For a pixel \\( ${x}_{t-1} \\in I\\_{t-1} $\\), the corresponding location in \\( $I_t$ \\) is:\n\n  $${x}_t' = {x}\\_{t-1} + Flow^{t-1 \\rightarrow t}({x}\\_{t-1})$$\n\n### 2. Consistency Check\n\nThe **forward-backward consistency check** verifies whether a pixel mapping is valid by ensuring round-trip correspondence.\n\n**Round-trip Mapping**\n\nA pixel in \\( $I_t $\\) is mapped to \\( $I_{t-1} $\\) using forward flow, and then mapped back using backward flow:\n\n$${x}_t'' = {x}_t + Flow^{t \\rightarrow t-1}({x}_t) + Flow^{t-1 \\rightarrow t}({x}_t + F^{t \\rightarrow t-1}({x}_t))$$\n\nDefine the **consistency residual** as:\n$$\\Delta({x}_t) = Flow^{t \\rightarrow t-1}({x}_t) + Flow^{t-1 \\rightarrow t}({x}_t + F^{t \\rightarrow t-1}({x}_t))$$\n\nIf the norm \\( $\\|\\Delta({x}_t)\\| $\\) is small (below a threshold), the mapping is considered consistent.\n\n### 3. Occlusion Mask\n\nPixels with high inconsistency are typically considered **occluded or unreliable**.\n\n**Occlusion Mask \\($ M({x}) $\\):**\n$$\nM({x}) = \n\\begin{cases}\n1 & \\text{if } \\|\\Delta({x})\\| > \\tau \\quad \\text{(occluded)} \\\\\\\\\n0 & \\text{otherwise} \\quad \\text{(non-occluded)}\n\\end{cases}\n$$\n\nWhere \\($ \\tau $\\) is a predefined threshold.\n\n\n------\n\n*We thank the reviewer again and hope our clarifications address the concerns.*"}, {"review_id": "DvOjpRKuhH", "reviewer": "Reviewer_RB8s", "summary": "This paper introduces FlowScene, a novel method for temporal 3D Semantic Scene Completion (SSC) that leverages optical flow to improve performance. The authors argue that previous temporal SSC methods, which often just stack features from multiple frames, fail to capture crucial motion dynamics and temporal consistency. To address this, FlowScene incorporates two key modules: a Flow-Guided Temporal Aggregation (FGTA) module that uses optical flow to warp and intelligently aggregate features from past frames, and an Occlusion-Guided Voxel Refinement (OGVR) module that uses occlusion masks (derived from flow consistency checks) to refine the 3D voxel representation. This approach explicitly models motion and handles occlusions, leading to more accurate geometric and semantic predictions. The method achieves state-of-the-art results on the SemanticKITTI and SSCBench-KITTI-360 benchmarks, demonstrating significant improvements, especially in the completion of dynamic objects.", "questions": "1. In the Flow-Guided Temporal Aggregation (FGTA) module, you use cosine similarity between the warped historical features (F _warp) and the current features (F_t) to generate weights for aggregation. Could you provide more intuition on why this is effective? Does this implicitly down-weight regions where the flow is inaccurate (leading to poor warping and low similarity) or regions that have changed in appearance for reasons other than motion?\n\n2. In Table 6, you compare your \"Flow-Guided\" alignment against a \"Stack\" baseline. How exactly is this \"Stack\" baseline implemented? Does it simply concatenate the features from the current and two historical frames along the channel dimension before the view transformation?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. The core concept of using optical flow to guide temporal feature aggregation and explicitly handle occlusions in 3D SSC is a strong and intuitive contribution. It directly addresses a clear limitation of prior work—the naive handling of temporal information and scene dynamics.\n\n2.  Despite the added complexity of optical flow estimation, the proposed method is shown to be efficient. It achieves its state-of-the-art results with fewer parameters than many competing methods and maintains a comparable inference time. The use of only two historical frames also makes it practical.\n\n3. The paper is well-written and easy to follow. The figures (especially Figures 2 and 3) effectively illustrate the core differences between FlowScene and previous methods, and the qualitative results in Figures 6 and 10 clearly showcase the method's superiority in handling occlusions and dynamic objects.\n\nWeakness\n\n1. The method's performance is inherently tied to the quality of the pre-trained optical flow network. The authors acknowledge this as a limitation, noting that performance could be limited in visually degraded conditions. While the ablation study shows robustness to different flow networks, a failure of the flow estimator would likely cause a catastrophic failure in the SSC prediction.\n\n2. The paper does not report error bars or standard deviations for its results, citing computational expense. This makes it difficult to assess the statistical significance of the reported improvements over other methods.", "was_revised": true, "final_justification": "Thanks the authors for the responses. It well resolves my problems. I will keep my rating.", "rebuttal": "*We thank the reviewer for the thoughtful comments and valuable feedback. We address each concern below.*\n\n\n\n------\n\n>  **[Weakness 1] The method's performance is inherently tied to the quality of the pre-trained optical flow network. The authors acknowledge this as a limitation, noting that performance could be limited in visually degraded conditions. While the ablation study shows robustness to different flow networks, a failure of the flow estimator would likely cause a catastrophic failure in the SSC prediction.**\n\n**[Answer]** *We acknowledge that the performance of our method is influenced by the accuracy of the optical flow estimator. However, we have designed our system to be **robust to moderate flow errors** by incorporating:*\n\n1. ***Cosine-Similarity Weights Selection**, in 2D features, we use the cosine similarity between the current frame feature and the warped historical frame feature to design the weight. When the warp historical frame feature $F_{warp}$ is close to the current feature $F_t$, a larger weight is assigned, otherwise a smaller weight (i.e. when **the optical flow error is large**) is assigned **(see Sec. 3.4).***\n\n2.  ***Consistency Check,** we use the forward and backward consistency check of the optical flow  to query unreliable areas and assign lower weights in 3D space **(see Sec. 3.3 and 3.5).***\n\n*Furthermore, our **flow network (GMFlow)** is lightweight yet strong **(Table 8)** . In future work, we will try to jointly optimize the optical flow and SSC tasks to alleviate the reliance on pre-trained optical flow models.*\n\n\n\n------\n\n>  **[Weakness 2] The paper does not report error bars or standard deviations for its results, citing computational expense. This makes it difficult to assess the statistical significance of the reported improvements over other methods.**\n\n**[Answer]** *We appreciate the reviewer’s concern. We followed prior works such as MonoScene, TPVFormer, BRGScene, CGFormer and HTCL in reporting **deterministic evaluations** on fixed validation/test splits.*\n\n*Thank you for raising this important point. Following your suggestion, we conducted a statistical analysis on the SemanticKITTI validation set and report the **weighted mean IoU (W-mIoU), weighted standard deviation (W-SD), and weighted standard error (W-SE)** across semantic categories.*\n\n| Method                | mIoU(⬆️)   | W-mIoU(⬆️) | W-SD(⬇️)   | W-SE(⬇️)  |\n| :-------------------- | :-------- | :-------- | :-------- | :------- |\n| **Ours**              | **17.70** | **33.11** | **13.80** | **6.50** |\n| CGFormer (NIPS'2024)  | 16.63     | 31.91     | 14.43     | 6.80     |\n| HTCL (ECCV'2024)      | 17.08     | 32.64     | 14.17     | 6.67     |\n| BRGScene (IJCAI'2024) | 15.35     | 30.22     | 14.01     | 6.60     |\n| TPVFormer (ICCV'2023) | 12.32     | 24.44     | 14.34     | 6.75     |\n| MonoScene (CVPR'2022) | 11.08     | 22.58     | 14.38     | 6.77     |\n\n*As shown, our method not only achieves the **highest mIoU and W-mIoU**, but also demonstrates **W-SD** and **W-SE** compared to other strong baselines. This indicates that our performance improvements are **statistically meaningful and stable across classes**.*\n\n*We appreciate your suggestion to quantify this, and will include this analysis in the paper to better support our claims.*\n\n\n\n------\n\n>  **[Question 1] In the Flow-Guided Temporal Aggregation (FGTA) module, you use cosine similarity between the warped historical features ($F _{warp}$) and the current features ($F_t$) to generate weights for aggregation. Could you provide more intuition on why this is effective? Does this implicitly down-weight regions where the flow is inaccurate (leading to poor warping and low similarity) or regions that have changed in appearance for reasons other than motion?**\n\n**[Answer]** *Yes, the use of **cosine similarity** between the warped historical features ($F_{warp}$) and the current features ($F_t$) acts as an implicit confidence estimator for flow-guided alignment. When the flow is accurate, $F_{warp}$≈$F_t$ and their similarity is high, resulting in **larger weights** for reliable regions. In contrast, misaligned or occluded areas typically produce low similarity, leading to **automatic down-weighting** during aggregation. This not only filters out flow errors but also adapts to **appearance changes** due to motion blur, lighting, or deformation. Our ablation (Table 5) confirms that this improves mIoU by **1.05%** over equal-weight aggregation (Variant 8 vs. 5).* \n\n------\n\n>  **[Question 2] In Table 6, you compare your \"Flow-Guided\" alignment against a \"Stack\" baseline. How exactly is this \"Stack\" baseline implemented? Does it simply concatenate the features from the current and two historical frames along the channel dimension before the view transformation?**\n\nThe **\"Stack\"** baseline is implemented by **concatenating the image features** from the current frame and two historical frames **along the channel dimension** prior to the view transformation step. This approach does not include any alignment or motion modeling—it treats multi-frame features as spatially aligned, which introduces artifacts when objects move. This baseline represents a **naïve temporal fusion** method and serves as a lower bound in our comparison. In contrast, our flow-guided alignment explicitly handles motion, achieving a **+2.24%** mIoU improvement over Stack (Table 6).\n\n------\n\n*We thank the reviewer again and hope our clarifications address the concerns.*"}, {"review_id": "jRnVwDQi2M", "reviewer": "Reviewer_n69C", "summary": "This paper presents FlowScene, a novel framework for temporal 3D Semantic Scene Completion that explicitly uses optical flow to guide spatio-temporal dependencies. Specifically, the Flow-Guided Temporal Aggregation module is designed to align features from past frames using motion cues, while the Occlusion-Guided Voxel Refinement module intelligently combines current and historical information using occlusion masks. Experiments on SemanticKITTI and SSCBench-KITTI-360 benchmarks demonstrate the effectiveness of the proposed modules.", "questions": "It would be better to provide more evidence to address my concerns about the role of occlusion masks in refining voxel features and the limited improvement on the KITTI360 test set. I would raise the evaluation score when these concerns are solved.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n- Utilizing optical flow as explicit guidance for temporal feature aggregation provides more fine-grained motion and contextual cues.\n- The introduction of occlusion masks in 3D voxel refinement enhances representation quality in occluded regions.\n- Extensive experimental validation with detailed ablation studies demonstrates the effectiveness of each component.\n\n**Weaknesses**:\n- The main concern is about the role of occlusion masks in refining voxel features. According to the paper's description, occlusion masks are primarily used to enhance the fusion of features from non-occluded regions in historical frames, while using current frame features for occluded parts. However, for constructing 3D voxels of the current frame, it's more reasonable and helpful to obtain 3D features about the occluded parts of the current frame from historical frames to achieve a more complete 3D representation. Conversely, when performing 2D feature fusion, it's better to consider the interference of occluded features, and for the current frame, it will make sense to fuse non-occluded 2D features.\n- It would be better to provide more explanation on the limited improvement on the KITTI360 test set compared to the SOTA.\n- It would be better to unify the notation of occlusion masks in Figure 3 and Figure 4 for better clarification.\n- Based on the supplementary video materials, the occlusion masks generated from optical flow appear to have significant errors. It would be beneficial to further elaborate on the role and significance of generating occlusion masks through optical flow.", "was_revised": true, "final_justification": "The authors addressed my primary concerns. Considering the feedback from other reviewers, I am inclined to raise my score to borderline accept.", "rebuttal": "*We sincerely thank the reviewer for the valuable feedback and insightful suggestions. Below, we address the identified weaknesses and questions in detail.*\n\n\n\n------\n\n> **[Weakness 1] The main concern is about the role of occlusion masks in refining voxel features. According to the paper's description, occlusion masks are primarily used to enhance the fusion of features from non-occluded regions in historical frames, while using current frame features for occluded parts. However, for constructing 3D voxels of the current frame, it's more reasonable and helpful to obtain 3D features about the occluded parts of the current frame from historical frames to achieve a more complete 3D representation. Conversely, when performing 2D feature fusion, it's better to consider the interference of occluded features, and for the current frame, it will make sense to fuse non-occluded 2D features.**\n\n**[Answer]** *We sincerely thank the reviewer for the valuable suggestion. We explain this issue as follows:* \n\n1. *In the **early stages of our study**, we indeed implemented a similar design that uses features from historical frames to supplement the occluded regions in the current frame. However, our experiments showed that this strategy **did not improve the performance** and, in scenes with significant object motion, even led to performance degradation.*\n\n2. *Upon further **investigation and theoretical analysis**, we realized that autonomous driving scenarios are characterized by **forward ego-motion** and **frequent dynamic objects**. As a result, the occluded regions in the current frame are often **not visible in historical frames**, or the features from past frames may suffer from **severe geometric misalignment and semantic inconsistency** due to perspective shifts and object movement.* \n\n3. *In our implementation, the “occlusion regions” are identified using **forward-backward flow consistency check**, which essentially marks **uncertain areas around object boundaries**. After being projected into the 3D voxel space, these regions become **unreliable regions**, and any features obtained from previous frames in these locations are equally **untrustworthy**. Therefore, we choose to fill these regions using the **current frame’s features**, which are spatially aligned and directly observed, making them more geometrically **accurate and stable**.*\n\n4. *Moreover, consistent with observations from prior multi-frame fusion works, **features of dynamic objects or low-texture areas** are highly prone to drift and deformation across time, whereas features extracted from the current frame are generally more **robust and reliable** in such regions.*\n\n5. *For 2D feature fusion, we use the **cosine similarity** between the current frame feature and the warped historical frame feature to design the weight. When the warp historical frame feature $F_{warp}$ is close to the current feature $F_t$, a larger weight is assigned, otherwise a smaller weight (i.e. when **the optical flow error is large**) is assigned **(see Sec. 3.4).** Then, we employ a **occlusion cross-attention** to effectively integrate the features in **non-occluded regions**.*\n\n\n\n------\n\n> **[Weakness 2] It would be better to provide more explanation on the limited improvement on the KITTI360 test set compared to the SOTA.**\n\n***[Answer]** Thank you for this important observation. While our method achieves slightly lower IoU compared to CGFormer, we would like to highlight several key distinctions that explain this and emphasize our method’s strengths:*\n\n1. ***Use of Pretrained Depth in CGFormer:***\n   *CGFormer benefits from **the pre-trained** depth model **on the kitti dataset** provided by MobileStereoNet, which improves the voxel completion accuracy. In contrast, our approach does **not rely on any pre-trained depth estimators**. We employ a **implicit depth learning** based on the LSS paradigm.*\n2. ***Stronger mIoU and Dynamic Object Handling:***\n   *Our method achieves **higher semantic performance**, with **20.81% mIoU**—a **+0.76% gain over CGFormer**. This improvement is particularly evident for **dynamic object classes** (car +2.63%, truck +7.88%, other-vehicle +8.07%, person +2.99% etc.), where we observe a **+3.5% mIoU gain** (Table 11). These dynamic categories are critical in autonomous driving applications and reflect our method’s strength in motion-aware scene understanding.*\n3. ***Higher Efficiency:***\n   *Our model is also significantly more efficient, with **only 52.4M parameters**, compared to **122.42M in CGFormer**. This efficiency allows for better scalability and deployment potential in real-time systems.*\n\n-----\n\n> **[Weakness 3] It would be better to unify the notation of occlusion masks in Figure 3 and Figure 4 for better clarification.**\n\n**[Answer]** *We thank the reviewer for pointing this out. We will unify the notation of occlusion masks in Figures 3 and 4 of the paper.*\n\n------\n\n> **[Weakness 4] Based on the supplementary video materials, the occlusion masks generated from optical flow appear to have significant errors. It would be beneficial to further elaborate on the role and significance of generating occlusion masks through optical flow.**\n\n\n\n**[Answer]** *We thank the reviewer for this observation. We acknowledge that occlusion masks derived from optical flow may not be perfect, especially in complex or blurred regions, as seen in some frames of the supplementary video. In fact, the white part of the occlusion mask represents the occluded area, which is only a small part. The inaccurate optical flow enhances the robustness of the model to a certain extent.*\n\n*We have designed our system to be **robust to moderate flow errors** by incorporating:*\n\n1. ***Cosine-Similarity Weights Selection**, in 2D features, we use the cosine similarity between the current frame feature and the warped historical frame feature to design the weight. When the warp historical frame feature $F_{warp}$ is close to the current feature $F_t$, a larger weight is assigned, otherwise a smaller weight (i.e. when **the optical flow error is large**) is assigned **(see Sec. 3.4).***\n\n2.  ***Consistency Check,** we use the forward and backward consistency check of the optical flow  to query unreliable areas in 3D space **(see Sec. 3.3 and 3.5).***\n3. ***Empirical Effectiveness,** The occlusion cross-attention in FGTA improves mIoU by **+0.9%** (Table 5, Variant 8 vs. 4). The OGVR module further enhances performance by **+0.5%** mIoU (Table 5, Variant 8 vs. 7).*\n\n*Our model integrates historical features of **non-occluded (high confidence)** areas to enhance robustness. We have verified the effectiveness of accurate optical flow for SSC tasks. In the future, we will consider **joint optimization** of optical flow and SSC to alleviate the limitations of optical flow pre-training models. In addition, we will further explain the role and significance of generating occlusion masks through optical flow in the paper.*\n\n\n\n------\n\n> **[Question 1] It would be better to provide more evidence to address my concerns about the role of occlusion masks in refining voxel features and the limited improvement on the KITTI360 test set. I would raise the evaluation score when these concerns are solved.**\n\n**[Answer]** *Thank you for the reviewer's thoughtful questions. We have answered your questions above and hope to have addressed them. If you have any further questions, please let us know so that we can address them as soon as possible.*\n\n------\n\n*We thank the reviewer again and hope our clarifications address the concerns.*"}]

Author rebuttal:

Meta-review: The paper proposes a 3D semantic scene completion method using optical flow and occlusion cues. 
The reviewers give **one accept** and **two borderline accepts**.

**Strengths**
- (r9BF,RB8s,n69C) The motivation for using optical flow for temporal feature fusion is well justified.
- (r9BF,RB8s) Good clarity
- (r9BF,RB8s,n69C) Better accuracy over existing approaches

**Weaknesses**
- (r9BF,RB8s) Accuracy depending on the off-the-shelf optical flow methods
   - (resolved) Table 8 reports the accuracy analyses on using different optical flow methods. The difference is not so substantial.
- (n69C) The unclear role of occlusion mask 
   - (resolved) Justified in the rebuttal (through empirical finding)
- (n69C) Justification of limited accuracy on KITTI360
   - (resolved) Other methods have a more advantageous setup; the proposed method is more light-weighted.


The rebuttal resolves almost all of the concerns from the reviewers. ACs agree to accept the paper. It's highly encouraged to include all the new analyses and discussions in the final version.
Decision: Accept (poster)