the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Sep 2026
A Lightweight BEV Perception Optimization Framework for Real-Time 3D Object Detection in Occluded Scenes
Abstract. Real-time 3D object detection in occluded scenes is a core challenge for pure-vision autonomous driving perception. Existing dense Bird's Eye View (BEV) detection methods suffer from redundant backbone parameters, inflexible fixed sampling strategies, and coarse-grained temporal modeling, making it difficult to simultaneously satisfy detection accuracy and real-time latency requirements on embedded vehicle platforms. This paper proposes a lightweight BEV perception optimization framework for occluded scenes. An occlusion-aware adaptive voxel feature sampling module dynamically switches between fast ray projection and deformable attention paths according to scene complexity. A temporal grouping fusion module based on Res2Net principles performs grouped cross-frame fusion of consecutive BEV feature maps without introducing additional learnable parameters. A two-stage LiDAR-to-camera knowledge distillation scheme with a geometric compensation module transfers depth geometry knowledge during training while incurring zero inference overhead. Experiments on the nuScenes dataset demonstrate that the proposed method achieves 38.7% mAP and 51.3% NDS under the lightweight configuration, with an inference latency of 38.2 ms and 26.2 FPS on NVIDIA Tesla T4, ranking highest in NDS among comparable methods.
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Status: open (until 29 Oct 2026)
- RC1: 'Comment on ms-2026-168', Anonymous Referee #1, 27 Sep 2026 reply
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RC2: 'Comment on ms-2026-168', Anonymous Referee #2, 11 Oct 2026
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The manuscript proposes a lightweight BEV perception framework for real-time 3D object detection in occluded scenes by combining adaptive sampling, temporal fusion, and LiDAR-to-camera knowledge distillation. The topic is relevant and the experimental results are promising. However, several issues should be addressed before publication.
Major Comments
- The occlusion-aware mechanism needs stronger validation. The scene complexity score is defined using the mean and standard deviation of BEV features, but the manuscript does not demonstrate that this score is directly related to occlusion severity. More quantitative evidence is needed to show that the adaptive sampling strategy is truly occlusion-aware.
- The adaptive sampling pipeline is not sufficiently clear. The manuscript states that the scene complexity score is calculated from the current BEV feature map, while this score is also used to determine how the BEV feature map is generated. The authors should clarify the exact processing order and avoid possible circular dependency in the framework description.
- Some claims are inconsistent with the reported results. The manuscript states that the temporal grouping fusion introduces no additional learnable parameters, but the method contains several convolutional layers and Table 3 shows an increase in model parameters. This statement should be corrected.
- The fairness of the baseline comparison should be clarified. The authors should state whether the results of the comparison methods were reproduced under the same settings or directly taken from the literature. In addition, EdgeNeXt-S should not be described as a “ResNet-50 backbone configuration.”
- The latency analysis requires further explanation. The manuscript reports that knowledge distillation reduces inference latency from 43.5 ms to 38.2 ms, although the teacher model is removed during inference. The authors should explain the mechanism responsible for this latency reduction and provide more rigorous timing results.
- The ablation study only demonstrates incremental rather than independent contributions. The modules are added sequentially in Table 3. Therefore, the current results mainly show the incremental effect of each module under a fixed addition order. The wording should be revised, or additional leave-one-out experiments should be provided.
Minor Comments
- “Reasoning efficiency” should be replaced by “inference efficiency.”
- The equation numbering should be carefully checked, as several equation numbers appear duplicated. ms-2026-168
- The qualitative discussion should distinguish between object occlusion and adverse weather or nighttime conditions.
- The resolution and readability of Figs. 1, 4, and 5 should be improved.
- The manuscript would benefit from further English-language polishing.
Citation: https://doi.org/10.5194/ms-2026-168-RC2
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- 1
The manuscript presents a practical lightweight BEV framework that achieves a reasonable trade-off between accuracy and efficiency. The scene-complexity-driven adaptive switching between inexpensive ray projection and deformable sampling is interesting and has potential practical value for engineering applications.
Some minor suggestions for revision:
1. The manuscript is motivated primarily by the occlusion problem. The authors are encouraged to provide more rigorous experiments to verify whether the proposed complexity metric effectively reflects the degree of occlusion and to quantify the performance improvements under different occlusion levels.
2. The claim regarding real-time embedded deployment appears stronger than what is currently supported by the experimental evidence, as inference efficiency is evaluated only on an NVIDIA Tesla T4 rather than on an embedded/mobile platform. The power consumption of the T4 is also substantially higher than that of mobile computing platforms with a typical power budget of approximately 20 W. Evaluation on representative embedded hardware would therefore strengthen the deployment-related claims.
3. The manuscript is somewhat verbose in places. For example, Section 5 repeats much of the numerical evidence already presented in Sections 4.3–4.5. In addition, the Introduction could be streamlined by removing detailed descriptions of prior studies that are subsequently repeated in Section 2.
4. The readability and visual presentation of the Figure 2, 4, and 5 could be improved.