L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916653640450048 |
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| author | Wang, Ruoyu Ma, Yukai Yao, Yi Tao, Sheng Li, Haoang Zhu, Zongzhi Liu, Yong Zuo, Xingxing |
| author_facet | Wang, Ruoyu Ma, Yukai Yao, Yi Tao, Sheng Li, Haoang Zhu, Zongzhi Liu, Yong Zuo, Xingxing |
| contents | Semantic Scene Completion (SSC) constitutes a pivotal element in autonomous driving perception systems, tasked with inferring the 3D semantic occupancy of a scene from sensory data. To improve accuracy, prior research has implemented various computationally demanding and memory-intensive 3D operations, imposing significant computational requirements on the platform during training and testing. This paper proposes L2COcc, a lightweight camera-centric SSC framework that also accommodates LiDAR inputs. With our proposed efficient voxel transformer (EVT) and cross-modal knowledge modules, including feature similarity distillation (FSD), TPV distillation (TPVD) and prediction alignment distillation (PAD), our method substantially reduce computational burden while maintaining high accuracy. The experimental evaluations demonstrate that our proposed method surpasses the current state-of-the-art vision-based SSC methods regarding accuracy on both the SemanticKITTI and SSCBench-KITTI-360 benchmarks, respectively. Additionally, our method is more lightweight, exhibiting a reduction in both memory consumption and inference time by over 23% compared to the current state-of-the-arts method. Code is available at our project page:https://studyingfufu.github.io/L2COcc/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model Wang, Ruoyu Ma, Yukai Yao, Yi Tao, Sheng Li, Haoang Zhu, Zongzhi Liu, Yong Zuo, Xingxing Computer Vision and Pattern Recognition Semantic Scene Completion (SSC) constitutes a pivotal element in autonomous driving perception systems, tasked with inferring the 3D semantic occupancy of a scene from sensory data. To improve accuracy, prior research has implemented various computationally demanding and memory-intensive 3D operations, imposing significant computational requirements on the platform during training and testing. This paper proposes L2COcc, a lightweight camera-centric SSC framework that also accommodates LiDAR inputs. With our proposed efficient voxel transformer (EVT) and cross-modal knowledge modules, including feature similarity distillation (FSD), TPV distillation (TPVD) and prediction alignment distillation (PAD), our method substantially reduce computational burden while maintaining high accuracy. The experimental evaluations demonstrate that our proposed method surpasses the current state-of-the-art vision-based SSC methods regarding accuracy on both the SemanticKITTI and SSCBench-KITTI-360 benchmarks, respectively. Additionally, our method is more lightweight, exhibiting a reduction in both memory consumption and inference time by over 23% compared to the current state-of-the-arts method. Code is available at our project page:https://studyingfufu.github.io/L2COcc/. |
| title | L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.12369 |