L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model

Ruoyu Wang*, Yukai Ma*, Yi Yao, Sheng Tao, Zongzhi Zhu, Haoang Li, Yong Liu, Xingxing Zuo,
Zhejiang University, Technical University of Munich
Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
*Indicates Equal Contribution
Paper Code arXiv

Abstract

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 three types of cross-modal knowledge modules (FSD, TPVD, 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%.

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BibTeX


@inproceedings{wang2025l2cocc,
  title={L2cocc: Lightweight camera-centric semantic scene completion via distillation of lidar model},
  author={Wang, Ruoyu and Ma, Yukai and Yao, Yi and Tao, Sheng and Li, Haoang and Zhu, Zongzhi and Liu, Yong and Zuo, Xingxing},
  booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={716--723},
  year={2025},
  organization={IEEE}
}