L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model
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Abstract (arXiv)
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/.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 38 references that are in the map. First posted on arXiv 16 Mar 2025.
Builds on
30 of 38 references.
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18 more
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Citing papers per year: 2025: 2; 2026: 6.
8 papers.
- 2025VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detectioncited by 13
- 2026Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robotscited by 5
- 2026FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Predictioncited by 2
- 2026Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Predictioncited by 1
- 2026VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understandingcited by 1
- 2025HD2-SSC: High-Dimension High-Density Semantic Scene Completion for Autonomous Drivingcited by 0
- 2026UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusioncited by 0
- 2026SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completioncited by 0
- 2026SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion15 Sep 2026
- 2026UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion2 Jun 2026
- 2026VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding24 May 2026
- 2026FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction30 Apr 2026
- 2026Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction24 Mar 2026
- 2026Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots13 Mar 2026
- 2025HD2-SSC: High-Dimension High-Density Semantic Scene Completion for Autonomous Driving11 Nov 2025
- 2025VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection5 Jun 2025