用低成本二值占用数据提升3D语义占位预测精度
From Binary to Semantic: Utilizing Large-Scale Binary Occupancy Data for 3D Semantic Occupancy Prediction
- 分两阶段：先用二值数据预训练，再自动生成语义标签
- 在KITTI和Waymo数据集上均超越现有方法，性能提升显著
- 适合无激光雷达的视觉自动驾驶系统研究者参考
精准感知周围环境对安全自动驾驶至关重要。3D占位预测能估计道路、建筑等物体的详细三维结构，尤其适用于不依赖激光雷达的视觉主导自动驾驶系统。然而，3D语义占位预测需标注点云数据，获取成本高。相比之下，大规模二值占位数据（仅区分占据与空闲空间）可低成本获取，但其潜力尚未被探索。本文从预训练与基于学习的自动标注两个角度研究如何利用此类数据。提出一种新型二值占位框架，将预测过程分解为二值与语义占位模块，有效融合二值数据。实验表明，该框架在预训练与自动标注任务中均优于现有方法，显著提升3D语义占位预测性能。代码将开源于https://github.com/ToyotaInfoTech/b2s-occupancy。
原文摘要 · Abstract (English)
Accurate perception of the surrounding environment is essential for safe autonomous driving. 3D occupancy prediction, which estimates detailed 3D structures of roads, buildings, and other objects, is particularly important for vision-centric autonomous driving systems that do not rely on LiDAR sensors. However, in 3D semantic occupancy prediction -- where each voxel is assigned a semantic label -- annotated LiDAR point clouds are required, making data acquisition costly. In contrast, large-scale binary occupancy data, which only indicate occupied or free space without semantic labels, can be collected at a lower cost. Despite their availability, the potential of leveraging such data remains unexplored. In this study, we investigate the utilization of large-scale binary occupancy data from two perspectives: (1) pre-training and (2) learning-based auto-labeling. We propose a novel binary occupancy-based framework that decomposes the prediction process into binary and semantic occupancy modules, enabling effective use of binary occupancy data. Our experimental results demonstrate that the proposed framework outperforms existing methods in both pre-training and auto-labeling tasks, highlighting its effectiveness in enhancing 3D semantic occupancy prediction. The code will be available at https://github.com/ToyotaInfoTech/b2s-occupancy
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。