标题： 从二进制到语义：利用大规模二进制占据数据进行三维语义占据预测 显示英文标题

标题： From Binary to Semantic: Utilizing Large-Scale Binary Occupancy Data for 3D Semantic Occupancy Prediction

摘要： 准确感知周围环境对于安全的自动驾驶至关重要。 3D占用预测，即估计道路、建筑物和其他物体的详细3D结构，在不依赖激光雷达传感器的以视觉为中心的自动驾驶系统中尤为重要。 然而，在3D语义占用预测中——每个体素都被分配一个语义标签——需要标注的激光雷达点云数据，使得数据获取成本较高。 相比之下，大规模二进制占用数据仅指示占用或自由空间而不带语义标签，可以以较低的成本收集。 尽管这些数据易于获取，但利用这些数据的潜力尚未被探索。 在本研究中，我们从两个角度探讨了大规模二进制占用数据的利用：(1) 预训练和 (2) 基于学习的自动标记。 我们提出了一种基于二进制占用的新框架，将预测过程分解为二进制和语义占用模块，从而有效利用二进制占用数据。 我们的实验结果表明，所提出的框架在预训练和自动标记任务中均优于现有方法，突显了其在增强3D语义占用预测方面的有效性。 代码可在 https://github.com/ToyotaInfoTech/b2s-occupancy 获取。 显示英文摘要

摘要： 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 is available at https://github.com/ToyotaInfoTech/b2s-occupancy