OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Xiaofeng Wang, Zheng Zhu, Wenbo Xu, Yunpeng Zhang, Yi Wei, Xu Chi, Yun, Ye, Dalong Du, Jiwen Lu, Xingang Wang

TL;DR
OpenOccupancy introduces a comprehensive benchmark for surrounding semantic occupancy perception in autonomous driving, extending the nuScenes dataset with dense annotations and proposing a new network to improve prediction accuracy.
Contribution
It is the first benchmark for surrounding semantic occupancy perception, with enhanced dense annotations and a novel Cascade Occupancy Network for better predictions.
Findings
Enhanced annotation density with ~2x improvement
Baseline methods established for camera, LiDAR, and multi-modal data
Proposed CONet improves performance by ~30% over baselines
Abstract
Semantic occupancy perception is essential for autonomous driving, as automated vehicles require a fine-grained perception of the 3D urban structures. However, existing relevant benchmarks lack diversity in urban scenes, and they only evaluate front-view predictions. Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark. In the OpenOccupancy benchmark, we extend the large-scale nuScenes dataset with dense semantic occupancy annotations. Previous annotations rely on LiDAR points superimposition, where some occupancy labels are missed due to sparse LiDAR channels. To mitigate the problem, we introduce the Augmenting And Purifying (AAP) pipeline to ~2x densify the annotations, where ~4000 human hours are involved in the labeling process. Besides, camera-based, LiDAR-based…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Advanced Vision and Imaging
