OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception

Kavli Affiliate: Zheng Zhu

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| Summary:

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 and
multi-modal baselines are established for the OpenOccupancy benchmark.
Furthermore, considering the complexity of surrounding occupancy perception
lies in the computational burden of high-resolution 3D predictions, we propose
the Cascade Occupancy Network (CONet) to refine the coarse prediction, which
relatively enhances the performance by ~30% than the baseline. We hope the
OpenOccupancy benchmark will boost the development of surrounding occupancy
perception algorithms.

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