UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915445479571456 |
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| author | Wang, Yuping Huang, Xiangyu Sun, Xiaokang Yan, Mingxuan Xing, Shuo Tu, Zhengzhong Li, Jiachen |
| author_facet | Wang, Yuping Huang, Xiangyu Sun, Xiaokang Yan, Mingxuan Xing, Shuo Tu, Zhengzhong Li, Jiachen |
| contents | We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy prediction (i.e., predicting current-frame occupancy from camera images. UniOcc unifies the data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), providing 2D/3D occupancy labels and annotating innovative per-voxel flows. Unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel evaluation metrics that do not depend on ground-truth labels, enabling robust assessment on additional aspects of occupancy quality. Through extensive experiments on state-of-the-art models, we demonstrate that large-scale, diverse training data and explicit flow information significantly enhance occupancy prediction and forecasting performance. Our data and code are available at https://uniocc.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving Wang, Yuping Huang, Xiangyu Sun, Xiaokang Yan, Mingxuan Xing, Shuo Tu, Zhengzhong Li, Jiachen Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multiagent Systems Robotics We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy prediction (i.e., predicting current-frame occupancy from camera images. UniOcc unifies the data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), providing 2D/3D occupancy labels and annotating innovative per-voxel flows. Unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel evaluation metrics that do not depend on ground-truth labels, enabling robust assessment on additional aspects of occupancy quality. Through extensive experiments on state-of-the-art models, we demonstrate that large-scale, diverse training data and explicit flow information significantly enhance occupancy prediction and forecasting performance. Our data and code are available at https://uniocc.github.io/. |
| title | UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2503.24381 |