We introduce UniOcc, a comprehensive, unified benchmark for occupancy forecasting (i.e., predicting future occupancies based on historical information) and current-frame occupancy prediction from camera images. UniOcc unifies data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), which provides 2D/3D occupancy labels with per-voxel flow annotations and support for cooperative autonomous driving. In terms of evaluation, unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel metrics that do not depend on ground-truth occupancy, enabling robust assessment of 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.

翻译：我们提出了UniOcc，一个用于基于相机图像的占据预报（即依据历史信息预测未来占据状态）及当前帧占据预测的全面统一基准。UniOcc整合了来自多个真实世界数据集（即nuScenes、Waymo）和高保真驾驶模拟器（即CARLA、OpenCOOD）的数据，提供了带有逐体素流标注的2D/3D占据标签，并支持协同自动驾驶。在评估方面，与现有研究依赖次优伪标签进行评估不同，UniOcc引入了不依赖于真实占据标注的新颖度量标准，从而能够对占据质量的额外维度进行鲁棒评估。通过对最先进模型的大量实验，我们证明大规模、多样化的训练数据以及显式的流信息能显著提升占据预测与预报的性能。