Unlike humans, who can effortlessly estimate the entirety of objects even when partially occluded, modern computer vision algorithms still find this aspect extremely challenging. Leveraging this amodal perception for autonomous driving remains largely untapped due to the lack of suitable datasets. The curation of these datasets is primarily hindered by significant annotation costs and mitigating annotator subjectivity in accurately labeling occluded regions. To address these limitations, we introduce AmodalSynthDrive, a synthetic multi-task multi-modal amodal perception dataset. The dataset provides multi-view camera images, 3D bounding boxes, LiDAR data, and odometry for 150 driving sequences with over 1M object annotations in diverse traffic, weather, and lighting conditions. AmodalSynthDrive supports multiple amodal scene understanding tasks including the introduced amodal depth estimation for enhanced spatial understanding. We evaluate several baselines for each of these tasks to illustrate the challenges and set up public benchmarking servers. The dataset is available at http://amodalsynthdrive.cs.uni-freiburg.de.

翻译：不同于人类能够轻松估计部分遮挡物体的完整形态，现代计算机视觉算法在处理这一问题时仍面临极大挑战。由于缺乏合适的匹配数据集，利用共情感知（amodal perception）推动自动驾驶发展的潜力仍未充分挖掘。这类数据集的构建主要受限于高昂的标注成本，以及减少标注人员在准确标注遮挡区域时主观差异的难度。针对上述限制，我们提出了AmodalSynthDrive——一个合成的多任务、多模式共情感知数据集。该数据集包含150个驾驶序列的多视角摄像机图像、3D边界框、LiDAR数据和里程计信息，在多样化的交通、天气和光照条件下提供超过100万个物体标注。AmodalSynthDrive支持多种共情场景理解任务，包括我们引入的用于增强空间理解的共情深度估计（amodal depth estimation）。我们为每个任务评估了若干基线方法以阐明挑战，并建立了公开基准测试服务器。该数据集可通过http://amodalsynthdrive.cs.uni-freiburg.de获取。