  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.
