AmodalSynthDrive is a comprehensive dataset providing multi-view camera images, 3D bounding boxes, LiDAR data, and odometry for various tasks in amodal perception. It aims to advance research in autonomous driving by offering benchmarks and novel tasks like amodal depth estimation.
The dataset facilitates the development of standalone and integrated approaches for amodal scene understanding tasks. It includes annotations for traditional modal perception tasks as well.
Key challenges include predicting occluded regions accurately and modeling relative occlusion order for precise depth estimations. The dataset's complexity is highlighted by its diverse weather conditions and detailed occluded region annotations.
Various baselines are evaluated on the dataset to demonstrate challenges and improvements in tasks like amodal panoptic segmentation, instance segmentation, and semantic segmentation.
Transfer learning results show that pre-training on AmodalSynthDrive enhances performance on real-world datasets for amodal instance segmentation and panoptic segmentation.
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by Ahmed Rida S... om arxiv.org 03-12-2024
https://arxiv.org/pdf/2309.06547.pdfDiepere vragen