AutoLay: Benchmarking amodal layout estimation for autonomous driving

• URL: http://arxiv.org/abs/2108.09047v1
• Date: Fri, 20 Aug 2021 08:21:11 GMT
• Title: AutoLay: Benchmarking amodal layout estimation for autonomous driving
• Authors: Kaustubh Mani, N. Sai Shankar, Krishna Murthy Jatavallabhula and K. Madhava Krishna
• Abstract summary: AutoLay is a dataset and benchmark for amodal layout estimation from monocular images. In addition to fine-grained attributes such as lanes, sidewalks, and vehicles, we also provide semantically annotated 3D point clouds.
• Score: 18.152206533685412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view. The term amodal implies we also reason about entities in the scene that are occluded or truncated in image space. While several recent efforts have tackled this problem, there is a lack of standardization in task specification, datasets, and evaluation protocols. We address these gaps with AutoLay, a dataset and benchmark for amodal layout estimation from monocular images. AutoLay encompasses driving imagery from two popular datasets: KITTI and Argoverse. In addition to fine-grained attributes such as lanes, sidewalks, and vehicles, we also provide semantically annotated 3D point clouds. We implement several baselines and bleeding edge approaches, and release our data and code.

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