Monocular BEV perception of road scenes via front-to-top view projection

• Main Authors: LIU, Wenxi, LI, Qi, YANG, Weixiang, CAI, Jiaxin, YU, Yuanhong, MA, Yuexin, HE, Shengfeng, PAN, Jia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Autonomous driving; BEV perception; Estimation; Feature extraction; Layout; Roads; segmentation; Task analysis; Three-dimensional displays; Transformers; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8727; https://ink.library.smu.edu.sg/context/sis_research/article/9730/viewcontent/mathematics_12_00916_pvoa_cc_by.pdf
• Institution: Singapore Management University

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.