Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2024
Abstract
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.
Keywords
Autonomous driving, BEV perception, Estimation, Feature extraction, Layout, Roads, segmentation, Task analysis, Three-dimensional displays, Transformers
Discipline
Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
First Page
1
Last Page
17
ISSN
0162-8828
Identifier
10.1109/TPAMI.2024.3377812
Publisher
Institute of Electrical and Electronics Engineers
Citation
LIU, Wenxi; LI, Qi; YANG, Weixiang; CAI, Jiaxin; YU, Yuanhong; MA, Yuexin; HE, Shengfeng; and PAN, Jia.
Monocular BEV perception of road scenes via front-to-top view projection. (2024). IEEE Transactions on Pattern Analysis and Machine Intelligence. 1-17.
Available at: https://ink.library.smu.edu.sg/sis_research/8727
Copyright Owner and License
Authors-CC-BY
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional URL
https://doi.org/10.1109/TPAMI.2024.3377812
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons