Monocular BEV perception of road scenes via front-to-top view projection
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 p...
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sg-smu-ink.sis_research-97302024-04-18T07:32:57Z Monocular BEV perception of road scenes via front-to-top view projection LIU, Wenxi LI, Qi YANG, Weixiang CAI, Jiaxin YU, Yuanhong MA, Yuexin HE, Shengfeng PAN, Jia 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. 2024-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8727 info:doi/10.1109/TPAMI.2024.3377812 https://ink.library.smu.edu.sg/context/sis_research/article/9730/viewcontent/mathematics_12_00916_pvoa_cc_by.pdf http://creativecommons.org/licenses/by/3.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University 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 |
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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 |
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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 LIU, Wenxi LI, Qi YANG, Weixiang CAI, Jiaxin YU, Yuanhong MA, Yuexin HE, Shengfeng PAN, Jia Monocular BEV perception of road scenes via front-to-top view projection |
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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. |
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text |
author |
LIU, Wenxi LI, Qi YANG, Weixiang CAI, Jiaxin YU, Yuanhong MA, Yuexin HE, Shengfeng PAN, Jia |
author_facet |
LIU, Wenxi LI, Qi YANG, Weixiang CAI, Jiaxin YU, Yuanhong MA, Yuexin HE, Shengfeng PAN, Jia |
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LIU, Wenxi |
title |
Monocular BEV perception of road scenes via front-to-top view projection |
title_short |
Monocular BEV perception of road scenes via front-to-top view projection |
title_full |
Monocular BEV perception of road scenes via front-to-top view projection |
title_fullStr |
Monocular BEV perception of road scenes via front-to-top view projection |
title_full_unstemmed |
Monocular BEV perception of road scenes via front-to-top view projection |
title_sort |
monocular bev perception of road scenes via front-to-top view projection |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
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 |
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