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Article: Monocular BEV Perception of Road Scenes Via Front-to-Top View Projection

TitleMonocular BEV Perception of Road Scenes Via Front-to-Top View Projection
Authors
KeywordsAutonomous driving
BEV perception
Estimation
Feature extraction
Layout
Roads
segmentation
Task analysis
Three-dimensional displays
Transformers
Issue Date1-Sep-2024
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, v. 46, n. 9, p. 6109-6125 How to Cite?
AbstractHD 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.
Persistent Identifierhttp://hdl.handle.net/10722/351846
ISSN
2023 Impact Factor: 20.8
2023 SCImago Journal Rankings: 6.158

 

DC FieldValueLanguage
dc.contributor.authorLiu, Wenxi-
dc.contributor.authorLi, Qi-
dc.contributor.authorYang, Weixiang-
dc.contributor.authorCai, Jiaxin-
dc.contributor.authorYu, Yuanlong-
dc.contributor.authorMa, Yuexin-
dc.contributor.authorHe, Shengfeng-
dc.contributor.authorPan, Jia-
dc.date.accessioned2024-12-03T00:35:16Z-
dc.date.available2024-12-03T00:35:16Z-
dc.date.issued2024-09-01-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, v. 46, n. 9, p. 6109-6125-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10722/351846-
dc.description.abstractHD 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.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAutonomous driving-
dc.subjectBEV perception-
dc.subjectEstimation-
dc.subjectFeature extraction-
dc.subjectLayout-
dc.subjectRoads-
dc.subjectsegmentation-
dc.subjectTask analysis-
dc.subjectThree-dimensional displays-
dc.subjectTransformers-
dc.titleMonocular BEV Perception of Road Scenes Via Front-to-Top View Projection-
dc.typeArticle-
dc.identifier.doi10.1109/TPAMI.2024.3377812-
dc.identifier.scopuseid_2-s2.0-85188533229-
dc.identifier.volume46-
dc.identifier.issue9-
dc.identifier.spage6109-
dc.identifier.epage6125-
dc.identifier.eissn1939-3539-
dc.identifier.issnl0162-8828-

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