BEVMap: Map-Aware BEV Modeling for 3D Perception

Mincheol Chang, Seokha Moon, Reza Mahjourian, Jinkyu Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In autonomous driving applications, there is a strong preference for modeling the world in Bird's-Eye View (BEV), as it leads to improved accuracy and performance. BEV features are widely used in perception tasks since they allow fusing information from multiple views in an efficient manner. However, BEV features generated from camera images are prone to be imprecise due to the difficulty of estimating depth in the perspective view. Improper placement of BEV features limits the accuracy of downstream tasks. We introduce a method for incorporating map information to improve perspective depth estimation from 2D camera images and thereby producing geometrically- and semantically-robust BEV features. We show that augmenting the camera images with the BEV map and map-to-camera projections can compensate for the depth uncertainty and enrich camera-only BEV features with road contexts. Experiments on the nuScenes dataset demonstrate that our method outperforms previous approaches using only camera images in segmentation and detection tasks.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7404-7413
Number of pages10
ISBN (Electronic)9798350318920
DOIs
Publication statusPublished - 2024
Event2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 - Waikoloa, United States
Duration: 2024 Jan 42024 Jan 8

Publication series

NameProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

Conference

Conference2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Country/TerritoryUnited States
CityWaikoloa
Period24/1/424/1/8

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Algorithms
  • Applications
  • Autonomous Driving
  • Image recognition and understanding

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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