Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues

Stefanie Walz, Mario Bijelic, Andrea Ramazzina, Amanpreet Walia, Fahim Mannan, Felix Heide

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

3 Scopus citations

Abstract

We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we propose a depth estimation method with a monocular and stereo depth prediction branch which are combined in a final fusion stage. Each block is supervised through a combination of supervised and gated self-supervision losses. To facilitate training and validation, we acquire a long-range synchronized gated stereo dataset for automotive scenarios. We find that the method achieves an improvement of more than 50 % MAE compared to the next best RGB stereo method, and 74 % MAE to existing monocular gated methods for distances up to 160 m. Our code, models and datasets are available here11https://light.princeton.edu/gatedstereo/.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
PublisherIEEE Computer Society
Pages13252-13262
Number of pages11
ISBN (Electronic)9798350301298
DOIs
StatePublished - 2023
Event2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada
Duration: Jun 18 2023Jun 22 2023

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2023-June
ISSN (Print)1063-6919

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
Country/TerritoryCanada
CityVancouver
Period6/18/236/22/23

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Keywords

  • 3D from multi-view and sensors

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