A Structure-Aware Method for Direct Pose Estimation

Hunter Blanton, Scott Workman, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Estimating camera pose from a single image is a fundamental problem in computer vision. Existing methods for solving this task fall into two distinct categories, which we refer to as direct and indirect. Direct methods, such as PoseNet, regress pose from the image as a fixed function, for example using a feed-forward convolutional network. Such methods are desirable because they are deterministic and run in constant time. Indirect methods for pose regression are often non-deterministic, with various external dependencies such as image retrieval and hypothesis sampling. We propose a direct method that takes inspiration from structure-based approaches to incorporate explicit 3D constraints into the network. Our approach maintains the desirable qualities of other direct methods while achieving much lower error in general. Code is available at https://github.com/mvrl/structure-aware-pose-estimation.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Pages205-214
Number of pages10
ISBN (Electronic)9781665409155
DOIs
StatePublished - 2022
Event22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 - Waikoloa, United States
Duration: Jan 4 2022Jan 8 2022

Publication series

NameProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022

Conference

Conference22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Country/TerritoryUnited States
CityWaikoloa
Period1/4/221/8/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • 3D Computer Vision

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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