Cited by 206Advanced Vision and ImagingAdvanced Neural Network ApplicationsVisual perception and processing mechanisms

Recovering Occlusion Boundaries from a Single Image

Open access
Hoiem, Derek, Stein, Andrew N., Efros, Alexei A., Hebert, Martial·2007·2007 IEEE 11th International Conference on Computer Vision

Abstract

Occlusion reasoning, necessary for tasks such as navigation and object search, is an important aspect of everyday life and a fundamental problem in computer vision. We believe that the amazing ability of humans to reason about occlusions from one image is based on an intrinsically 3D interpretation. In this paper, our goal is to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. Our approach is to learn to identify and label occlusion boundaries using the traditional edge and region cues together with 3D surface and depth cues. Since some of these cues require good spatial support (i.e., a segmentation), we gradually create larger regions and use them to improve inference over the boundaries. Our experiments demonstrate the power of a scene-based approach to occlusion reasoning.

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Crossref · 10.1109/iccv.2007.4408985

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Authors
Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M.
Affiliations
Carnegie Mellon University (US)
Venue
2007 IEEE 11th International Conference on Computer Vision
Publisher
IEEE
Published
2007
Pages
1-8
OpenAlex
W2103380420
Cited by
206
References
35
Citations by year
2026: 1 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 8 · 2020: 9 · 2019: 3 · 2018: 4 · 2017: 7 · 2016: 15 · 2015: 19 · 2014: 14 · 2013: 17 · 2012: 14
FWCI
17.24
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
  • KiltHub Repository (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • KiltHub Repository
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
occlusion boundaries, occlusion reasoning, depth ordering, free-standing structures, depth cues, single image

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