Recovering Occlusion Boundaries from a Single Image
Open accessAbstract
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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Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Mac Aodha, O., & Brostow, G. J. (2016) · arXiv · doi:10.48550/arxiv.1609.03677 · Cited by 28 · see citing works
Shape from Shading Through Shape Evolution
Yang, D., & Deng, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00398 · Cited by 28 · see citing works
Alphabet SOUP: A framework for approximate energy minimization
Gould, S. J., Amat, F., & Koller, D. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206650 · Cited by 28 · see citing works
Scene shape priors for superpixel segmentation
Moore, A. P., Prince, S. J. D., Warrell, J., Mohammed, U., & Jones, G. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459246 · Cited by 27 · see citing works
Occlusion Boundary Detection via Deep Exploration of Context
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HerbDisc: Towards lifelong robotic object discovery
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Self-citationMulti-view Occlusion Reasoning for Probabilistic Silhouette-Based Dynamic Scene Reconstruction
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Contour based object detection using part bundles
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Saliency and KAZE features assisted object segmentation
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Camera handoff with adaptive resource management for multi-camera multi-object tracking
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Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding
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Exploiting domain knowledge for Object Discovery
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Hallucinating Stereoscopy from a Single Image
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Detecting planes and estimating their orientation from a single image
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Monocular Depth by Nonlinear Diffusion
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Depth Estimation From a Single Image Using Guided Deep Network
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Layered Graphical Models for Tracking Partially Occluded Objects
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Depth Transfer: Depth Extraction from Videos Using Nonparametric Sampling
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Automatic Identification of Vehicle Partial Occlusion in Data Collected by Roadside LiDAR Sensors
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Semantic classification by covariance descriptors within a randomized forest
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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
- green
- figshare.com
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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