The DOI matches its Crossref record; 31 of the 33 references it cites were found in a registry or an index.
Automatic photo pop-up
Abstract
This paper presents a fully automatic method for creating a 3D model from a single photograph. The model is made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration. Our main insight is that instead of attempting to recover precise geometry, we statistically model geometric classes defined by their orientations in the scene. Our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. Because of the inherent ambiguity of the problem and the statistical nature of the approach, the algorithm is not expected to work on every image. However. it performs surprisingly well for a wide range of scenes taken from a typical person's photo album.
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“As a baseline we compare against the pose network used by [62], which builds upon [76] with an additional scaling of the translation by the mean of the inverse depth.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [62]
Citations
Cites
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Use of the hough transformation to detect lines and curves in pictures
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Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,205
Levoy, M., & Hanrahan, P. (1996) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/237170.237199 · Cited by 3,789
Multiple View Geometry in Computer Vision (2nd ed)
Hartley, R. I., & Zisserman, A. (2003) · Cambridge University Press eBooks · Cited by 2,747
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Research works printed but not linked: 1
- 10.5555/839287.1515131 · DOI
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Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
Digging Into Self-Supervised Monocular Depth Estimation
Godard, C., Aodha, O. M., Firman, M., & Brostow, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00393 · Cited by 2,561
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. J. (2017) · 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.2017.264 · Cited by 2,513
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer
Ranftl, R., Lasinger, K., Hafner, D., Konrad Schindler, B., & Koltun, V. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3019967 · Cited by 2,279
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Choy, C., Xu, D., Gwak, J., Chen, K., & Savarese, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_38 · Cited by 1,762
Metadata
- Authors
- Hoiem, D., Efros, A. A., & Hebert, M.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- ACM Transactions on Graphics
- Published
- 2005-07
- Volume
- 24
- Issue
- 3
- Pages
- 577-584
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2151996626
- Cited by
- 616
- References
- 33
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 7 · 2023: 7 · 2022: 23 · 2021: 25 · 2020: 30 · 2019: 48 · 2018: 59 · 2017: 33 · 2016: 43 · 2015: 38 · 2014: 39 · 2013: 36 · 2012: 33
- FWCI
- 24.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Graphics (publishedVersion)
- KiltHub Repository (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- automatic 3D model generation, single-image 3D reconstruction, image segmentation
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