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.
“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]
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Example Based 3D Reconstruction from Single 2D Images
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CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
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Synthetic Data for Text Localisation in Natural ImagesPreprint
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BA-Net: Dense Bundle Adjustment Networks
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Content-preserving warps for 3D video stabilization
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Three-Dimensional Video Postproduction and Processing
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Learning Single Camera Depth Estimation Using Dual-Pixels
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Efficient structured prediction for 3D indoor scene understanding
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Single image depth estimation: An overview
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Single View Reconstruction of Curved Surfaces
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Novel View Synthesis of Dynamic Scenes With Globally Coherent Depths From a Monocular Camera
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Learning Object-to-Class Kernels for Scene Classification
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Coherent intrinsic images from photo collections
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Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks
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Superpixel Endmember Detection
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Dynamic hair manipulation in images and videos
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- 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 › Engineering › Computational Mechanics › 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
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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