The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Robust single-view geometry and motion reconstruction
Abstract
We present a framework and algorithms for robust geometry and motion reconstruction of complex deforming shapes. Our method makes use of a smooth template that provides a crude approximation of the scanned object and serves as a geometric and topological prior for reconstruction. Large-scale motion of the acquired object is recovered using a novel space-time adaptive, non-rigid registration method. Fine-scale details such as wrinkles and folds are synthesized with an efficient linear mesh deformation algorithm. Subsequent spatial and temporal filtering of detail coefficients allows transfer of persistent geometric detail to regions not observed by the scanner. We show how this two-scale process allows faithful recovery of small-scale shape and motion features leading to a high-quality reconstruction. We illustrate the robustness and generality of our algorithm on a variety of examples composed of different materials and exhibiting a large range of dynamic deformations.
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In the citing paper
“To alleviate it, [2], [6], [32] reduce the DoF explicitly by computing local transformations with respect to clusters or nodes, and most non-rigid registration methods use regularization to further constrain the solutions.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [32]
In the citing paper
“Yet, given only two surfaces and a high-dimensional deformation model (like [6], [32]), the solution can only be regularized by other prior knowledge-mostly in the form of regularizations to ensure smoothness.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [32]
In the citing paper
“Further challenges lie in (i) avoiding the need for markers or templates, and (ii) capturing and processing fine scale details [32].”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [32]
Citations
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Efficient 3D scan registration for hand modeling and reconstruction
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Unsupervised Dense Deformation Embedding Network for Template-Free Shape CorrespondencePreprint
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Self-citationImplicit Surface Compression -- with Good Old Discrete Cosine Transform and Motion Compensation
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Human Body Segment Volume Estimation With Front and Back RGB-D Images
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Modélisation Spatio-Temporelle des scènes dynamiques 3D à partir de données visuelles
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Dynamic Geometry Capture with a Multi-View Structured-Light System
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Metadata
- Authors
- Li, H., Adams, B., Guibas, L. J., & Pauly, M.
- Affiliations
- ETH Zurich (CH)
- KU Leuven (BE)
- Stanford University (US)
- Venue
- ACM Transactions on Graphics
- Published
- 2009-12
- Volume
- 28
- Issue
- 5
- Pages
- 1-10
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W1985290145
- Cited by
- 334
- References
- 44
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 6 · 2023: 12 · 2022: 12 · 2021: 29 · 2020: 22 · 2019: 24 · 2018: 32 · 2017: 23 · 2016: 24 · 2015: 28 · 2014: 30 · 2013: 22 · 2012: 24
- FWCI
- 19.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Graphics (publishedVersion)
- Funders
- National Science Foundation (ITR 0205671FRG 0354543FODAVA 808515)
- National Institutes of Health (GM-072970)
- Stavros Niarchos Foundation (200021-112122)
- 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
- single-view reconstruction, 3D shape reconstruction, motion reconstruction, non-rigid registration, mesh deformation, temporal filtering, dynamic deformation
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