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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To alleviate overfitting, [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.
“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]
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.
“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]
Further challenges lie in (i) avoiding the need for markers or templates, and (ii) capturing and processing fine scale details [32].
“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]
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RobustFusion: Human Volumetric Capture with Data-Driven Visual Cues Using a RGBD Camera
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Gilani, S. Z., Mian, A. S., Faisal Shafait, B., & Reid, I. D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2725279 · Cited by 89 · see citing works
Global registration of dynamic range scans for articulated model reconstruction
Chang, W., & Zwicker, M. (2011) · ACM Transactions on Graphics · doi:10.1145/1966394.1966405 · Cited by 87 · see citing works
Animation cartography—intrinsic reconstruction of shape and motion
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Leveraging motion capture and 3D scanning for high-fidelity facial performance acquisition
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User-Specific Hand Modeling from Monocular Depth Sequences
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Learning an animatable detailed 3D face model from in-the-wild images
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Deformation capture and modeling of soft objects
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Analyzing Clothing Layer Deformation Statistics of 3D Human Motions
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UnstructuredFusion: Realtime 4D Geometry and Texture Reconstruction Using Commercial RGBD Cameras
Xu, L., Su, Z., Han, L., Yu, T., Liu, Y., & Fang, L. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2915229 · Cited by 74 · see citing works
Long, Y., Li, Z., Yee, C. H., Ng, C., Taylor, R. H., Unberath, M., & Dou, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_40 · Cited by 72 · see citing works
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras
Xu, L., Liu, Y., Cheng, W., Guo, K., Zhou, G., Dai, Q., & Fang, L. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2728660 · Cited by 72 · see citing works
Scanning and tracking dynamic objects with commodity depth cameras
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SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction
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Consensus Skeleton for Non‐rigid Space‐time Registration
Zheng, Q., Sharf, A., Tagliasacchi, A., Chen, B., Zhang, H., Sheffer, A., & Cohen‐Or, D. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01633.x · Cited by 67 · see citing works
Real-Time Geometry, Albedo, and Motion Reconstruction Using a Single RGB-D Camera
Guo, K., Xu, F., Yu, T., Liu, X.-Y., Dai, Q., & Liu, Y. (2017) · ACM Transactions on Graphics · doi:10.1145/3083722 · Cited by 67 · see citing works
Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure
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Leveraging motion capture and 3D scanning for high-fidelity facial performance acquisition
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Modeling clothing as a separate layer for an animatable human avatar
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Quasi-Newton Solver for Robust Non-Rigid Registration
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Tracking surfaces with evolving topology
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Self-citationEfficient and Flexible Deformation Representation for Data-Driven Surface Modeling
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Self-citationOn Computing Mapping of 3D Objects
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Self-citationOcclusion-robust Deformable Object Tracking without Physics Simulation
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Accurate and Robust 3D Facial Capture Using a Single RGBD Camera
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Mapping cardiac surface mechanics with structured light imaging
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Self-citationFully automatic expression-invariant face correspondence
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4D facial expression recognition
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High fidelity facial animation capture and retargeting with contours
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Rigid stabilization of facial expressions
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Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry
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Globally Consistent Space‐Time Reconstruction
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Realtime and Accurate 3D Eye Gaze Capture with DCNN-Based Iris and Pupil Segmentation
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NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Cameras
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Multi-view hair capture using orientation fields
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Self-citationFlyFusion: Realtime Dynamic Scene Reconstruction Using a Flying Depth Camera
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Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
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Fully automatic generation of anatomical face simulation models
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Template-Based 3D Model Fitting Using Dual-Domain Relaxation
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Multiscale gigapixel video: A cross resolution image matching and warping approach
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3-D Reconstruction of Human Body Shape From a Single Commodity Depth Camera
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DeVito, Z., Mara, M., Zollhöfer, M., Bernstein, G., Ragan‐Kelley, J., Theobalt, C., Hanrahan, P., Fisher, M., & Nießner, M. (2017) · ACM Transactions on Graphics · doi:10.1145/3132188 · Cited by 43 · see citing works
- 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 › 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
- single-view reconstruction, 3D shape reconstruction, motion reconstruction, non-rigid registration, mesh deformation, temporal filtering, dynamic deformation
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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