Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data
Abstract
We present a new technique for reconstructing a single shape and its nonrigid motion from 3D scanning data. Our algorithm takes a set of time-varying unstructured sample points that capture partial views of a deforming object as input and reconstructs a single shape and a deformation field that fit the data. This representation yields dense correspondences for the whole sequence, as well as a completed 3D shape in every frame. In addition, the algorithm automatically removes spatial and temporal noise artifacts and outliers from the raw input data. Unlike previous methods, the algorithm does not require any shape template but computes a fitting shape automatically from the input data. Our reconstruction framework is based upon a novel topology-aware adaptive subspace deformation technique that allows handling long sequences with complex geometry efficiently. The algorithm accesses data in multiple sequential passes, so that long sequences can be streamed from hard disk, not being limited by main memory. We apply the technique to several benchmark datasets, significantly increasing the complexity of the data that can be handled efficiently in comparison to previous work.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
[6] can register 4D surfaces and produce an urshape template.
“[6] that can register 4D surfaces and produce an urshape template”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [6]
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 [6]
[6], [28], [79] constructs a template, an urshape, by accumulating and filling in missing data from other scans in the same sequence. They have been used extensively in non-rigid registration to constrain dense correspondences. This works for objects with well defined forms, such as the human face and body.
“[6], [28], [79] constructs a template, an urshape, by accumulating and filling in missing data from other scans in the same sequence.They have been used extensively in non-rigid registration to constrain dense correspondences [23], [29], [32], [34], [80].This works for objects with well defined forms, such as the human face and body.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [6]
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 [6]
Cites
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163 · see citing works
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,281 · see citing works
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Shewchuk, J. R. (1994) · Cited by 2,193 · see citing works
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(2001) · 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.2001.990916 · Cited by 1,884 · see citing works
Terzopoulos, D., Platt, J. C., Barr, A. H., & Fleischer, K. (1987) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/37401.37427 · Cited by 1,716 · see citing works
SCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489 · see citing works
High-quality video view interpolation using a layered representation
Zitnick, C. L., Kang, S. B., Uyttendaele, M., Winder, S. A. J., & Szeliski, R. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015766 · Cited by 1,386 · see citing works
Nonlinear Programming: Analysis and Methods
Avriel, M. (1976) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,307 · see citing works
Nonlinear Programming Analysis and Methods
Rodin, E. Y. (1977) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(77)90071-2 · Cited by 1,285 · see citing works
The space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913 · see citing works
The space of human body shapes
Allen, B., Curless, B., & Popović, Z. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882311 · Cited by 725 · see citing works
Zwicker, M., Pfister, H., van Baar, J., & Groß, M. H. (2001) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/383259.383300 · Cited by 674 · see citing works
On Linear Variational Surface Deformation Methods
Botsch, M., & Sorkine, O. (2007) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2007.1054 · Cited by 593 · see citing works
Performance capture from sparse multi-view video
de Aguiar, E., Stoll, C., Theobalt, C., Ahmed, N., Seidel, H., & Thrun, S. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360697 · Cited by 535 · see citing works
Self-citationZhang, L., Snavely, N., Curless, B., & Seitz, S. M. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015759 · Cited by 493 · see citing works
Free-viewpoint video of human actors
Carranza, J., Theobalt, C., Magnor, M. A., & Seidel, H. (2003) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1201775.882309 · Cited by 485 · see citing works
Self-citationEmbedded deformation for shape manipulation
Sumner, R. W., Schmid, J., & Pauly, M. V. (2007) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1275808.1276478 · Cited by 459 · see citing works
A Computational Framework for Segmentation and Grouping
(2000) · Elsevier eBooks · doi:10.1016/b978-0-444-50353-4.x5000-8 · Cited by 415 · see citing works
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1186822.1073207 · Cited by 410 · see citing works
Global Correspondence Optimization for Non‐Rigid Registration of Depth Scans
Li, H., Sumner, R. W., & Pauly, M. V. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01282.x · Cited by 401 · see citing works
Point based animation of elastic, plastic and melting objects
M�ller, M., Keiser, R., Nealen, A., Pauly, M. V., Groß, M. H., & Alexa, M. (2004) · Computer animation and simulation · doi:10.1145/1028523.1028542 · Cited by 388 · see citing works
High-quality video view interpolation using a layered representation
Zitnick, C. L., Kang, S. B., Uyttendaele, M., Winder, S., & Szeliski, R. (2004) · ACM SIGGRAPH 2004 Papers · doi:10.1145/1186562.1015766 · Cited by 367 · see citing works
Large mesh deformation using the volumetric graph Laplacian
Zhou, K., Huang, J., Snyder, J., Liu, X., Bao, H., Guo, B., & Shum, H. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073219 · Cited by 316 · see citing works
Spacetime stereo: shape recovery for dynamic scenes
Zhang, L., Curless, B., & Seitz, S. M. (2003) · 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.2003.1211492 · Cited by 294 · see citing works
Fast 3D Scanning with Automatic Motion Compensation
Weise, T., Leibe, B., & Gool, L. V. (2007) · 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.2007.383291 · Cited by 290 · see citing works
Non‐Rigid Registration Under Isometric Deformations
Huang, Q., Adams, B., Wicke, M., & Guibas, L. J. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01285.x · Cited by 279 · see citing works
Self-citationArticulated body deformation from range scan data
Allen, B., Curless, B., & Popović, Z. (2002) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/566570.566626 · Cited by 272 · see citing works
Spacetime stereo: a unifying framework for depth from triangulation
Davis, J., Neh, D., Ramamoorthi, R., & Rusinkiewicz, S. M. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.37 · Cited by 271 · see citing works
Sumner, R. W., Zwicker, M., Gotsman, C., & Popović, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073218 · Cited by 266 · see citing works
Subspace gradient domain mesh deformation
Huang, J., Shi, X., Liu, X., Zhou, K., Wei, L., Teng, S., Bao, H., Guo, B., & Shum, H. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1142003 · Cited by 264 · see citing works
Classification and Overview of Meshfree Methods
Fries, T., & Matthies, H. G. (2004) · Digitale Bibliothek Braunschweig (Verbundzentrale Göttingen (VZG)) · doi:10.24355/dbbs.084-200511080100-465 · Cited by 250 · see citing works
The ACM SIGGRAPH / Eurographics Symposium on Computer Animation
(2021) · doi:10.1145/3475946 · Cited by 249 · see citing works
Meshless animation of fracturing solids
Pauly, M. V., Keiser, R., Adams, B., Dutré, P., Groß, M. H., & Guibas, L. J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073296 · Cited by 208 · see citing works
Self-citationCapturing and animating skin deformation in human motion
Park, S. I., & Hodgins, J. K. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141970 · Cited by 202 · see citing works
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces
Anguelov, D., Srinivasan, P., Pang, H.-C., Koller, D., Thrun, S., & Davis, J. (2004) · Cited by 197 · see citing works
PriMo : coupled prisms for intuitive surface modeling
Botsch, M., Pauly, M. V., Groß, M. H., & Kobbelt, L. P. (2006) · Cited by 178 · see citing works
Adaptive Space Deformations Based on Rigid Cells
Botsch, M., Pauly, M. V., Wicke, M., & Groß, M. H. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01056.x · Cited by 125 · see citing works
Zhang, L., Snavely, N., Curless, B., & Seitz, S. M. (2004) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1186562.1015759 · Cited by 123 · see citing works
Carceroni, R. L., & Kutulakos, K. N. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020145606604 · Cited by 117 · see citing works
A fast multigrid algorithm for mesh deformation
Shi, L., Yu, Y., Bell, N., & Feng, W.-W. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1142001 · Cited by 116 · see citing works
Articulated Object Reconstruction and Markerless Motion Capture from Depth Video
Pekelny, Y., & Gotsman, C. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01137.x · Cited by 114 · see citing works
Pyramid Coordinates for Morphing and Deformation
Sheffer, A., & Kraevoy, V. (2004) · Cited by 110 · see citing works
Continuous capture of skin deformation
Sand, P., McMillan, L., & Popović, J. (2003) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1201775.882310 · Cited by 108 · see citing works
Automatic Registration for Articulated Shapes
Chang, W., & Zwicker, M. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01286.x · Cited by 107 · see citing works
Dual Laplacian editing for meshes
Au, O. K., Tai, C., Liu, L., & Fu, H. (2006) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2006.47 · Cited by 105 · see citing works
Reconstruction of deforming geometry from time-varying point clouds
Wand, M., Jenke, P., Qi-xing Huang, A., Bokeloh, M., Guibas, L. J., & Schilling, A. (2007) · doi:10.5555/1281991.1281998 · Cited by 102 · see citing works
Self-citationMitra, N. J., Flöry, S., Ovsjanikov, M., Gelfand, N., Guibas, L. J., & Pottmann, H. (2007) · doi:10.5555/1281991.1282015 · Cited by 101 · see citing works
Self-citationReconstructing Animated Meshes from Time‐Varying Point Clouds
Süßmuth, J., Winter, M., & Greiner, G. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01287.x · Cited by 81 · see citing works
Würmlin, S., Lamboray, E., Staadt, O., & Groß, M. H. (2003) · Pacific Conference on Computer Graphics and Applications · doi:10.1109/pccga.2002.1167876 · Cited by 79 · see citing works
Shi, X., Zhou, K., Tong, Y., Desbrun, M., Bao, H., & Guo, B. (2007) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1275808.1276479 · Cited by 77 · see citing works
Temporal Surface Tracking Using Mesh Evolution
Varanasi, K., Zaharescu, A., Boyer, E., & Horaud, R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_3 · Cited by 77 · see citing works
Dense correspondence finding for parametrization-free animation reconstruction from video
Ahmed, N., Theobalt, C., Rössl, C., Thrun, S., & Seidel, H. (2008) · 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.2008.4587758 · Cited by 75 · see citing works
Self-citationSpace-time surface reconstruction using incompressible flow
Sharf, A., Alcantara, D. A., Lewiner, T., Greif, C., Sheffer, A., Amenta, N., & Cohen‐Or, D. (2008) · ACM Transactions on Graphics · doi:10.1145/1409060.1409063 · Cited by 73 · see citing works
Continuous capture of skin deformation
Sand, P., McMillan, L., & Popović, J. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882310 · Cited by 71 · see citing works
Gradient domain editing of deforming mesh sequences
Xu, W., Zhou, K., Yu, Y., Tan, Q., Peng, Q., & Guo, B. (2007) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1275808.1276482 · Cited by 69 · see citing works
Proceedings of the Vision Modeling and Visualization Conference 2001
Ertl, T., Girod, B., Niemann, H., & Seidel, H. (2001) · Cited by 64 · see citing works
Self-citationSpherical matching for temporal correspondence of non-rigid surfaces
Starck, J., & Hilton, A. (2005) · doi:10.1109/iccv.2005.229 · Cited by 47 · see citing works
Meshless Modeling of Deformable Shapes and their Motion
Adams, B., Ovsjanikov, M., Wand, M., Seidel, H., & Guibas, L. J. (2008) · PubMed · doi:10.2312/sca/sca08/077-086 · Cited by 38 · see citing works
Self-citationPhysically Based Deformable Models in Computer Graphics
Nealen, A., Müller, M., Keiser, R., Boxerman, E., & Carlson, M. (2005) · doi:10.2312/egst.20051045 · Cited by 33 · see citing works
Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype
Huang, Q., Adams, B., & Wand, M. D. (2007) · Max Planck Institute for Plasma Physics · doi:10.2312/sgp/sgp07/213-223 · Cited by 26 · see citing works
Self-citationExtracting Animated Meshes with Adaptive Motion Estimation.
Anuar, N., & Guskov, I. (2004) · Vision Modeling and Visualization · Cited by 24 · see citing works
High-resolution three-dimensional sensing of fast deforming objects
Fong, P. W. L., & Buron, F. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1545602 · Cited by 20 · see citing works
Image-Based Motion Compensation for Structured Light Scanning of Dynamic Surfaces
Gumhold, S., & König, S. (2008) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/dagsemproc.07171.3 · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time
Newcombe, R. A., Fox, D., & Seitz, S. M. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298631 · Cited by 951 · see citing works
Industry 4.0 technologies and their applications in fighting COVID-19 pandemic
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.032 · Cited by 758 · see citing works
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
Tam, G. K. L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X.-F., & Rosin, P. L. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.310 · Cited by 679 · see citing works
“Template DC, D [6], [23], [28], [34]”
BackgroundMethodInfluentialA Survey on Shape Correspondence
van Kaick, O. M., Zhang, H., Hamarneh, G., & Cohen‐Or, D. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.01884.x · Cited by 606 · see citing works
High-quality streamable free-viewpoint video
Collet, A., Chuang, M., Sweeney, P., Gillett, D., Evseev, D., Calabrese, D., Hoppe, H., Kirk, A. G., & Sullivan, S. (2015) · ACM Transactions on Graphics · doi:10.1145/2766945 · Cited by 577 · see citing works
Dou, M., Khamis, S., Degtyarev, Y., Davidson, P., Fanello, S., Kowdle, A., Escolano, S. O., Rhemann, C., Kim, D., Taylor, J., et al. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925969 · Cited by 500 · see citing works
“Other nonrigid tech-121 niques remove the need for a shape or template prior, but assume small and smooth motions [Zeng et al. 2013; Wand et al. 2009; Mitra et al. 2007]; or deal with topology changes in the input data (e.g., the fusing and then separation of hands) but suffer from drift and…”
BackgroundReal-time non-rigid reconstruction using an RGB-D camera
Zollhöfer, M., Nießner, M., Izadi, S., Rehmann, C., Zach, C., Fisher, M., Wu, C., Fitzgibbon, A. W., Loop, C., Theobalt, C., et al. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601165 · Cited by 405 · see citing works
Robust single-view geometry and motion reconstruction
Li, H., Adams, B., Guibas, L. J., & Pauly, M. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618521 · Cited by 334 · see citing works
Self-citationState of the Art on 3D Reconstruction with RGB‐D Cameras
Zollhöfer, M., Stotko, P., Görlitz, A., Theobalt, C., Nießner, M., Klein, R., & Kolb, A. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13386 · Cited by 336 · see citing works
Point Cloud Skeletons via Laplacian Based Contraction
Cao, J., Tagliasacchi, A., Olson, M., Zhang, H., & Su, Z. (2010) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/smi.2010.25 · Cited by 333 · see citing works
Yu, T., Zheng, Z., Guo, K., Zhao, J., Dai, Q., Li, H., Pons‐Moll, G., & Liu, Y. (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.2018.00761 · Cited by 270 · see citing works
“…many meth-ods have been proposed to address free-form capture: linear variational deformation [20], deformation graph [18], sub-space deformation [40], articulate deformation [7, 8] and [29], 4D spatio-temporal surface [26] and [36], incompress-ible flows [33], animation cartography [38],…”
BackgroundReconstruction of Personalized 3D Face Rigs from Monocular Video
Garrido, P., Zollhöfer, M., Casas, D., Valgaerts, L., Varanasi, K., Pérez, P., & Theobalt, C. (2016) · ACM Transactions on Graphics · doi:10.1145/2890493 · Cited by 260 · see citing works
“For instance, algorithms that use dense camera arrays and dense lighting arrays to reconstruct face geometry, facial performance, and/or face appearance were developed [Beeler et al. 2010, 2011; Alexander et al. 2009].”
MethodInfluentialDynamic shape capture using multi-view photometric stereo
Vlasic, D., Peers, P., Baran, I., Debevec, P. E., Popović, J., Rusinkiewicz, S. M., & Matusik, W. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618520 · Cited by 239 · see citing works
High-quality passive facial performance capture using anchor frames
Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P. A., Gotsman, C., Sumner, R. W., & Groß, M. H. (2011) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1964921.1964970 · Cited by 239 · see citing works
“Other passive deformable surface reconstruction techniques have been applied to faces [Wand et al. 2009; Popa et al. 2010].”
MethodHigh-quality passive facial performance capture using anchor frames
Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P. A., Gotsman, C., Sumner, R. W., & Groß, M. H. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964970 · Cited by 224 · see citing works
Dense Human Body Correspondences Using Convolutional Networks
Wei, L., Qi-xing Huang, A., Ceylan, D., Vouga, E., & Li, H. (2016) · 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.2016.171 · Cited by 184 · see citing works
“Unlike existing methods [40, 23, 45, 42] which track a template using the previour method [Li et al.”
MethodBodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera
Yu, T., Guo, K., Xu, F., Dong, Y., Su, Z., Zhao, J., Li, J., Dai, Q., & Liu, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.104 · Cited by 179 · see citing works
“, linear variational deformation in [22], embedded deformation in [36, 20], and subspace deformation in [40].”
BackgroundCoregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape
Hirshberg, D. A., Loper, M., Rachlin, E., & Black, M. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_18 · Cited by 173 · see citing works
Li, H., Vouga, E., Gudym, A., Luo, L., Barron, J. T., & Gusev, G. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508407 · Cited by 162 · see citing works
“Several dynamic shape reconstruction techniques [Mitra et al. 2007; Wand et al. 2009] do not require any shape prior, but assume the motion of the subject to be very small and smooth.”
MethodReal-Time Geometry, Albedo, and Motion Reconstruction Using a Single RGB-D Camera
Guo, K., Xu, F., Yu, T., Liu, X., Dai, Q., & Liu, Y. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3083722 · Cited by 144 · see citing works
“However, non-rigid registration techniques, which generally assume multi-view scans [28] or involve additional motion priors [35], are also applicable to this task.”
BackgroundLightweight binocular facial performance capture under uncontrolled lighting
Valgaerts, L., Wu, C., Bruhn, A., Seidel, H., & Theobalt, C. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366206 · Cited by 143 · see citing works
Self-citation3D scanning deformable objects with a single RGBD sensor
Dou, M., Taylor, J., Fuchs, H., Fitzgibbon, A. W., & Izadi, S. (2015) · 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.2015.7298647 · Cited by 139 · see citing works
“More general approaches either work on diverse (non-rigged) templates [8, 4, 12, 10], or use template-less spatio-temporal representations [13, 22, 17].”
BackgroundRecent developments and trends in point set registration methods
Maiseli, B. J., Gu, Y., & Gao, H. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.03.012 · Cited by 120 · see citing works
Analyzing growing plants from 4D point cloud data
Li, Y., Fan, X., Mitra, N. J., Chamovitz, D., Cohen‐Or, D., & Chen, B. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508368 · Cited by 117 · see citing works
“In such cases, shapes and deformations can be directly recovered without the need for any prior template priors [Mitra et al. 2007; Liao et al. 2009; Wand et al. 2009; Popa et al. 2010; Tevs et al. 2012; Akhter et al. 2012].”
BackgroundLearning an efficient model of hand shape variation from depth images
Khamis, S., Taylor, J., Shotton, J., Keskin, C., Izadi, S., & Fitzgibbon, A. W. (2015) · 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.2015.7298869 · Cited by 115 · see citing works
“Learning a lower dimensional parametrization of shape from examples of range scans or other 3D data has proved effective in creating generic morphable models for human bodies and faces [7, 4, 5, 13, 10, 29, 17, 27, 30, 16, 31, 19].”
BackgroundA Survey of Non‐Rigid 3D Registration
Deng, B., Yao, Y., Dyke, R. M., & Zhang, J. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14502 · Cited by 118 · see citing works
Temporally coherent completion of dynamic shapes
Li, H., Luo, L., Vlasic, D., Peers, P., Popović, J., Pauly, M. V., & Rusinkiewicz, S. M. (2012) · ACM Transactions on Graphics · doi:10.1145/2077341.2077343 · Cited by 109 · see citing works
“For efficiency, we operate on down-sampled [Wand et al. 2009] our method”
MethodData-driven shape analysis and processing
Xu, K., Kim, V. G., Qi-xing Huang, A., Mitra, N. J., & Kalogerakis, E. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988473 · Cited by 104 · see citing works
Zimmerling, A., & Chen, D. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101541 · Cited by 105 · see citing works
“These technologies provide the ability to design customized products in real-time, while 3D printing technologies allow for production of products on-site and on-demand by a wide-range of individuals and corporations due to the accessibility of 3D printing technologies [11,35].”
BackgroundKinectAvatar: Fully Automatic Body Capture Using a Single Kinect
Cui, Y., Chang, W., Nöll, T., & Stricker, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37484-5_12 · Cited by 94 · see citing works
“Aligning these scans is a challenging task that often requires high quality scan data and small changes of the pose in each scan [3][16][17][18][19][20][21].”
BackgroundDynamic shape capture using multi-view photometric stereo
Vlasic, D., Peers, P., Baran, I., Debevec, P. E., Popović, J., Rusinkiewicz, S. M., & Matusik, W. (2009) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1661412.1618520 · Cited by 89 · see citing works
“…is ideally suited as input to algorithms that perform temporal registration and merging of geometry, which have recently received considerable interest in the geometry processing community [Wand et al. 2007; Huang et al. 2008; Li et al. 2008; Sharf et al. 2008; Zhang et al. 2008; Wand et al. 2009].”
BackgroundMethodGlobal 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
“This optimization included joint constraints that preserves the connectivity between parts and automatically handled cases when parts are disappear or [Wand et al. 2009] Our Method”
MethodAnimation cartography—intrinsic reconstruction of shape and motion
Tevs, A., Berner, A., Wand, M., Ihrke, I., Bokeloh, M., Kerber, J., & Seidel, H. (2012) · ACM Transactions on Graphics · doi:10.1145/2159516.2159517 · Cited by 87 · see citing works
“Therefore, we combine the intrinsic manifold charting with a state-of-the-art extrinsic reconstruction scheme [Wand et al. 2009] that computes actual geometry.”
MethodResultInfluentialSelf-citationDeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data
Božič, A., Zollhöfer, M., Theobalt, C., & NieBner, M. (2020) · 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/cvpr42600.2020.00703 · Cited by 87 · see citing works
“Non-Rigid Reconstruction The reconstruction of general non-rigidly deforming objects based on real-time scan data has a long tradition [44].”
BackgroundFarhan, M., Wang, J. Z., Bray, P., Burns, J., & Cheng, T. L. (2021) · Journal of Foot and Ankle Research · doi:10.1186/s13047-020-00442-8 · Cited by 86 · see citing works
“The digitised data can be used to create a 3D model of the patient and/or their device [8].”
MethodUser-Specific Hand Modeling from Monocular Depth Sequences
Taylor, J., Stebbing, R. V., Ramakrishna, V., Keskin, C., Shotton, J., Izadi, S., Hertzmann, A., & Fitzgibbon, A. W. (2014) · 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.2014.88 · Cited by 79 · see citing works
“Wand et al. [29] demonstrate impressive results on noisy range sequences, matching an evolving core model to incoming range images.”
BackgroundRobust single-view geometry and motion reconstruction
Li, H., Adams, B., Guibas, L. J., & Pauly, M. V. (2009) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1661412.1618521 · Cited by 76 · see citing works
“The method of [Wand et al. 2009] that avoids the use of a template cannot track the motion of the fingers accurately.”
MethodSelf-citationGlobal Non-rigid Alignment of Surface Sequences
Budd, C. J., Huang, P. F., Klaudiny, M., & Hilton, A. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0553-4 · Cited by 69 · see citing works
“…2003; Vlasic et al. 2008); and surfacetracking or scene flow approaches which do not assume prior knowledge of the surface structure allowing alignment of freeform non-rigid surfaces (Cagniart et al. 2010a; Starck and Hilton 2005, 2007a; Tevs et al. 2011; Vedula et al. 2005; Wand et al. 2009).”
BackgroundConsensus 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
Zeng, M., Zheng, J., Cheng, X., & Liu, X. (2013) · 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.2013.26 · Cited by 67 · see citing works
“[26, 23]) which leverage few assumptions on particular objects, but these methods take little care about loop-closure, a crucial issue on object reconstruction.”
BackgroundQuasi-Newton Solver for Robust Non-Rigid Registration
Yao, Y., Deng, B., Xu, W., & Zhang, J. (2020) · 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/cvpr42600.2020.00762 · Cited by 60 · see citing works
“Also with a statistical framework, Wand et al.[34] used a meshless deformation model to perform the pairwise alignment.”
MethodTracking surfaces with evolving topology
Bojsen-Hansen, M., Li, H., & Wojtan, C. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185549 · Cited by 59 · see citing works
Yu, T., Zhao, J., Zheng, Z., Guo, K., Dai, Q., Li, H., Pons‐Moll, G., & Liu, Y. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2928296 · Cited by 58 · see citing works
“…many methods have been proposed to address free-form capture: linear variational deformation [36], deformation graph [37], subspace deformation [38], articulate deformation [39], [40] and [41], 4D spatio-temporal surface [42] and [43], incompressible flows [44], animation cartography [45],…”
MethodOn Computing Mapping of 3D Objects
Li, X. S., & Iyengar, S. S. (2014) · ACM Computing Surveys · doi:10.1145/2668020 · Cited by 56 · see citing works
“Therefore, they are widely applied in registering dynamically deforming surface sequences [Wand et al. 2007; Pekelny and Gotsman 2008; Wand et al. 2009; Li et al. 2009; Allen et al. 2003; Zhang 1994; Rusinkiewicz and Levoy 2001], in which case one can assume that the adjacent frames do not change…”
BackgroundTemporal upsampling of performance geometry using photometric alignment
Wilson, C. A., Ghosh, A., Peers, P., Chiang, J.-Y., Busch, J., & Debevec, P. E. (2010) · ACM Transactions on Graphics · doi:10.1145/1731047.1731055 · Cited by 54 · see citing works
Kaur, I., Behl, T., Aleya, L., Rahman, H., Kumar, A., Arora, S., & Bulbul, I. J. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-13823-8 · Cited by 47 · see citing works
“…AI-based video surveillance minimizes the workload of health care employees, especially during the pandemic, and helps in better understanding of the effect of virus on the population (Moeslund and Granum 2001; Wand et al. 2009; Sampol 2020; Kim et al. 2018; Pejcic et al. 2006; Ren et al. 2020).”
BackgroundGlobally Consistent Space‐Time Reconstruction
Popa, T., South‐Dickinson, I., Bradley, D., Sheffer, A., & Heidrich, W. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2010.01772.x · Cited by 47 · see citing works
Masci, J., Rodolà, E., Boscaini, D., Bronstein, M. M., & Li, H. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988485 · Cited by 46 · see citing works
Space-time tomography for continuously deforming objects
Zang, G., Idoughi, R., Tao, R., Gilles Lubineau, A., Wonka, P., & Heidrich, W. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201298 · Cited by 46 · see citing works
“2008], or assume slow deformations that ensure large overlapping regions between successive scans [Li et al. 2012; Mitra et al. 2007; Wand et al. 2009].”
BackgroundMethodRobust Non-Rigid Registration with Reweighted Position and Transformation Sparsity
Li, K., Yang, J., Lai, Y., & Guo, D. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2832136 · Cited by 43 · see citing works
Video Normals from Colored Lights
Brostow, G., Hernández, C., Vogiatzis, G., Stenger, B., & Cipolla, R. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.37 · Cited by 41 · see citing works
“Wand et al. [46] present an impressive optimization system for computing a single shape and its time-varying deformation function from a sequence of point clouds (as many as 201 frames).”
Balloon Shapes: Reconstructing and Deforming Objects with Volume from Images
Vicente, S., & Agapito, L. (2013) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2013.37 · Cited by 40 · see citing works
“One way to overcome the limitations of NRSfM is to manipulate the acquisition system and increase the amount of information available, by using, for example, multiple synchronized views of the object [25] or 3D scanner data [28].”
MethodCapturing Dynamic Textured Surfaces of Moving Targets
Wang, R., Wei, L., Vouga, E., Qi-xing Huang, A., Ceylan, D., Medioni, G., & Li, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_17 · Cited by 38 · see citing works
“A follow-up work [22] proposes to separate the deformation models used for geometry and motion capture.”
BackgroundMeshless Modeling of Deformable Shapes and their Motion
Adams, B., Ovsjanikov, M., Wand, M., Seidel, H., & Guibas, L. J. (2008) · PubMed · doi:10.2312/sca/sca08/077-086 · Cited by 38 · see citing works
Self-citation4D parametric motion graphs for interactive animation
Casas, D., Tejera, M., Guillemaut, J., & Hilton, A. (2012) · ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) · doi:10.1145/2159616.2159633 · Cited by 37 · see citing works
“…align a prior model of the surface with successive frames [Carranza et al. 2003; de Aguiar et al. 2008; Vlasic et al. 2008]; and surface-tracking or scene .ow approaches which do not assume prior knowledge of the surface structure [Cagniart et al. 2010; Vedula et al. 2005; Wand et al. 2009].”
BackgroundModern techniques and applications for real-time non-rigid registration
Bouaziz, S., Tagliasacchi, A., Li, H., & Pauly, M. V. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988490 · Cited by 35 · see citing works
“Unlike existing methods [38, 23, 47, 41, 39, 42] which track a template using the previous frame, in order to avoid potential drifts, the template model is deformed from its canonical rest pose using the computed full-to-partial correspondences.”
MethodDeformation-driven topology-varying 3D shape correspondence
Alhashim, I., Xu, K., Zhuang, Y., Cao, J., Simari, P. D., & Zhang, H. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818088 · Cited by 32 · see citing works
“Earlier approaches have mostly been designed to deal with rigid alignments [Gelfand et al. 2005; Aiger et al. 2008], near-isometric shape articulations [Huang et al. 2008; Kim et al. 2011], or non-rigid shape deformations involving part stretching [Zhang et al. 2008; Wand et al. 2009].”
BackgroundMethodSuperMatching: Feature Matching Using Supersymmetric Geometric Constraints
Cheng, Z., Chen, Y., Martin, R. R., Lai, Y., & Wang, A. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.15 · Cited by 32 · see citing works
“You a r e a dvise d to cons ult t h e p u blish e r’s version if you wish to cit e t his p a p er.”
BackgroundFast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization
Yao, Y., Deng, B., Xu, W., & Zhang, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3247603 · Cited by 29 · see citing works
“[30] used a meshless deformation model to perform the pairwise alignment.”
MethodInteractive Animation of 4D Performance Capture
Casas, D., Tejera, M., Guillemaut, J., & Hilton, A. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.314 · Cited by 28 · see citing works
Transferring Category-Based Functional Grasping Skills by Latent Space Non-Rigid Registration
Rodriguez, D., & Behnke, S. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2826057 · Cited by 28 · see citing works
Sparse Non‐rigid Registration of 3D Shapes
Yang, J., Li, K., Li, K., & Lai, Y. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12699 · Cited by 27 · see citing works
Proactive 3D scanning of inaccessible parts
Yan, F., Sharf, A., Wenzhen, L., Huang, H. F., & Chen, B. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601191 · Cited by 27 · see citing works
“In recent years numerous methods have been introduced for dynamic 3D registration and reconstruction, presenting a wide range of solutions [Huang et al. 2008; Pekelny and Gotsman 2008; Sharf et al. 2008; Süssmuth et al. 2008; de Aguiar et al. 2008; Wand et al. 2009; Li et al. 2009; Chang and Zwicker 2011; Li et al. 20…”
MethodInfluentialden Herrewegen, I. V., Cuppens, K., Broeckx, M., Barisch‐Fritz, B., Sloten, J. V., Leardini, A., & Peeraer, L. (2014) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2014.06.010 · Cited by 26 · see citing works
“Given the deformability of the foot (Kouchi et al., 2009; Okita et al., 2009; Wand et al., 2009), other methods such s nonrigid ICP or deformable shape models might result in a higher reproducibility of the joint rotations calculated from the scan data.”
MethodTransferring Grasping Skills to Novel Instances by Latent Space Non-Rigid Registration
Rodríguez, D., Cogswell, C., Koo, S., & Behnke, S. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461169 · Cited by 25 · see citing works
Limit Shapes – A Tool for Understanding Shape Differences and Variability in 3D Model Collections
Huang, R., Achlioptas, P., Guibas, L. J., & Ovsjanikov, M. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13799 · Cited by 25 · see citing works
Self-citationDeformable registration using patch-wise shape matching
Bonarrigo, F., Signoroni, A., & Botsch, M. (2014) · Graphical Models · doi:10.1016/j.gmod.2014.04.004 · Cited by 24 · see citing works
“Under the gradual deformation hypothesis, template-free methods that guarantee global consistency of the space-time object reconstruction and prevent erroneous topology changes have been proposed in [10, 11], while the template-based approach [12] has been proposed in the same perspective.”
MethodConvex Hull Aided Registration Method (CHARM)
Fan, J., Yang, J., Zhao, Y., Ai, D., Liu, Y., Wang, G., & Wang, Y. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2602858 · Cited by 24 · see citing works
Robust Non-Rigid Motion Tracking and Surface Reconstruction Using $L_0$ Regularization
Guo, K., Xu, F., Wang, Y., Liu, Y., & Dai, Q. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2688331 · Cited by 24 · see citing works
“[40] applied a subspace deformation method to generate dense correspondences of the sequence and a complete geometry of the object.”
Li, C., Zhao, Z., & Guo, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_20 · Cited by 21 · see citing works
“[28] proposed an algorithm to align and merge pairs of adjacent frames in a hierarchical fashion to gradually build the template shape.”
BackgroundA Condition Number for Non‐Rigid Shape Matching
Ovsjanikov, M., Qi-xing Huang, A., & Guibas, L. J. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.02024.x · Cited by 18 · see citing works
Self-citationTowards Optimal Non-rigid Surface Tracking
Klaudiny, M., Budd, C., & Hilton, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_53 · Cited by 18 · see citing works
“Wand et al.[9] propose so-called urshape representing the surface and optimise its time-varying deformation field to fit a point cloud sequence.”
BackgroundSpatio‐temporal geometry fusion for multiple hybrid cameras using moving least squares surfaces
Kuster, C., Bazin, J., Öztireli, C., Deng, T., Martin, T., Popa, T., & Groß, M. H. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12285 · Cited by 18 · see citing works
A survey on image-based continuum-body motion estimation
Liu, W., & Ribeiro, E. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.03.003 · Cited by 21 · see citing works
A low-dimensional representation for robust partial isometric correspondences computation
Brunton, A., Wand, M., Wuhrer, S., Seidel, H., & Weinkauf, T. (2013) · Graphical Models · doi:10.1016/j.gmod.2013.11.003 · Cited by 16 · see citing works
“(6) to the 3-manifold of the embedding Euclidean volume [39, 40].”
Self-citationRobust 3D Human Motion Reconstruction via Dynamic Template Construction
Li, Z., Ji, Y., Yang, W. H., Ye, J., & Jingyi Yu, A. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00063 · Cited by 15 · see citing works
“They further improves the template-free shape alignment by using volumetric deformation model [44].”
MethodA Low-dimensional Representation for Robust Partial Isometric Correspondences Computation
Brunton, A., Wand, M., Wuhrer, S., Seidel, H., & Weinkauf, T. (2014) · Max Planck Digital Library · Cited by 15 · see citing works
Self-citationAnimation Control of Surface Motion Capture
Tejera, M., Casas, D., & Hilton, A. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2260328 · Cited by 14 · see citing works
Parametric Control of Captured Mesh Sequences for Real-Time Animation
Casas, D., Tejera, M., Guillemaut, J., & Hilton, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25090-3_21 · Cited by 13 · see citing works
“These can be categorised into two methodologies: model-based approaches which align a prior model of the surface with successive frames [7,9,32]; and surface-tracking or scene flow approaches which do not assume prior knowledge of the surface structure [6,31,33].”
MethodNon-rigid Registration in 3D Implicit Vector Space
Cheng, Z., Jiang, W., Dang, G., Martin, R. R., Li, J., Li, H., Chen, Y., Wang, Y., Li, B., Xu, K., et al. (2010) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/smi.2010.21 · Cited by 13 · see citing works
Dynamic 3D facial expression modeling using Laplacian smooth and multi-scale mesh matching
Chi, J., Tu, C., & Zhang, C. (2014) · The Visual Computer · doi:10.1007/s00371-014-0960-3 · Cited by 12 · see citing works
Mobility Fitting using 4D RANSAC
Li, H., Wan, G., Li, H., Sharf, A., Xu, K., & Chen, B. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12965 · Cited by 12 · see citing works
Application of industry 4.0 technology in containing Covid-19 spread and its challenges
Paranitharan, K. P., Ebenezer, G., Balaji, V., Khan, M., & Babu, T. R. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.06.009 · Cited by 11 · see citing works
Fragmentation Guided Human Shape Reconstruction
Zhang, Y., Luo, X., Yang, W., & Jingyi Yu, A. (2019) · IEEE Access · doi:10.1109/access.2019.2905879 · Cited by 10 · see citing works
Huber-$$L_1$$-based non-isometric surface registration
Jiang, T., Yang, X., Zhang, J., Tian, F., Liu, S., Xiang, N., & Qian, K. (2019) · The Visual Computer · doi:10.1007/s00371-019-01670-1 · Cited by 10 · see citing works
Su, Y.-H., Huang, K., & Hannaford, B. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968489 · Cited by 10 · see citing works
“created deformation fields to fit data during optimization for non-rigid registration [17], [18].”
MethodFull 3D Reconstruction of Non-Rigidly Deforming Objects
Afzal, H., Aouada, D., Mirbach, B., & Ottersten, B. (2018) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3177756 · Cited by 10 · see citing works
“Wand et al. use a topology-aware adaptive sub-space deformation technique to reduce the drift, together with as-rigid-as-possible and temporally coherent constraints on motion, to establish correspondences between acquisitions in 3D videos [60, 61].”
BackgroundDistributed Consolidation of Highly Incomplete Dynamic Point Clouds Based on Rank Minimization
Vlachos, E., Lalos, A. S., Spathis-Papadiotis, A., & Moustakas, K. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2839911 · Cited by 9 · see citing works
“This template model is either provided by the user [21] or is being reconstructed by the data [22].”
MethodSüßmuth, J., Zollhöfer, M., & Greiner, G. (2010) · Computer Animation and Virtual Worlds · doi:10.1002/cav.364 · Cited by 8 · see citing works
Color-aware surface registration
Lin, S., Lai, Y., Martin, R. R., Jin, S., & Cheng, Z. (2016) · Computers & Graphics · doi:10.1016/j.cag.2016.05.007 · Cited by 8 · see citing works
Non-rigid Reconstruction with a Single Moving RGB-D Camera
Elanattil, S., Moghadam, P., Sridharan, S., Fookes, C., & Cox, M. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546201 · Cited by 8 · see citing works
“[14] proposed a point based surface representation for temporal fusion to build a template of dynamic objects.”
BackgroundScalable non-rigid registration for multi-view stereo data
Palma, G., Boubekeur, T., Ganovelli, F., & Cignoni, P. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.06.012 · Cited by 7 · see citing works
“maps [22, 23, 8], or explicit markers [24] or a template model to fit the range scan data [25, 26, 27].”
BackgroundSplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes
Li, Y., Zhang, T., Nakamura, Y., & Harada, T. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341082 · Cited by 7 · see citing works
“The reconstruction of general non-rigidly deforming objects/scenes based on real-time depth sensor data has a long tradition [19].”
BackgroundTemplate-Based Reconstruction of Surface Mesh Animation from Point Cloud Animation
Park, S. I., & Lim, S. (2014) · ETRI Journal · doi:10.4218/etrij.14.0113.1181 · Cited by 7 · see citing works
Review of fringe projection profilometry: from geometric triangulation to computational 3D imaging
Wu, Z., Guo, W., Chen, F., Chen, Z., Zhang, C., Li, Y., Liu, Y., Wang, Y., Su, X., Notni, G., et al. (2026) · Light Advanced Manufacturing · doi:10.37188/lam.2026.074 · Cited by 6 · see citing works
3D Modeling by Scanning Physical Modifications
Hattab, A., & Taubin, G. (2015) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2015.8 · Cited by 5 · see citing works
“In the case of non-rigid registration, some algorithms are based on segmenting the 3D models; some use skeletons (Allen [18]) or bones and joints (Chang [33]) for articulated movements; and others are based on using a common template (Wand [34]).”
MethodStructured Representation of Non-Rigid Surfaces from Single View 3D Point Tracks
Malleson, C., Klaudiny, M., Guillemaut, J., & Hilton, A. (2014) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2014.13 · Cited by 5 · see citing works
“Recovery of complete shape and motion from partial depth views without prior templates - has been demonstrated in [14] (using topology-aware adaptive subspace deformation) and in [11], in which topography-inspired landmark correspondences are used.”
BackgroundMethodLearning Spectral Unions of Partial Deformable 3D Shapes
Moschella, L., Melzi, S., Cosmo, L., Maggioli, F., Litany, O., Ovsjanikov, M., Guibas, L. J., & Rodolà, E. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14483 · Cited by 5 · see citing works
Self-citationBinocular photometric stereo acquisition and reconstruction for 3d talking head applications
Wang, C., Wang, L., Matsushita, Y., Huang, B., Chen, M., & Soong, F. K. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-630 · Cited by 5 · see citing works
“Structured light capture Similar to marker-based capture, structured light systems project known patterns on a face for densely measuring the shape [11] [12].”
Background
- Authors
- Wand, M., Adams, B., Ovsjanikov, M., Berner, A., Bokeloh, M., Jenke, P., Guibas, L., Seidel, H.-P., & Schilling, A.
- Affiliations
- Max Planck Institute for Informatics (DE)
- Stanford University (US)
- University of Tübingen (DE)
- Venue
- ACM Transactions on Graphics
- Published
- 2009-04
- Volume
- 28
- Issue
- 2
- Pages
- 1-15
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2065270678
- Cited by
- 136
- References
- 64
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 5 · 2020: 4 · 2019: 6 · 2018: 15 · 2017: 6 · 2016: 12 · 2015: 10 · 2014: 14 · 2013: 15 · 2012: 14
- FWCI
- 11.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Graphics (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency (HR0011-05-1-0007)
- National Science Foundation (ITR 0205671FRG 0354543)
- National Institutes of Health (GM -072970)
- 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
- deformation field estimation, 3D scanning, unstructured point clouds, shape fitting, noise and outlier removal, dense correspondence, real-time 3D scanning
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 ›
Are you an author, or do you see a problem with this page? Request a review
