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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.
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In the citing paper
“[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]
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 [6]
In the citing paper
“[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]
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 [6]
Citations
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Cited by
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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
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
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
“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
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
Dou, M., Khamis, S., Degtyarev, Y. G., Davidson, P. L., 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 424
“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
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
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 (Print) · doi:10.1111/cgf.13386 · Cited by 349
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
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
“…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
“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
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 242
“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 232
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
“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.-H., Li, J., Dai, Q.-H., & Liu, Y.-B. (2017) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2017.104 · Cited by 198
“, 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
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
“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.-H., & Liu, Y.-B. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3083722 · Cited by 178
“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
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
“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., Gu, Y., & Gao, H.-J. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.03.012 · Cited by 128
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
“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
“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., & Zhang, J.-Y. (2022) · Computer graphics forum (Print) · doi:10.1111/cgf.14502 · Cited by 154
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
“For efficiency, we operate on down-sampled [Wand et al. 2009] our method”
MethodData-driven shape analysis and processing
Xu, K., Kim, V. G., Huang, Q., Mitra, N. J., & Kalogerakis, E. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988473 · Cited by 107
Zimmerling, A., & Chen, D. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101541 · Cited by 110
“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
“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
“…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
“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
“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
“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 100
“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
“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
“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
“…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
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
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
“[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
“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
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
“…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
“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
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
“…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
Masci, J., Rodolà, E., Boscaini, D., Bronstein, M. M., & Li, H. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988485 · Cited by 51
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
“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
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
“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
“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
“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
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
“…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. (2016) · SIGGRAPH ASIA Courses · doi:10.1145/2988458.2988490 · Cited by 40
“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
“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
“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
“[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
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
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
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
“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
“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
Rodriguez, D., Cogswell, C., Koo, S., & Behnke, S. (2018) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2018.8461169 · Cited by 37
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
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
“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
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
“[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
“[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
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
“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
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
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
“(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
“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
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
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
“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
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
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
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
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
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
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
“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
“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
“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
Color-aware surface registration
Lin, S., Lai, Y.-K., Martin, R. R., Jin, S., & Cheng, Z.-Q. (2016) · Computers & graphics · doi:10.1016/j.cag.2016.05.007 · Cited by 9
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
“[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 9
“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
“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
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
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
“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
“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
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
“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
Metadata
- 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 › 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
- deformation field estimation, 3D scanning, unstructured point clouds, shape fitting, noise and outlier removal, dense correspondence, real-time 3D scanning
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