SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registration
Not on the map. Why?
Abstract (arXiv)
Existing optimization-based methods for non-rigid registration typically minimize an alignment error metric based on the point-to-point or point-to-plane distance between corresponding point pairs on the source surface and target surface. However, these metrics can result in slow convergence or a loss of detail. In this paper, we propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration. The symmetrized point-to-plane distance relies on both the positions and normals of the corresponding points, resulting in a more accurate approximation of the underlying geometry and can achieve higher accuracy than existing methods. To solve this optimization problem efficiently, we introduce an as-rigid-as-possible regulation term to estimate the deformed normals and propose an alternating minimization solver using a majorization-minimization strategy. Moreover, for effective initialization of the solver, we incorporate a deformation graph-based coarse alignment that improves registration quality and efficiency. Extensive experiments show that the proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency. The code is publicly available at https://github.com/yaoyx689/spare.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 91 references that are in the map. First posted on arXiv 30 May 2024.
Builds on
40 of 91 references.
References · 40
- 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
- 2022BEHAVE: Dataset and Method for Tracking Human Object Interactionscited by 246
- 2022Geometric Transformer for Fast and Robust Point Cloud Registrationcited by 200
- 2021BANMo: Building Animatable 3D Neural Models from Many Casual Videoscited by 195
- 20183D-CODED : 3D Correspondences by Deep Deformationcited by 160
- 2017Deep Functional Maps: Structured Prediction for Dense Shape Correspondencecited by 134
- 20214DComplete: Non-Rigid Motion Estimation Beyond the Observable Surfacecited by 123
- 2020LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registrationcited by 108
- 2019Efficient Learning on Point Clouds with Basis Point Setscited by 101
- 2018DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensorcited by 95
- 2021Lepard: Learning partial point cloud matching in rigid and deformable scenescited by 85
- 2021NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Gocited by 81
28 more
- 2019DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Datacited by 76
- 20193DN: 3D Deformation Networkcited by 60
- 2022A Survey of Non-Rigid 3D Registrationcited by 59
- 2022Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Cameracited by 57
- 2023Unsupervised Learning of Robust Spectral Shape Matchingcited by 57
- 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 56
- 2021Shape registration in the time of transformerscited by 51
- 2020Neural Non-Rigid Trackingcited by 49
- 2020CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Cloudscited by 47
- 2022Non-rigid Point Cloud Registration with Neural Deformation Pyramidcited by 41
- 2022Learned Vertex Descent: A New Direction for 3D Human Model Fittingcited by 37
- 2023Dynamic Point Fieldscited by 37
- 2020Robust 3D Self-portraits in Secondscited by 30
- 2021Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronizationcited by 30
- 2015Dense Human Body Correspondences Using Convolutional Networkscited by 26
- 2016FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Camerascited by 23
- 2023NICP: Neural ICP for 3D Human Registration at Scalecited by 21
- 2020Recurrent Multi-view Alignment Network for Unsupervised Surface Registrationcited by 19
- 2020Quasi-Newton Solver for Robust Non-Rigid Registrationcited by 17
- 2023Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registrationcited by 14
- 2018Robust 3D Human Motion Reconstruction Via Dynamic Template Constructioncited by 13
- 2022Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimizationcited by 12
- 2018ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Cameracited by 8
- 2018Topology-Aware Non-Rigid Point Cloud Registrationcited by 8
- 2022Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matchingcited by 8
- 2024DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstructioncited by 8
- 2020Topology-Change-Aware Volumetric Fusion for Dynamic Scene Reconstructioncited by 5
- 2024DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fieldscited by 5
Built on by
Citing papers per year: 2025: 1.
1 paper.