Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera
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Abstract (arXiv)
We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to jointly optimize the surface and deformations. To represent and constrain the non-rigid deformations, we propose a novel neural invertible deforming network such that the cycle consistency between arbitrary two frames is automatically satisfied. Considering that the surface topology of dynamic scene might change over time, we employ a topology-aware strategy to construct the topology-variant correspondence for the fused frames. NDR also further refines the camera poses in a global optimization manner. Experiments on public datasets and our collected dataset demonstrate that NDR outperforms existing monocular dynamic reconstruction methods.
Key facts. Cited by 57 papers in the map (data through 14 Oct 2026). Builds on 40 of its 76 references that are in the map. First posted on arXiv 30 Jun 2022.
Builds on
40 of 76 references.
References · 40
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- 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
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- 2021AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesiscited by 339
- 2018Learning Category-Specific Mesh Reconstruction from Image Collectionscited by 317
- 2021Neural RGB-D Surface Reconstructioncited by 313
- 20163D Menagerie: Modeling the 3D shape and pose of animalscited by 312
- 2019Learning Correspondence from the Cycle-Consistency of Timecited by 269
- 2020PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstructioncited by 231
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- 2021HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fieldscited by 214
- 2021Neural Head Avatars from Monocular RGB Videoscited by 213
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- 2022SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Videocited by 144
- 2021Robust High-Resolution Video Matting with Temporal Guidancecited by 137
- 2021Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusioncited by 129
- 2021LASR: Learning Articulated Shape Reconstruction from a Monocular Videocited by 116
- 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
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- 2016VolumeDeform: Real-time Volumetric Non-rigid Reconstructioncited by 75
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- 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 57
- 2020Neural Non-Rigid Trackingcited by 49
- 2019Deep Non-Rigid Structure from Motioncited by 38
- 2020ShapeFlow: Learnable Deformations Among 3D Shapescited by 28
- 2022OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstructioncited by 27
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Citing papers per year: 2022: 2; 2023: 25; 2024: 19; 2025: 9; 2026: 2.
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