DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction

• URL: http://arxiv.org/abs/2403.11586v2
• Date: Mon, 22 Jul 2024 12:16:22 GMT
• Title: DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction
• Authors: Yuxin Yao, Siyu Ren, Junhui Hou, Zhi Deng, Juyong Zhang, Wenping Wang,
• Abstract summary: This paper explores the problem of reconstructing temporally consistent surfaces from a 3D point cloud sequence without correspondence. We propose DynoSurf, an unsupervised learning framework integrating a template surface representation with a learnable deformation field. Experimental results demonstrate the significant superiority of DynoSurf over current state-of-the-art approaches.
• Score: 93.18586302123633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the problem of reconstructing temporally consistent surfaces from a 3D point cloud sequence without correspondence. To address this challenging task, we propose DynoSurf, an unsupervised learning framework integrating a template surface representation with a learnable deformation field. Specifically, we design a coarse-to-fine strategy for learning the template surface based on the deformable tetrahedron representation. Furthermore, we propose a learnable deformation representation based on the learnable control points and blending weights, which can deform the template surface non-rigidly while maintaining the consistency of the local shape. Experimental results demonstrate the significant superiority of DynoSurf over current state-of-the-art approaches, showcasing its potential as a powerful tool for dynamic mesh reconstruction. The code is publicly available at https://github.com/yaoyx689/DynoSurf.

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