DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction
This addresses the challenge of dynamic mesh reconstruction for applications in computer vision and graphics, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of reconstructing temporally consistent surfaces from 3D point cloud sequences without correspondence, proposing DynoSurf, an unsupervised learning framework that integrates a template surface with a learnable deformation field, and demonstrates significant superiority over state-of-the-art approaches.
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.