CVPR 2023 – NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction

In this episode we discuss NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction
by Bowen Cai, Jinchi Huang, Rongfei Jia, Chengfei Lv, Huan Fu. The paper proposes a new approach called Neural Deformable Anchor (NeuDA) for implicit surface reconstruction using differentiable ray casting. Unlike previous methods, NeuDA leverages hierarchical voxel grids to capture sharp local topologies and maintain anchor grids where each vertex stores a 3D position instead of direct embedding. The paper also introduces a hierarchical positional encoding method for the anchor structure to exploit the properties of high-frequency and low-frequency geometry and appearance. Experiments on two datasets demonstrate NeuDA’s ability to produce promising mesh surfaces.