Implicit Neural Distance Optimization for Mesh Neural Subdivision

Ke Liu, Ning Ma, Zhihua Wang, Jingjun Gu, Jiajun Bu, Haishuai Wang

Abstract: Surface subdivision is crucial in 3D mesh presentation and storage. Existing classical subdivision methods adopt manually-defined rules and fail to precisely restore a high-resolution mesh. Recently, a data-driven subdivision method delivers neural networks to learn complex non-linear subdivision schemes, but it suffers from time-consuming training data preparation and limited subdivision levels. In this paper, we propose a new data-driven surface subdivision framework, namely Implicit Neural Distance Optimization Neural Subdivision (INDONS), aiming to subdivide coarse meshes to restore high-resolution meshes. Our method subdivides coarse meshes once in each forward process while can be recursively applied to obtain multilevel subdivision results, which is the same as the classical subdivision mechanism. Moreover, we introduce a new loss function, named implicit neural distance, to provide precise supervised signals for training the subdivision network. The implicit neural distance is defined as the distance between a mesh and an implicit neural field. Extensive experiments demonstrate that our INDONS framework is flexible to be deployed, and INDONS outperforms classical subdivision methods and existing data-driven subdivision method in restoring high-resolution meshes.