Title: Neural Compression of 3D Meshes using Sparse Implicit Representation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: compression, 3D mesh

Abstract: The growing demand for high-quality 3D models has fueled the need for efficient 3D mesh compression techniques. However, existing methods suffer from suboptimal compression performance due to inefficient data representation of meshes. To address this issue, we propose a novel neural mesh compression method based on Sparse Implicit Representation (SIR). Specifically, SIR only records values of the signed distance field on regular grids near the surface, enabling high-resolution structured representation of arbitrary geometric data with much less memory cost while supporting precise surface recovery. Subsequently, a lightweight Sparse Neural Compression (SNC) network is constructed to extract compact embedded features from the SIR and encode them into a bitstream. Extensive experiments and ablation studies demonstrate that our method outperforms state-of-the-art mesh and point cloud compression approaches in both compression performance and computational efficiency across diverse mesh models. The code is included in the Supplementary Material.