Mesh Compression with Quantized Neural Displacement Fields
This work addresses the challenge of efficiently storing and transmitting 3D meshes for applications in computer graphics and visualization, representing an incremental advancement by extending existing methods to a new data type.
The paper tackles the problem of compressing 3D triangle meshes, which are unstructured data, by using implicit neural representations to encode a displacement field, achieving state-of-the-art performance with compression ratios from 4x to 380x while preserving intricate geometric textures.
Implicit neural representations (INRs) have been successfully used to compress a variety of 3D surface representations such as Signed Distance Functions (SDFs), voxel grids, and also other forms of structured data such as images, videos, and audio. However, these methods have been limited in their application to unstructured data such as 3D meshes and point clouds. This work presents a simple yet effective method that extends the usage of INRs to compress 3D triangle meshes. Our method encodes a displacement field that refines the coarse version of the 3D mesh surface to be compressed using a small neural network. Once trained, the neural network weights occupy much lower memory than the displacement field or the original surface. We show that our method is capable of preserving intricate geometric textures and demonstrates state-of-the-art performance for compression ratios ranging from 4x to 380x.