Mesh Compression with Quantized Neural Displacement Fields
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
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.
