Related papers: Mesh Compression with Quantized Neural Displacement Fields

Mesh Compression with Quantized Neural Displacement Fields

URL: http://arxiv.org/abs/2504.01027v1
Date: Fri, 28 Mar 2025 13:35:32 GMT
Title: Mesh Compression with Quantized Neural Displacement Fields
Authors: Sai Karthikey Pentapati, Gregoire Phillips, Alan C. Bovik,
Abstract summary: Implicit neural representations (INRs) have been successfully used to compress a variety of 3D surface representations.<n>This work presents a simple yet effective method that extends the usage of INRs to compress 3D triangle meshes.<n>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.
Score: 31.316999947745614
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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.

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