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.

翻译：隐式神经表示已成功应用于压缩多种三维表面表示，如符号距离函数、体素网格，以及其他结构化数据形式，如图像、视频和音频。然而，这些方法在应用于非结构化数据（如三维网格和点云）时存在局限。本研究提出了一种简单而有效的方法，将隐式神经表示的应用扩展至三维三角形网格的压缩。我们的方法通过一个小型神经网络编码一个位移场，该位移场用于精化待压缩三维网格表面的粗糙版本。训练完成后，神经网络的权重所占用的内存远低于位移场或原始表面。我们证明，该方法能够保留精细的几何纹理，并在4倍至380倍的压缩比范围内展现出最先进的性能。