Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

TL;DR

This paper introduces a fully convolutional mesh autoencoder capable of processing arbitrary registered meshes with high reconstruction accuracy, leveraging novel spatially varying kernels for improved performance and localized latent codes for better interpolation.

Contribution

The authors propose a non-template-specific, fully convolutional mesh autoencoder with novel operators that efficiently capture spatially varying features on irregular meshes.

Findings

Outperforms state-of-the-art methods in reconstruction accuracy

Supports arbitrary registered mesh data including tetrahedrons and non-manifold meshes

Latent codes are fully localized, enhancing interpolation capabilities

Abstract

Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.