MeshFeat: Multi-Resolution Features for Neural Fields on Meshes

Autor:	Mahajan, Mihir, Hofherr, Florian, Cremers, Daniel
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Parametric feature grid encodings have gained significant attention as an encoding approach for neural fields since they allow for much smaller MLPs, which significantly decreases the inference time of the models. In this work, we propose MeshFeat, a parametric feature encoding tailored to meshes, for which we adapt the idea of multi-resolution feature grids from Euclidean space. We start from the structure provided by the given vertex topology and use a mesh simplification algorithm to construct a multi-resolution feature representation directly on the mesh. The approach allows the usage of small MLPs for neural fields on meshes, and we show a significant speed-up compared to previous representations while maintaining comparable reconstruction quality for texture reconstruction and BRDF representation. Given its intrinsic coupling to the vertices, the method is particularly well-suited for representations on deforming meshes, making it a good fit for object animation. Comment: To appear at European Conference on Computer Vision (ECCV), 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.13592 Zobrazit plný text záznamu View this record from Arxiv