1. TT-NF: Tensor Train Neural Fields
- Author
-
Obukhov, Anton, Usvyatsov, Mikhail, Sakaridis, Christos, Schindler, Konrad, and Van Gool, Luc
- Subjects
Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Statistics - Machine Learning - Abstract
Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we introduce a novel low-rank representation termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense regular grids and efficient methods for sampling from them. Our representation is a TT parameterization of the neural field, trained with backpropagation to minimize a non-convex objective. We analyze the effect of low-rank compression on the downstream task quality metrics in two settings. First, we demonstrate the efficiency of our method in a sandbox task of tensor denoising, which admits comparison with SVD-based schemes designed to minimize reconstruction error. Furthermore, we apply the proposed approach to Neural Radiance Fields, where the low-rank structure of the field corresponding to the best quality can be discovered only through learning., Comment: Preprint, under review
- Published
- 2022