TT-NF: Tensor Train Neural Fields

• URL: http://arxiv.org/abs/2209.15529v1
• Date: Fri, 30 Sep 2022 15:17:39 GMT
• Title: TT-NF: Tensor Train Neural Fields
• Authors: Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis, Konrad Schindler, Luc Van Gool
• Abstract summary: We introduce a novel low-rank representation termed Train Neural Fields (TT-NF) for learning fields on regular grids. We analyze the effect of low-rank compression on the downstream task quality metrics.
• Score: 88.49847274083365
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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