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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. Mehr anzeigen
Externe Links
Zeitschrift / Serie
IEEE Journal of Selected Topics in Signal ProcessingBand
Seiten / Artikelnummer
Thema
Tensors; Three-dimensional displays; Neural radiance field; Rendering (computer graphics); Shape; Training; Optimization; Deep learning; neural fields; radiance fields; sampling; tensor decompositions; tensor train; voxelsAltmetrics