Canonical Factors for Hybrid Neural Fields

Factored feature volumes offer a simple way to build more compact, efficient, and intepretable neural fields, but also introduce biases that are not necessarily beneficial for realworld data. In this work, we (1) characterize the undesirable biases that these architectures have for axis-aligned signals—they can lead to radiance field reconstruction differences of as high as 2 PSNR—and (2) explore how learning a set of canonicalizing transformations can improve representations by removing these biases. We prove in a simple two-dimensional model problem that a hybrid architecture that simultaneously learns these transformations together with scene appearance succeeds with drastically improved efficiency. We validate the resulting architectures, which we call TILTED, using 2D image, signed distance field, and radiance field reconstruction tasks, where we observe improvements across quality, robustness, compactness, and runtime. Results demonstrate that TILTED can enable capabilities comparable to baselines that are 2x larger, while highlighting weaknesses of standard procedures for evaluating neural field representations.