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 real-world 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 two-dimensional model problem that simultaneously learning these transformations together with scene appearance succeeds with drastically improved efficiency. We validate the resulting architectures, which we call TILTED, using image, signed distance, 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 neural field evaluation procedures.

翻译：因子化特征体素提供了一种构建更紧凑、高效且可解释神经场的简单方法，但也引入了对真实世界数据未必有益的偏差。本研究旨在：(1) 刻画这类架构在轴对齐信号中存在的不利偏差——这些偏差可能导致辐射场重建质量差异高达2个PSNR值；(2) 探究学习一组规范化变换如何通过消除这些偏差来改进表征。我们通过二维模型问题证明，同时学习这些变换与场景外观能以显著提升的效率取得成功。我们利用图像、符号距离和辐射场重建任务验证了所提出的架构（称为TILTED），在质量、鲁棒性、紧凑性和运行时间方面均观察到改进。结果表明，TILTED能够实现与规模大2倍的基线方法相媲美的能力，同时揭示了神经场评估流程的薄弱环节。