Driven by the appealing properties of neural fields for storing and communicating 3D data, the problem of directly processing them to address tasks such as classification and part segmentation has emerged and has been investigated in recent works. Early approaches employ neural fields parameterized by shared networks trained on the whole dataset, achieving good task performance but sacrificing reconstruction quality. To improve the latter, later methods focus on individual neural fields parameterized as large Multi-Layer Perceptrons (MLPs), which are, however, challenging to process due to the high dimensionality of the weight space, intrinsic weight space symmetries, and sensitivity to random initialization. Hence, results turn out significantly inferior to those achieved by processing explicit representations, e.g., point clouds or meshes. In the meantime, hybrid representations, in particular based on tri-planes, have emerged as a more effective and efficient alternative to realize neural fields, but their direct processing has not been investigated yet. In this paper, we show that the tri-plane discrete data structure encodes rich information, which can be effectively processed by standard deep-learning machinery. We define an extensive benchmark covering a diverse set of fields such as occupancy, signed/unsigned distance, and, for the first time, radiance fields. While processing a field with the same reconstruction quality, we achieve task performance far superior to frameworks that process large MLPs and, for the first time, almost on par with architectures handling explicit representations.

翻译：受神经场在存储和通信3D数据中吸引人的特性驱动，直接处理神经场以解决分类和部件分割等任务的问题已出现，并在近期工作中得到研究。早期方法采用在整个数据集上训练的共享网络参数化的神经场，实现了良好的任务性能但牺牲了重构质量。为改进后者，后续方法聚焦于参数化为大型多层感知器（MLP）的个体神经场，但由于权重空间的高维度、固有的权重空间对称性以及对随机初始化的敏感性，这些神经场处理起来极具挑战性。因此，其结果显著劣于处理显式表示（如点云或网格）所达到的性能。与此同时，混合表示（特别是基于三平面的表示）已成为实现神经场的更有效和高效的替代方案，但其直接处理尚未被研究。本文表明，三平面离散数据结构编码了丰富信息，可通过标准深度学习机制有效处理。我们定义了一个涵盖多种场（如占用场、有符号/无符号距离场，以及首次包含的辐射场）的广泛基准。在处理具有相同重构质量的场时，我们实现的任务性能远优于处理大型MLP的框架，且首次几乎与处理显式表示的架构相当。