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：神经场因其在存储和传递三维数据方面的优异特性而受到关注，直接处理神经场以完成分类与部件分割等任务的问题已在近期的研究中得到探讨。早期方法采用在整体数据集上训练的共享网络参数化神经场，取得了良好的任务性能，但牺牲了重建质量。为改善后者，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，然而由于权重空间的高维特性、固有的权重空间对称性以及随机初始化的敏感性，这类神经场处理起来颇具挑战。因此，其结果显著逊色于处理显式表征（如点云或网格）的方法。与此同时，混合表征（特别是基于三平面的混合表征）已成为实现神经场更高效、更有效的替代方案，但其直接处理尚未得到研究。本文表明，三平面离散数据结构编码了丰富的信息，可被标准深度学习机制有效处理。我们定义了一个覆盖多种场（如占有场、有符号/无符号距离场，以及首次引入的辐射场）的广泛基准测试。在保证相同重建质量下处理场时，我们取得的任务性能远优于处理大型MLP的框架，并且首次与处理显式表征的架构几乎持平。