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.

本文通过对三平面离散数据结构进行处理，展示了该结构具有丰富的信息，可以通过标准深度学习模型进行有效处理，并在处理过程中实现了与处理显式表示结构相当的任务性能，同时保持了相同的重建质量。