Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid. However, existing feature grids either come with a large memory footprint (dense or factorized grids, trees, and hash tables) or slow performance (index learning and vector quantization). In this paper, we show that a hash table with learned probes has neither disadvantage, resulting in a favorable combination of size and speed. Inference is faster than unprobed hash tables at equal quality while training is only 1.2-2.6x slower, significantly outperforming prior index learning approaches. We arrive at this formulation by casting all feature grids into a common framework: they each correspond to a lookup function that indexes into a table of feature vectors. In this framework, the lookup functions of existing data structures can be combined by simple arithmetic combinations of their indices, resulting in Pareto optimal compression and speed.

翻译：当神经网络通过空间数据结构（如网格排列的可训练特征）增强时，神经图形基元能实现更快速度与更高质量。然而，现有特征网格要么占用大量内存（密集网格、因子化网格、树结构与哈希表），要么性能缓慢（索引学习与向量量化）。本文证明，采用学习型探测的哈希表可同时避免这两类缺陷，实现存储与速度的优异平衡。在同等质量下，其推理速度优于无探测哈希表，训练速度仅慢1.2-2.6倍，显著超越先前索引学习方法。我们通过将所有特征网格纳入统一框架得出该公式：每个特征网格对应一个索引到特征向量表的查找函数。在此框架下，现有数据结构的查找函数可通过其索引的简单算术组合进行整合，从而实现帕累托最优的压缩效率与运行速度。