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.

通过将所有特征网格转化为一个共同的框架，我们通过简单的索引运算将现有数据结构的查找函数组合在一起，实现了帕累托最优压缩和速度，从而表明具有学习探针的哈希表在大小和速度上具有优势。

学习哈希探测的紧凑神经图形原语

Compact Neural Graphics Primitives with Learned Hash Probing

Neural approximations of scalar and vector fields, such as signed distance
functions and radiance fields, have emerged as accurate, high-quality
representations. State-of-the-art results are obtained by conditioning a neural
approximation with a lookup from trainable feature grids that take on part of
the learning task and allow for smaller, more efficient neural networks.
Unfortunately, these feature grids usually come at the cost of significantly
increased memory consumption compared to stand-alone neural network models. We
present a dictionary method for compressing such feature grids, reducing their
memory consumption by up to 100x and permitting a multiresolution
representation which can be useful for out-of-core streaming. We formulate the
dictionary optimization as a vector-quantized auto-decoder problem which lets
us learn end-to-end discrete neural representations in a space where no direct
supervision is available and with dynamic topology and structure. Our source
code will be available at this https URL

本研究提出了一种使用字典方法进行特征网格压缩的方法，可以将其内存消耗降低 100 倍，同时实现了多分辨率表示，其优化方法可以作为离散神经表示的端到端训练解决方案，适用于无法进行直接监督并具有动态拓扑和结构的空间。