Compact Neural Graphics Primitives with Learned Hash Probing


Compact neural graphics primitives (Ours) have an inherently small size across a variety of use cases with automatically chosen hyperparameters. In contrast to similarly compressed representations like JPEG for images (top) and masked wavelet representations [Rho et al. 2023] for NeRFs [Mildenhall et al. 2020] (bottom), our representation neither uses quantization nor coding, and hence can be queried without a dedicated decompression step. This is essential for level of detail streaming and working-memory-constrained environments such as video game texture compression. The compression artifacts of our method are easy on the eye: there is less ringing than in JPEG and less blur than in Rho et al. (though more noise). Compact neural graphics primitives are also fast: training is only 1.2-2.6x slower (depending on compression settings) and inference is faster than Instant NGP because our significantly reduced file size fits better into caches.

SIGGRAPH Asia 2023