Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

We demonstrate instant training of neural graphics primitives on a single GPU for multiple tasks. In Gigapixel image we represent a gigapixel image by a neural network. SDF learns a signed distance function in 3D space whose zero level-set represents a 2D surface. Neural radiance caching (NRC) [Müller et al. 2021] employs a neural network that is trained in real-time to cache costly lighting calculations. Lastly, NeRF uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching. In all tasks, our encoding and its efficient implementation provide clear benefits: rapid training, high quality, and simplicity. Our encoding is task-agnostic: we use the same implementation and hyper parameters across all tasks and only vary the hash table size which trades off quality and performance. 2022: T. Müller, Alex Evans, Christoph Schied, A. Keller https://arxiv.org/pdf/2201.05989v1.pdf