Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding from NVIDIA Research Lavs.

Description

We demonstrate near-instant training of neural graphics primitives on a single GPU for multiple tasks. In gigapixel image we represent an image by a neural network. SDF learns a signed distance function in 3D space whose zero level-set represents a 2D surface. NeRF Mildenhall et al. 2020 uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching. Lastly, neural volume learns a denoised radiance and density field directly from a volumetric path tracer. In all tasks, our encoding and its efficient implementation provide clear benefits: instant training, high quality, and simplicity. Our encoding is task-agnostic: we use the same implementation and hyperparameters across all tasks and only vary the hash table size which trades off quality and performance.

References

Müller, T., Evans, A., Schied, C., & Keller, A. (2022). Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989.

Website

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding: https://nvlabs.github.io/instant-ngp/