# Instant NeRF - [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://arxiv.org/abs/2201.05989) - [Neural Radiance Field](Neural%20Radiance%20Field.md) - Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate - rely on task specific [Data Structures](Data%20Structures.md) - new input encoding that permits the use of a smaller network without sacrificing quality - educing the number of floating point and memory access operations - near-instant training of neural graphics primitives on a single GPU for multiple tasks - small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through [Gradient Descent](Gradient%20Descent.md) - automatically focuses on relevant detail, independent of task at hand - low overhead - In a gigapixel image, they represent an image by a neural network. SDF learns a signed distance function in 3D space whose zero level-set represents a 2D surface - 2D images and their camera poses to reconstruct a volumetric radiance-and-[Density](Density.md) field that is visualized using ray marching. - neural volume learns a denoised radiance and [Density](Density.md) field directly from a volumetric path tracer. - only vary the hash table size which trades off quality and performance - disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs - parallelism - fully-fused [Operator Fusion](Operator%20Fusion.md) CUDA kernels with a focus on minimizing wasted bandwidth and compute operations