Hash Encoding

Hash encoding is a technique used to efficiently represent high-dimensional data, such as volumetric data or scene representations, in a lower-dimensional space using hash tables and multi-resolution structures. Current research focuses on applying this method to improve the speed and memory efficiency of neural networks for tasks like scene reconstruction, volume visualization, and physics-informed neural networks, often incorporating architectures like coordinate-based networks and multi-layer conditioning. This approach offers significant advantages in handling large-scale datasets and enabling real-time applications, impacting fields ranging from computer graphics and medical imaging to scientific computing.