#Vector Quantization

LightGaussian provides advanced methods for 3D Gaussian compression, facilitating significant size reduction and enhanced frame rates. Achieve up to 15x compression while exceeding 200 FPS, maintaining processing quality. Leveraging techniques like Prune & Recovery, SH Distillation, and VecTree Quantization, this project supports sophisticated rendering tasks with comprehensive setup guides. Utilizing datasets such as MipNeRF360 and Tank & Temple, LightGaussian ensures minimal accuracy loss, advancing storage solutions and graphical efficiency in 3D data applications.

The vector-quantize-pytorch library offers a straightforward implementation for vector quantization, transcribed from Deepmind's TensorFlow version. It features Exponential Moving Averages for dictionary updates, proven effective in projects like VQ-VAE-2 and Jukebox by Deepmind and OpenAI. The package supports advanced methods like Residual VQ and grouped quantization, suitable for generative modeling and data representation tasks.