Research

SplatLab, a NeurIPS 2026 system from UC San Diego, reproduces a published 3D Gaussian splatting baseline, extends it, and writes a paper tied to logged runs.

MLP Splatting from Imperial College London swaps Gaussians for small neural primitives that align with objects, and its code is now out under an MIT license.

ChronoFuseGS from TU Wien merges separately trained Gaussian splatting captures of a changing site into one model and highlights changes at sub-object detail.

aussian Light Transport fits global illumination as a mixture of 13D Gaussians, training 10 to 23 times faster than Neural Radiosity in a few megabytes.