12 citations · 17 across the 23 of their papers we have counts for
4 papers · 1 filter
3DOF+Quantization: 3DGS quantization for large scenes with limited Degrees of Freedom
Matthieu Gendrin, Stéphane Pateux, Théo Ladune
3D Gaussian Splatting (3DGS) is a major breakthrough in 3D scene reconstruction. With a number of views of a given object or scene, the algorithm trains a model composed of 3D gaus…
Adam SLAM - the last mile of camera calibration with 3DGS
Matthieu Gendrin, Stéphane Pateux, Xiaoran Jiang +2
The quality of the camera calibration is of major importance for evaluating progresses in novel view synthesis, as a 1-pixel error on the calibration has a significant impact on th…
BOGausS: Better Optimized Gaussian Splatting
Stéphane Pateux, Matthieu Gendrin, Luce Morin +2
3D Gaussian Splatting (3DGS) proposes an efficient solution for novel view synthesis. Its framework provides fast and high-fidelity rendering. Although less complex than other solu…
CAwa-NeRF: Instant Learning of Compression-Aware NeRF Features
Omnia Mahmoud, Théo Ladune, Matthieu Gendrin
Modeling 3D scenes by volumetric feature grids is one of the promising directions of neural approximations to improve Neural Radiance Fields (NeRF). Instant-NGP (INGP) introduced m…