MrNeRF@janusch_patas
FastGS: Training 3D Gaussian Splatting in 100 Seconds
Contributions:
1. New, simple, and general framework for 3DGS acceleration that can train a scene within 100 seconds while achieving comparable rendering quality.
2. Efficient densification and pruning strategy strictly controls the addition and removal of each Gaussian based on its contribution to multi-view reconstruction quality, greatly accelerating the training process.
3. State-of-the-Art Performance on extensive experiments across Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets. Our method outperforms state-of-the-art (SOTA) approaches in training efficiency while maintaining comparable rendering quality.
4. Strong generality across various tasks, such as dynamic scene reconstruction, surface reconstruction, sparse-view reconstruction, large-scale reconstruction, and SLAM. FastGS achieves an average 2–7× training speedup.
