BOGausS: Better Optimized Gaussian Splatting
This work addresses the problem of model efficiency for 3D rendering applications, offering a significant improvement over existing methods.
The paper tackles the challenge of reducing model size in 3D Gaussian Splatting for novel view synthesis without quality loss, achieving models up to ten times lighter than the original with no degradation.
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 solutions such as Neural Radiance Fields (NeRF), there are still some challenges building smaller models without sacrificing quality. In this study, we perform a careful analysis of 3DGS training process and propose a new optimization methodology. Our Better Optimized Gaussian Splatting (BOGausS) solution is able to generate models up to ten times lighter than the original 3DGS with no quality degradation, thus significantly boosting the performance of Gaussian Splatting compared to the state of the art.