DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction
About
We propose a novel framework for scene decomposition and static background reconstruction from everyday videos. By integrating the trained motion masks and modeling the static scene as Gaussian splats with dynamics-aware optimization, our method achieves more accurate background reconstruction results than previous works. Our proposed method is termed DAS3R, an abbreviation for Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction. Compared to existing methods, DAS3R is more robust in complex motion scenarios, capable of handling videos where dynamic objects occupy a significant portion of the scene, and does not require camera pose inputs or point cloud data from SLAM-based methods. We compared DAS3R against recent distractor-free approaches on the DAVIS and Sintel datasets; DAS3R demonstrates enhanced performance and robustness with a margin of more than 2 dB in PSNR. The project's webpage can be accessed via \url{https://kai422.github.io/DAS3R/}
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Video Object Segmentation | DAVIS 2016 | J-Measure41.13 | 53 | |
| Moving Object Segmentation | DAVIS Moving 2016 | Jaccard Index41.6 | 48 | |
| Point Cloud Reconstruction | DyCheck | Accuracy (Mean)1.772 | 40 | |
| Video Object Segmentation | DAVIS 17 | J Score43.5 | 25 | |
| Camera pose estimation | DyCheck | ATE0.033 | 21 | |
| Camera pose estimation | TUM-dynamics (test) | ATE0.173 | 18 | |
| Camera pose estimation | DyCheck (test) | ATE0.033 | 12 | |
| Camera pose estimation | TUM-dynamics 48 | ATE0.173 | 12 | |
| Dynamic Segmentation | DyCheck | JM Score1.98 | 6 | |
| Pose Estimation | DyCheck | ATE0.0189 | 6 |