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DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction
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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/}
Forward citations
Cited by 7 Pith papers
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4DVGGT-D: 4D Visual Geometry Transformer with Improved Dynamic Depth Estimation
A training-free two-pass adaptation of VGGT, with attention-based motion masking and inverse-variance depth fusion, improves dynamic-scene point-cloud reconstruction on DyCheck.
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PAGE-4D: Disentangled pose and geometry estimation for vggt-4d perception
PAGE-4D is a feedforward extension of VGGT that uses a dynamics-aware aggregator and mask to disentangle pose estimation from geometry reconstruction in videos with moving objects.
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Robust Multimodal Dynamic Object Segmentation
A multimodal trajectory-classification network plus a point-query SAM refinement step yields better dynamic masks and static reconstructions than DAS3R-style baselines on DAVIS.
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Ground4D: Consistency-Aware 4D Reconstruction from Monocular Video
Ground4D uses VGGT-based geometry initialization and dynamic Gaussian Splatting refinement to achieve multi-view consistent 4D reconstruction and rendering from monocular video.
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SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping
Temporal sensitivity pruning plus grouped SE(3) motion distillation speeds up DeformableGS rendering by 6.78x to 13.71x and training by about 2.5x across 50 dynamic scenes in MonoDyGauBench.
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RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS
RobustSplat improves transient-free 3D Gaussian Splatting by postponing densification to 10,000 iterations and bootstrapping mask supervision from low to high resolution.
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