This decomposed splatting (DeSplat) approach explicitly separates distractors from static parts. Earlier methods (e.g. SpotlessSplats, WildGaussians) use loss masking of detected distractors to avoid overfitting, while DeSplat instead jointly reconstructs distractor elements.
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Knowing how 3DGS treats distractors, we initialize a set of Gaussians close to every camera view for reconstructing view-specific distractors. The Gaussians initialized from the point cloud should reconstruct static stuff. These separately rendered images are alpha-blended during training.

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This BabyYoda scene from RobustNeRF is similar to a crowdsourced scenario, where a set of static toys appear together with inconsistently-placed toys between the frames.

Vanilla 3DGS is quite robust here, but some views end up being rendered with spurious artefacts (right image).
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👋Interested in Gaussian splatting and removing dynamic content from images?

Our DeSplat is presented today at #CVPR2025 at Poster Session 1, ExHall D Poster #52.

Yihao will be there to present our fully splatting-based method for separating static and dynamic stuff in images.

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