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SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
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3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS. This makes reconstruction of real-world captures problematic. We present SpotLessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors. Our method achieves state-of-the-art reconstruction quality both visually and quantitatively, on casual captures. Additional results available at: https://spotlesssplats.github.io
Forward citations
Cited by 15 Pith papers
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Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios
RefineSplat removes ambiguous distractors from 3DGS via entropy-aware adaptive masking and density control, releasing an 18-scene Ambiguous wild dataset and reporting SOTA metrics on multiple wild benchmarks.
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Difix3D-W: Distractor-Free Few-Shot 3D Gaussian Splatting in the Wild
A new sparse-view 3D Gaussian splatting method for unconstrained scenes with distractors combines diffusion-based reference-guided refinement and sparsity-aware Gaussian replication to achieve better rendering quality.
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You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
YOGO reformulates stochastic 3D Gaussian Splatting into a deterministic budget-aware system and supplies an ultra-dense dataset to enforce physical fidelity over viewpoint interpolation.
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DreamDrive: Generative 4D Scene Modeling from Street View Images
DreamDrive generates 3D-consistent driving videos from a single image by lifting diffusion-generated reference frames into a hybrid static and dynamic 4D Gaussian scene.
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DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction
A dynamics-aware Gaussian splatting method reconstructs clean static backgrounds from unposed videos with large dynamic objects by training dynamic masks on image pairs and optimizing a per-Gaussian staticness score.
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HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting
HybridGS separates transient objects from static scenes by using 2D Gaussians per image for transients and 3D Gaussians for the static background, improving novel-view synthesis quality.
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RoMo: Robust Motion Segmentation Improves Structure from Motion
RoMo combines optical flow, epipolar geometry, and SAMv2 features to segment moving objects and thereby improves SfM camera calibration for dynamic scenes.
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Impact of Solar Particle Events on Space Radiation Shielding: OLTARIS Simulation and Quantum Optimization of Material Selection using QAOA and VQE Algorithms
The abstract claims quantum-optimized shielding material selection, but the full text is an unrelated 3D Gaussian Splatting paper, so the claim is unsupported.
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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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Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs
A training-free superpoint graph on 3D Gaussians enables fast, view-consistent, hierarchical open-vocabulary segmentation, reportedly cutting semantic field construction time by over 30x.
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T-3DGS: Removing Transient Objects for 3D Scene Reconstruction
T-3DGS detects and removes transient and semi-transient objects during 3D Gaussian Splatting reconstruction using bivariate uncertainty divergence and SAM2-based mask propagation, outperforming prior robust reconstruc...
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NexusSplats: Efficient 3D Gaussian Splatting in the Wild
NexusSplats replaces per-Gaussian appearance codes with kernel-level shared codes and 3D uncertainty propagation, achieving comparable rendering quality with 65.4% fewer parameters and 2.7x faster training.
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Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction
A 3D Gaussian Splatting framework for urban scenes that combines visibility-based data partitioning, budgeted level-of-detail generation, and per-Gaussian appearance embeddings to enable efficient training and real-ti...
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Visibility-Uncertainty-guided 3D Gaussian Inpainting via Scene Conceptional Learning
VISTA combines visibility-uncertainty-guided 3D Gaussian inpainting with diffusion-based scene conceptual learning to remove static and dynamic objects from 3D scenes.
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Object Learning and Robust 3D Reconstruction
The thesis presents FlowCapsules, RobustNeRF, and SpotLessSplats, demonstrating that unsupervised object-based learning with motion and geometric consistency improves segmentation and 3D reconstruction.
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