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SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting

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arxiv 2406.20055 v2 pith:64FESFMY submitted 2024-06-28 cs.CV cs.LG

Sara Sabour , Lily Goli , George Kopanas , Mark Matthews , Dmitry Lagun , Leonidas Guibas , Alec Jacobson , David J. Fleet
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Andrea Tagliasacchi
This is my paper
classification cs.CVcs.LG
keywords reconstructionspotlesssplatscapturesdistractorsgaussiansplattingachievesadditional
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    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.

  2. Difix3D-W: Distractor-Free Few-Shot 3D Gaussian Splatting in the Wild

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    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.

  3. You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    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.

  4. Impact of Solar Particle Events on Space Radiation Shielding: OLTARIS Simulation and Quantum Optimization of Material Selection using QAOA and VQE Algorithms

    physics.med-ph 2025-08 reject novelty 5.0 of 10

    The abstract claims quantum-optimized shielding material selection, but the full text is an unrelated 3D Gaussian Splatting paper, so the claim is unsupported.

  5. Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction

    cs.CV 2025-07 conditional novelty 4.0 of 10

    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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