{"ID":6537625,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11285","arxiv_id":"2607.11285","title":"SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation","abstract":"Reconstructing 3D scenes from unordered images remains bottlenecked by expensive Structure-from-Motion (SfM) preprocessing and frozen pose interfaces. We present SalientGS, a unified SfM-to-3D Gaussian Splatting (3DGS) pipeline. Its central contribution is importance-guided Markov Chain Monte Carlo (MCMC) Gaussian allocation, which aggregates multi-view residuals into per-Gaussian underfit and redundancy signals. These signals define a smooth importance-weighted sampling distribution that biases both birth and relocation toward underfit regions. This reallocates capacity from well-fit areas without altering the underlying stochastic gradient Langevin dynamics (SGLD). SalientGS achieves end-to-end reconstruction in 15 minutes with state-of-the-art perceptual quality. The supplementary material provides dedicated sections for Per-Scene Qualitative Comparisons and Per-Image Learned Perceptual Image Patch Similarity (LPIPS) Analysis, including failure cases. Code and evaluation scripts are available at https://github.com/Six-Bit-TX/SalientGS.","short_abstract":"Reconstructing 3D scenes from unordered images remains bottlenecked by expensive Structure-from-Motion (SfM) preprocessing and frozen pose interfaces. We present SalientGS, a unified SfM-to-3D Gaussian Splatting (3DGS) pipeline. Its central contribution is importance-guided Markov Chain Monte Carlo (MCMC) Gaussian allo...","url_abs":"https://arxiv.org/abs/2607.11285","url_pdf":"https://arxiv.org/pdf/2607.11285v1","authors":"[\"Tianyu Xiong\",\"Rui Li\",\"Suning Ge\",\"Jiaqi Yang\"]","published":"2026-07-13T09:06:49Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":614219,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-14T02:54:43.516908796Z","DeletedAt":null,"paper_id":6537625,"paper_url":"https://arxiv.org/abs/2607.11285","paper_title":"SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation","repo_url":"https://github.com/Six-Bit-TX/SalientGS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}