[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86552-en":3,"doc-seo-86552-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86552,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","SalientGS Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation","SalientGS addresses the bottleneck in reconstructing 3D scenes from unordered images by unifying SfM preprocessing and 3D Gaussian Splatting (3DGS) into an end-to-end pipeline. The core contribution is importance-guided MCMC Gaussian allocation, which converts multi-view residual evidence into per-Gaussian underfit and redundancy signals. These signals form a smooth importance-weighted sampling distribution that steers both Gaussian birth and relocation toward underfit regions. Capacity is reallocated from well-fit areas without changing the SGLD-based population dynamics, enabling 15-minute reconstructions with state-of-the-art perceptual quality (LPIPS).","SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC  \nGaussian Allocation  \narXiv :2607 . 11285v1 [ cs .CV] 13 Jul 2026  \nTianyu Xiong  \nSchool of Computer Science, Northwestern Polytechnical University  \nXi’an, China  \nSuning Ge  \nSchool of Computer Science, Northwestern Polytechnical University  \nXi’an, China  \nAbstract  \nReconstructing 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 achievesend-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](https://github.com/Six-Bit-TX/SalientGS).  \nCCS Concepts  \n• Computing methodologies → Computer vision.  \nKeywords  \n3D Gaussian Splatting, Structure from Motion, Joint Pose Optimization, Markov Chain Monte Carlo, Importance-Guided Allocation  \n1 Introduction  \nReconstructing high-fidelity 3D scenes from unordered image collections is a fundamental problem in computer vision and graphics, with applications spanning virtual reality, robotics, and cultural heritage preservation. The recent advent of 3D Gaussian Splatting (3DGS) [16] has transformed this landscape, enabling realtime novel view synthesis with quality rivaling Neural Radiance Fields (NeRF) [24] while dramatically reducing rendering time. However, 3DGS inherits a critical dependency from the NeRF paradigm: it requires accurate camera poses and sparse point clouds from Structure-from-Motion (SfM) preprocessing, most commonly performed with COLMAP [30] .  \nACM MM’26, Rio de Janeiro, Brazil  \n2026. ACM ISBN 978-x-xxxx-xxxx-x/YYYY/MM [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \nRui Li  \nCEMSE, King Abdullah University of Science and Technology  \nThuwal, Saudi Arabia  \nJiaqi Yang  \nSchool of Computer Science, Northwestern Polytechnical University  \nXi’an, China  \nQuality vs Speed vs Model Size on Mip-NeRF 360  \n10 15 20 25 30 35 40  \nTotal Time (minutes)  \nFigure 1: Quality vs. Speed vs. Model Size on Mip-NeRF 360. Bubble size indicates Gaussian count. SalientGS achieves the best perceptual quality (LPIPS ↓) with competitive speed using only 1.5M Gaussians—demonstrating that importanceguided MCMC allocation and joint pose optimization enable both fast reconstruction and state-of-the-art rendering quality without standalone COLMAP preprocessing.  \nIn widely used COLMAP-based two-stage pipelines, this preprocessing step can become a practical bottleneck for end-to-end reconstruction. Exhaustive image matching and second-order bundle adjustment can be expensive, especially for large image collections, and their cost can rival or exceed 3DGS training time itself (Table 1) . More importantly, the interface is typically frozen: SfM pose errors propagate downstream without photometric correction, and the separation between stages limits joint optimization of geometry and appearance.  \nRecent efforts to accelerate 3DGS training [5, 6, 22, 29] have achieved impressive speedups through improved densification strategies, progressive training, and efficient Gaussian management. However, these methods still rely on standalone COLMAPpreprocessing, meaning their reported times underst","cbCaitTCnj9w89Uh","https://ap.wps.com/l/cbCaitTCnj9w89Uh","pdf",13967776,6,1,9,"English","en",105,"# Introduction\n# Method Overview","[{\"question\":\"What problem does SalientGS address in 3D reconstruction from unordered images?\",\"answer\":\"SalientGS targets the expensive and rigid SfM preprocessing stage required by 3D Gaussian Splatting, where pose inaccuracies propagate downstream and prevent effective joint optimization. It unifies SfM and 3DGS into a single pipeline to reduce end-to-end cost while improving quality.\"},{\"question\":\"How does importance-guided MCMC Gaussian allocation work?\",\"answer\":\"SalientGS computes multi-view underfit (importance) and well-fit redundancy signals from residuals, then converts them into an importance-weighted sampling distribution. This biases Gaussian birth and relocation in the MCMC framework to reallocate capacity toward persistent errors.\"},{\"question\":\"Does importance guidance change the underlying Langevin dynamics or convergence guarantees?\",\"answer\":\"No. The importance guidance is used as a heuristic allocation strategy layered on top of the SGLD-based population dynamics. It does not alter the underlying Langevin updates and therefore makes no additional convergence claims beyond the base framework.\"}]",1784212579,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"salientgs-unified-sfm-to-3dgs-with-importance-guided-mcmc-gaussian-allocation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/salientgs-unified-sfm-to-3dgs-with-importance-guided-mcmc-gaussian-allocation/86552/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does SalientGS address in 3D reconstruction from unordered images?","Question",{"text":76,"@type":77},"SalientGS targets the expensive and rigid SfM preprocessing stage required by 3D Gaussian Splatting, where pose inaccuracies propagate downstream and prevent effective joint optimization. It unifies SfM and 3DGS into a single pipeline to reduce end-to-end cost while improving quality.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does importance-guided MCMC Gaussian allocation work?",{"text":81,"@type":77},"SalientGS computes multi-view underfit (importance) and well-fit redundancy signals from residuals, then converts them into an importance-weighted sampling distribution. This biases Gaussian birth and relocation in the MCMC framework to reallocate capacity toward persistent errors.",{"name":83,"@type":74,"acceptedAnswer":84},"Does importance guidance change the underlying Langevin dynamics or convergence guarantees?",{"text":85,"@type":77},"No. The importance guidance is used as a heuristic allocation strategy layered on top of the SGLD-based population dynamics. It does not alter the underlying Langevin updates and therefore makes no additional convergence claims beyond the base framework.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]