[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81629-en":3,"doc-seo-81629-105":30,"detail-sidebar-cat-0-en-105":91},{"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},81629,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis","3D Gaussian Splatting enables photorealistic novel view synthesis, yet autonomous agents and safety-critical systems require a trustworthy measure of where the representation is uncertain, not only high rendering fidelity. The work presents a lightweight, plug-and-play, pixel-wise and view-dependent predictive uncertainty estimation framework. A Bayesian-regularized linear least-squares posthoc method models uncertainty from reconstruction residuals, producing an uncertainty channel per primitive without modifying the scene representation or degrading RGB quality. The reliability signal improves next-best-view planning, pose-agnostic scene change detection, and pose-agnostic anomaly detection.","arXiv :2603 .22786v2 [ cs .CV] 10 Jul 2026  \nPredictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis  \nChamuditha Jayanga Galappaththige 1 ,2⋆, Thomas Gottwald3⋆, Peter Stehr3⋆ , Edgar Heinert4 , Niko Suenderhauf1 ,2 , Dimity Miller 1 ,2 , and Matthias  \nRottmann4  \n1 QUT Centre for Robotics, Australia  \n2 ARIAM Hub, Australia  \n3 University of Wuppertal, Germany  \n4 University of Osnabrück, Germany  \nAbstract. Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map for autonomous agents and safety-critical applications, knowing where the representation is uncertain is as important as the rendering fidelity itself. We bridge this critical gap by introducing a lightweight, plug-and-play framework for pixel-wise, view-dependent predictive uncertainty estimation. Our posthoc method formulates uncertainty as a Bayesian-regularized linear leastsquares optimization over reconstruction residuals. This architectureagnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation or degrading baseline visual fidelity. Crucially, we demonstrate that providing this actionable reliability signal successfully translates 3D Gaussian splatting into a trustworthy spatial map, further improving state-of-the-art performance across three critical downstream perception tasks: active view selection, pose-agnostic scene change detection, and pose-agnostic anomaly detection. Code is available at [github.io/3DGS-Uncertainty](github.io/3DGS-Uncertainty).  \nKeywords: Gaussian Splatting · Novel View Synthesis · Uncertainty Estimation  \n1 Introduction  \nRadiance fields [23, 40] have evolved from novel view synthesis (NVS) engines into foundational spatial maps for autonomous agents [11,39,63] . However, constructing a radiance field from 2D images is an inherently ill-posed inverse problem [55] . To function reliably in real-world deployments, where severe occlusions, unobserved regions, and geometric ambiguities are inevitable, these systems must be capable of rigorously quantifying their predictive uncertainty [53] . 3D Gaussian Splatting (3DGS) [23] has rapidly emerged as the leading representation for these tasks, combining the expressiveness of volumetric rendering with the  \n⋆ Equally contributed.  \n2 Galappaththige et al.  \nFig. 1: Per-pixel UE for NVS. Our post-hoc method generates a view-dependent uncertainty map that closely mirrors regions of error within the RGB render.  \nefficiency of rasterization. While recent advancements have drastically improved 3DGS visual fidelity [25, 61], geometric consistency [9, 24], and efficiency [16, 38], equipping these models with robust, system-level uncertainty estimation (UE) remains a critical, underexplored challenge.  \nExisting UE methods for 3DGS fall into two complementary families, depending on whether they quantify uncertainty in the learned representation, i.e., the Gaussian parameters, or in the rendered radiance field, i.e., the rendered pixels. Most prior work targets the learned representation. Stochastic formulations [1, 33, 37, 52] model distributions over Gaussian parameters, but rely on sampling-based optimization or complex inference that introduces prohibitive latency, requires architectural modifications degrading baseline visual fidelity, and lacks modularity with the rapidly expanding ecosystem of 3DGS variants. Post-hoc alternatives instead estimate epistemic uncertainty in parameter space via Hessian approximations [21, 58] . However, as shown in our experiments (Sec. 4.1), these parameter-centric methods capture view-dependent uncertainty poorly, hindering their use in downstream perception tasks.  \nIn contrast, we model uncertainty directly in the rendered radiance field [15], introducing an efficient, plug-and-play system that estimates pixel-wise predictive uncertainty for NVS. Our ","cbCaibRjPUKouL63","https://ap.wps.com/l/cbCaibRjPUKouL63","pdf",30395171,3,1,30,"English","en",105,"# Introduction\n## Motivation for uncertainty in novel view synthesis\n## Existing uncertainty estimation approaches for 3DGS\n## Proposed posthoc, view-dependent uncertainty framework\n## Reliability from reconstruction residuals and Bayesian regularization","[{\"question\":\"Why is predictive uncertainty important for 3D Gaussian Splatting in real-world use?\",\"answer\":\"Real deployments involve occlusions, unobserved regions, and geometric ambiguities. To operate reliably, systems must quantify predictive uncertainty, not just render high-fidelity images.\"},{\"question\":\"How does the proposed method estimate uncertainty?\",\"answer\":\"Uncertainty is formulated as a Bayesian-regularized linear least-squares optimization over reconstruction residuals, producing a view-dependent uncertainty map and a per-primitive uncertainty channel.\"},{\"question\":\"What tasks benefit from the uncertainty signal?\",\"answer\":\"The uncertainty improves performance in active view selection (next-best-view planning), pose-agnostic scene change detection, and pose-agnostic anomaly detection.\"}]",1784174965,76,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predictive-photometric-uncertainty-in-gaussian-splatting-for-novel-view-synthesis","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/predictive-photometric-uncertainty-in-gaussian-splatting-for-novel-view-synthesis/81629/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predictive uncertainty important for 3D Gaussian Splatting in real-world use?","Question",{"text":75,"@type":76},"Real deployments involve occlusions, unobserved regions, and geometric ambiguities. To operate reliably, systems must quantify predictive uncertainty, not just render high-fidelity images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method estimate uncertainty?",{"text":80,"@type":76},"Uncertainty is formulated as a Bayesian-regularized linear least-squares optimization over reconstruction residuals, producing a view-dependent uncertainty map and a per-primitive uncertainty channel.",{"name":82,"@type":73,"acceptedAnswer":83},"What tasks benefit from the uncertainty signal?",{"text":84,"@type":76},"The uncertainty improves performance in active view selection (next-best-view planning), pose-agnostic scene change detection, and pose-agnostic anomaly detection.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"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":22,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"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":106,"slug":137},19,"General","general"]