[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-650296-105":59,"doc-detail-650296-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","state-of-the-art-on-3d-reconstruction-with-rgb-d-cameras-state-of-theart-report","State of the Art on 3D Reconstruction with RGB-D Cameras - State of TheArt Report","","Affordable consumer RGB-D cameras have significantly accelerated visual scene reconstruction. Researchers in computer graphics and computer vision have introduced new algorithms for capturing detailed shape models of both static and dynamic scenes using RGB-D sensors. Progress spans higher reconstruction fidelity under limited resolution, real-time methods with quality trade-offs, and expanded spatial-temporal coverage. The survey analyzes algorithmic concepts, compares approaches, and studies how RGB-D benefits are maximized while mitigating common data distortions, also outlining open research directions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/state-of-the-art-on-3d-reconstruction-with-rgb-d-cameras-state-of-theart-report/650296/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/state-of-the-art-on-3d-reconstruction-with-rgb-d-cameras-state-of-theart-report/650296.png","ImageObject",300,407,{"name":92,"@type":93},"Riley West","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-24",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What impact have affordable consumer RGB-D cameras had on 3D reconstruction?","Question",{"text":112,"@type":113},"They have enabled a major advancement in visual scene reconstruction methods by making RGB-D sensing widely available at low cost and with real-time performance.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the report categorize recent progress in RGB-D scene reconstruction?",{"text":117,"@type":113},"It reviews developments for static geometry reconstruction, dynamic-scene and deformable motion reconstruction, and approaches that additionally estimate appearance such as material and lighting.",{"name":119,"@type":110,"acceptedAnswer":120},"What key challenges do the methods address when using RGB-D data?",{"text":121,"@type":113},"The report focuses on exploiting RGB-D strengths for geometry while suppressing non-trivial sensor data distortions, including noise and other limitations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},650296,1792823846,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},1099523885074,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","This is the accepted version of the following article: \"State of the Art on 3D Reconstruction with RGB-D Cameras\", which has been published in final form at [http://onlinelibrary.wiley.com. This](http://onlinelibrary.wiley.com. This) article may be used for non-commercial purposes in accordance with the Wiley SelfArchiving Policy [[http://olabout.wiley.com/WileyCDA/Section/id-820227.html](http://olabout.wiley.com/WileyCDA/Section/id-820227.html)] .  \nEUROGRAPHICS 2018  \nK. Hildebrandt and C. Theobalt (Guest Editors)  \nVolume 37 (2018), Number 2 STAR – State of TheArt Report  \nState of the Art on 3D Reconstruction with RGB-D Cameras  \nMichael Zollhöfer 1; 2 , Patrick Stotko3 , Andreas Görlitz4 , Christian Theobalt 1 , Matthias Nießner5 , Reinhard Klein3 , Andreas Kolb4  \n1 Max-Planck Institute for Informatics, Saarbrücken, Germany  \n2 Stanford University, CA, USA  \n3 Computer Graphics Group, University of Bonn, Germany  \n4 Computer Graphics Group, University of Siegen, Germany  \n5 Technical University of Munich, Germany  \nFigure 1: This state-of-the-art report provides an overview of RGB-D scene reconstruction approaches. We discuss recent trends in the geometric reconstruction of static (left) and dynamic scenes (middle) as well as the acquisition of corresponding color and reflectance (right) .  \nAbstract  \nThe advent of affordable consumer grade RGB-D cameras has brought about a profound advancement of visual scene recon struction methods. Both computer graphics and computer vision researchers spend significant effort to develop entirely new algorithms to capture comprehensive shape models of static and dynamic scenes with RGB-D cameras. This led to significant advances of the state of the art along several dimensions. Some methods achieve very high reconstruction detail, despite limited sensor resolution. Others even achieve real-time performance, yet possibly at lower quality. New concepts were developed to capture scenes at larger spatial and temporal extent. Other recent algorithms flank shape reconstruction with concurrent material and lighting estimation, even in general scenes and unconstrained conditions. In this state-of-the-art report, we analyze these recent developments in RGB-D scene reconstruction in detail and review essential related work. We explain, compare,  \nand critically analyze the common underlying algorithmic concepts that enabled these recent advancements. Furthermore, we  \nshow how algorithms are designed to best exploit the benefits of RGB-D data while suppressing their often non-trivial data distortions. In addition, this report identifies and discusses important open research questions and suggests relevant directions for future work.  \nCCS Concepts  \n•Computing methodologies ; : : :; Reconstruction; Appearance and texture representations; Motion capture;  \n1. Introduction  \nThe core technology behind today’s structured light or time-offlight-based depth cameras already dates back several decades. However, the recent introduction of consumer grade sensors that package this technology into mass-manufactured devices of small form factor made RGB-D cameras a commodity available to a larger user base. Microsoft started this development in 2010 with the Kinect, and several other devices followed, e.g., RGB-D cameras such as the Intel RealSense, Primesense Carmine, Google Tango, or Occiptial’s Structure Sensor. These cameras are not only available  \nat low price points, but these lightweight sensors also capture perpixel color and depth images at adequate resolution and at real-time rates. In conjunction, these features put them ahead of even some far more expensive 3D scanning systems, in particular when developing solutions for consumer grade applications. The potential of these new sensors has been quickly recognized in visual computing. For instance, the seminal KinectFusion work [NDI 􀀃 11, IKH􀀃 11] had remarkable impact in the computer graphics and vision communities, and triggered an incredible response.","cbCaitgKfissgQ3p","https://ap.wps.com/l/cbCaitgKfissgQ3p","pdf",3541856,28,"English","# Abstract\n# Introduction\n## RGB-D camera technology and commodity sensors\n## Advances in static-scene dense geometry reconstruction\n## Advances in dynamic-scene geometry reconstruction\n## Extending RGB-D to material and reflectance estimation\n# References and publication information","[{\"question\":\"What impact have affordable consumer RGB-D cameras had on 3D reconstruction?\",\"answer\":\"They have enabled a major advancement in visual scene reconstruction methods by making RGB-D sensing widely available at low cost and with real-time performance.\"},{\"question\":\"How does the report categorize recent progress in RGB-D scene reconstruction?\",\"answer\":\"It reviews developments for static geometry reconstruction, dynamic-scene and deformable motion reconstruction, and approaches that additionally estimate appearance such as material and lighting.\"},{\"question\":\"What key challenges do the methods address when using RGB-D data?\",\"answer\":\"The report focuses on exploiting RGB-D strengths for geometry while suppressing non-trivial sensor data distortions, including noise and other limitations.\"}]","State of the Art on 3D Reconstruction with RGB-D Cameras - State of TheArt Report | PDF",71]