[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84536-en":3,"doc-seo-84536-105":29,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84536,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","PRISM VO Scale Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment","PRISM-VO is a pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras, introducing a photometric plenoptic bundle adjustment that jointly optimizes camera poses and inverse depth within a sliding window. By modeling the plenoptic projection and fusing geometric depth from a single plenoptic image with temporal multi-view constraints, it delivers drift-resilient motion estimates. Explicit depth modeling enables reliable metric-scale reconstruction and mitigates monocular scale ambiguity without complex initialization, using only one plenoptic sensor.","arXiv :2607 .00176v1 [ cs .CV] 30 Jun 2026  \nPRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment  \nAymeric Fleith 1 ,2, Julian Zirbel 1 ,2, Daniel Cremers 1, and Niclas Zeller2  \n1 Technical University of Munich, Munich, Germany  \n{aymeric.fleith, julian.zirbel, [cremers}@tum.de](cremers}@tum.de)  \n2 Karlsruhe University of Applied Sciences, Karlsruhe, Germany  \n[niclas.zeller@h-ka.de](niclas.zeller@h-ka.de)  \nAbstract. We introduce PRISM-VO, a novel pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. The core of PRISM-VO is a novel photometric plenoptic bundle adjustment which jointly optimizes camera poses and inverse depth values of points in a sliding window. By combining geometric depth from a single plenoptic image with temporal multi-view constraints, PRISMVO achieves accurate and drift-resilient motion estimation. Through explicit modeling of the plenoptic projection, PRISM-VO provides reliable metric-scale reconstructions, overcoming the scale ambiguity of monocular SLAM algorithms. Importantly, our approach relies solely on a single plenoptic sensor and avoids complex initialization, as depth priors are computed directly from plenoptic imaging.  \nExperiments show that PRISM-VO outperforms the current state-ofthe-art plenoptic visual odometry method on indoor and outdoor scenes.  \nThe proposed approach rivals other optimization- and learning-based methods while accurately and reliably recovering a metric scale of the scene.  \nProject page: [https://prism-vo.github.io/](https://prism-vo.github.io/) .  \nKeywords: Plenoptic camera · Light field · Micro-lens array · Visual odometry · SLAM · Scale  \n1 Introduction  \n3D reconstruction of the environment and motion estimation are essential for real-time localization in robotics, autonomous vehicles, drones, virtual reality, and augmented reality. While lidars, radars, GPS, and inertial sensors are widely used, camera-based simultaneous localization and mapping (SLAM) and visual odometry (VO) have become widely adopted for mapping and localization. Passive cameras offer high resolution, rich visual information, versatility, and a cost-effective, lightweight, and compact sensing solution. However, monocular cameras still cannot recover absolute scale without additional information.  \nAlternatives such as stereo, RGB-D, and ToF cameras address the scale ambiguity of monocular vision but remain constrained by the stereo baseline, the structured light range, or the trade-off between range and accuracy. By placing  \n2 A. Fleith et al.  \nFig. 1: Metric 3D reconstruction of a 150 m long sequence (seq_ 007) from the dataset [39] using PRISM-VO. The zoom shows the accumulated drift over the whole sequence. The images below are examples of raw plenoptic images from the sequence.  \na micro-lens array (MLA) between the main lens and the sensor of a camera, a plenoptic camera extends conventional imaging by simultaneously capturing spatial and angular information of the scene. This produces multiple viewpointsin the form of micro-images, also offering a very wide depth of field. This makes them well suited for true-to-scale robust SLAM in compact systems.  \nTo exploit this advantage, we propose PRISM (Plenoptic Reconstruction via Inverse-depth Sparse Mapping) visual odometry, a new pure optimization-based photometric VO approach for focused plenoptic cameras. It combines disparity between micro-images for absolute scale estimation and larger baseline from temporal disparity to improve tracking accuracy and robustness. It enables reliable sparse metric reconstruction (see Fig. 1), which is not possible with a standard monocular camera. Our main contributions are:  \n– A sparse and photometric plenoptic bundle adjustment formulation that jointly optimizes camera poses and environment points.  \n– Tight integration of the plenoptic camera model into front-end tracking and back-end bundle adjustment of the VO pipeli","cbCainYRPwQiA4QV","https://ap.wps.com/l/cbCainYRPwQiA4QV","pdf",12062883,1,18,"English","en",105,"# Introduction\n## Classical SLAM Approaches\n## Plenoptic and Light-Field Vision","[{\"question\":\"What is PRISM-VO and what problem does it address?\",\"answer\":\"PRISM-VO is an optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. It targets accurate, drift-resilient motion estimation with reliable metric-scale reconstruction.\"},{\"question\":\"How does PRISM-VO achieve metric scale using a single sensor?\",\"answer\":\"It performs photometric plenoptic bundle adjustment that jointly optimizes camera poses and inverse depth. By leveraging geometric depth from a single plenoptic image together with temporal multi-view constraints, it overcomes monocular scale ambiguity without needing complex initialization.\"},{\"question\":\"What experimental outcome is reported for PRISM-VO?\",\"answer\":\"Experiments on indoor and outdoor scenes show that PRISM-VO outperforms existing state-of-the-art plenoptic visual odometry methods while rivaling other optimization- and learning-based approaches, with accurate and reliable metric-scale recovery.\"}]",1784196480,45,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"prism-vo-scale-aware-visual-odometry-using-photometric-plenoptic-bundle-adjustment","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/prism-vo-scale-aware-visual-odometry-using-photometric-plenoptic-bundle-adjustment/84536/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","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},"What is PRISM-VO and what problem does it address?","Question",{"text":75,"@type":76},"PRISM-VO is an optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. It targets accurate, drift-resilient motion estimation with reliable metric-scale reconstruction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does PRISM-VO achieve metric scale using a single sensor?",{"text":80,"@type":76},"It performs photometric plenoptic bundle adjustment that jointly optimizes camera poses and inverse depth. By leveraging geometric depth from a single plenoptic image together with temporal multi-view constraints, it overcomes monocular scale ambiguity without needing complex initialization.",{"name":82,"@type":73,"acceptedAnswer":83},"What experimental outcome is reported for PRISM-VO?",{"text":84,"@type":76},"Experiments on indoor and outdoor scenes show that PRISM-VO outperforms existing state-of-the-art plenoptic visual odometry methods while rivaling other optimization- and learning-based approaches, with accurate and reliable metric-scale recovery.","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":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]