{"id":11,"date":"2024-04-29T22:38:35","date_gmt":"2024-04-29T22:38:35","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/?page_id=11"},"modified":"2024-12-15T03:44:16","modified_gmt":"2024-12-15T03:44:16","slug":"large-scale-camera-array-calibration-via-sfm","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/","title":{"rendered":"About"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Overview<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Build an<strong> automated calibration <\/strong>system based on an efficient <strong>Structure-from-Motion (SfM)<\/strong> pipeline to estimate intrinsic camera parameters.<\/li>\n\n\n\n<li>Enhance accuracy by applying <strong>featuremetric<\/strong> <strong>refinement <\/strong>to detected keypoints and triangulated 3D points.<\/li>\n\n\n\n<li>Incorporate <strong>known ground-truth extrinsic<\/strong> in order to refine intrinsic estimates.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Background<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"477\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/about-1024x477.jpg\" alt=\"\" class=\"wp-image-65\" style=\"width:673px\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/about-1024x477.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/about-300x140.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/about-768x358.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/about.jpg 1116w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Human face data captured by hundreds of cameras across hundreds of frames require precise calibration.<\/li>\n\n\n\n<li><strong>Extrinsics remain stable<\/strong> over a short period (e.g. 1 day ~ 1 week).<\/li>\n\n\n\n<li><strong>Intrinsics fluctuate<\/strong> frequently due to environmental factors.<\/li>\n<\/ul>\n\n\n\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1446\" height=\"422\" class=\"wp-image-71\" style=\"width: 900px\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/regular_pattern.jpg\" alt=\"\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/regular_pattern.jpg 1446w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/regular_pattern-300x88.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/regular_pattern-1024x299.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/regular_pattern-768x224.jpg 768w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Traditional calibration using <strong>regular patterns<\/strong> achieves high precision but demands an additional capture process, which is <strong>time-consuming <\/strong>and <strong>inefficient<\/strong>.<\/li>\n\n\n\n<li>An efficient <strong>SfM pipeline<\/strong> is proposed to directly calibrate cameras using human face data, aligning with the system&#8217;s ultimate purpose of facial data capture.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Dataset<\/h2>\n\n\n\n<p><img loading=\"lazy\" decoding=\"async\" width=\"977\" height=\"305\" class=\"wp-image-72\" style=\"width: 900px\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/multiface.jpg\" alt=\"\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/multiface.jpg 977w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/multiface-300x94.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/wp-content\/uploads\/sites\/102\/2024\/05\/multiface-768x240.jpg 768w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The <strong><em>Multiface<\/em><\/strong> dataset from Meta Reality Labs is used for our calibration process. This dataset is designed for large-scale multi-view codec avatar tasks, particularly neural face rendering.<\/li>\n\n\n\n<li>The dataset includes <strong>10+<\/strong> <strong>identities<\/strong> recorded with various facial expressions, raw images, tracked meshes, unwrapped textures, headposes, phonetically balanced sentences for each identity, and ground-truth camera parameters.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Overview Background Dataset<\/p>\n","protected":false},"author":200,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>About - Large Scale Camera Array Calibration via SfM<\/title>\n<meta name=\"description\" content=\"CMU MSCV Capstone project with Meta to build an auto-calibration system and an efficient SfM pipeline to obtain the intrinsic camera parameters.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team4\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"About - 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