{"id":22,"date":"2023-05-10T07:39:25","date_gmt":"2023-05-10T07:39:25","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/?page_id=22"},"modified":"2023-12-19T22:41:20","modified_gmt":"2023-12-19T22:41:20","slug":"method","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/method\/","title":{"rendered":"Results"},"content":{"rendered":"\n<p><strong>SIREN<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"434\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img2_2-1024x434.png\" alt=\"\" class=\"wp-image-53\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img2_2-1024x434.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img2_2-300x127.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img2_2-768x325.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img2_2.png 1305w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"437\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img3_2-1024x437.png\" alt=\"\" class=\"wp-image-54\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img3_2-1024x437.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img3_2-300x128.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img3_2-768x327.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img3_2.png 1304w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>Although pure neural network (SIREN) demonstrated effective regularization, it tended to oversmooth the reconstruction results.<\/p>\n\n\n\n<p>[+] Fine regularization<\/p>\n\n\n\n<p>[-] Oversmoothing<\/p>\n\n\n\n<p><strong>SHINE-Mapping<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"435\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2_3-1024x435.png\" alt=\"\" class=\"wp-image-154\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2_3-1024x435.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2_3-300x127.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2_3-768x326.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2_3.png 1305w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1306\" height=\"555\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3_3.png\" alt=\"\" class=\"wp-image-157\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3_3.png 1306w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3_3-300x127.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3_3-1024x435.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3_3-768x326.png 768w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>While Poisson reconstruction exhibited a generally satisfactory level of reconstruction quality, it was prone to floating artifacts and exhibited limited adaptability to sparse regions.<\/p>\n\n\n\n<p>[+] Good global quality<\/p>\n\n\n\n<p>[+] Adaptive to Input<\/p>\n\n\n\n<p>[-] Strong local artifacts in certain scenes<\/p>\n\n\n\n<p>Puma<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"435\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2-1024x435.png\" alt=\"\" class=\"wp-image-161\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2-1024x435.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2-300x127.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2-768x326.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img2.png 1305w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"467\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3-1-1024x467.png\" alt=\"\" class=\"wp-image-159\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3-1-1024x467.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3-1-300x137.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3-1-768x350.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/12\/img3-1.png 1308w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>The combination of neural network and octree (SHINE) yielded impressive overall quality that adapted well to the input. However, the resulting reconstruction still exhibited notable strong local artifacts.<\/p>\n\n\n\n<p>[+] Overall good reconstruction result<\/p>\n\n\n\n<p>[-] Floating artifacts<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Future Works<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Exploit sequential information for better empty space\n<ul class=\"wp-block-list\">\n<li>Trim away the vertices in the reconstructed global mesh where the density is below a certain threshold<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Extend our method and test them on outdoor scenes<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>SIREN Although pure neural network (SIREN) demonstrated effective regularization, it tended to oversmooth the reconstruction results. [+] Fine regularization [-] Oversmoothing SHINE-Mapping While Poisson reconstruction exhibited a generally satisfactory level of reconstruction quality, it was prone to floating artifacts and exhibited limited adaptability to sparse regions. [+] Good global quality [+] Adaptive to Input [-] &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/method\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Results&#8221;<\/span><\/a><\/p>\n","protected":false},"author":165,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-22","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>Results - Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging<\/title>\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\/f23team6\/method\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Results - Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging\" \/>\n<meta property=\"og:description\" content=\"SIREN Although pure neural network (SIREN) demonstrated effective regularization, it tended to oversmooth the reconstruction results. [+] Fine regularization [-] Oversmoothing SHINE-Mapping While Poisson reconstruction exhibited a generally satisfactory level of reconstruction quality, it was prone to floating artifacts and exhibited limited adaptability to sparse regions. [+] Good global quality [+] Adaptive to Input [-] &hellip; Continue reading &quot;Results&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/method\/\" \/>\n<meta property=\"og:site_name\" content=\"Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-19T22:41:20+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/wp-content\/uploads\/sites\/83\/2023\/05\/img2_2-1024x434.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/\",\"name\":\"Results - Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/wp-content\\\/uploads\\\/sites\\\/83\\\/2023\\\/05\\\/img2_2-1024x434.png\",\"datePublished\":\"2023-05-10T07:39:25+00:00\",\"dateModified\":\"2023-12-19T22:41:20+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/#primaryimage\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/wp-content\\\/uploads\\\/sites\\\/83\\\/2023\\\/05\\\/img2_2-1024x434.png\",\"contentUrl\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/wp-content\\\/uploads\\\/sites\\\/83\\\/2023\\\/05\\\/img2_2-1024x434.png\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/method\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Results\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/#website\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/\",\"name\":\"Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging\",\"description\":\"Student: Xinyu Liu, Adviser: Prof. Matthew O&#039;Toole\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team6\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Results - Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/mscvprojects.ri.cmu.edu\/f23team6\/method\/","og_locale":"en_US","og_type":"article","og_title":"Results - Surface Reconstruction of Point Clouds from Low-Cost LiDARs Using Priors and Imaging","og_description":"SIREN Although pure neural network (SIREN) demonstrated effective regularization, it tended to oversmooth the reconstruction results. 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