{"id":20,"date":"2024-05-12T20:16:21","date_gmt":"2024-05-12T20:16:21","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/?page_id=20"},"modified":"2024-12-15T00:50:39","modified_gmt":"2024-12-15T00:50:39","slug":"methods","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/methods\/","title":{"rendered":"Challenges with existing methods"},"content":{"rendered":"\n<p>Gaussian splatting struggles to reconstruct indoor office environments with textureless planar surfaces, as shown in Figure 3. One key reason for reconstruction challenges is the poor point cloud initialization by Structure-from-Motion (SfM), which fails to generate reliable points in these textureless regions (Figure 4).<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"894\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.52.37\u202fPM-1024x894.png\" alt=\"\" class=\"wp-image-137\" style=\"width:609px;height:auto\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.52.37\u202fPM-1024x894.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.52.37\u202fPM-300x262.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.52.37\u202fPM-768x670.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.52.37\u202fPM.png 1052w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p class=\"has-small-font-size\">Figure 3: Effect of initialization on reconstruction quality. The left image shows a good reconstruction when points are sampled from the ground truth mesh. In contrast, using COLMAP to obtain the point cloud results in missing points on planar regions, leading to inaccurate geometry and unwanted artifacts<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"816\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.43.27\u202fPM-1024x816.png\" alt=\"\" class=\"wp-image-134\" style=\"width:596px;height:auto\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.43.27\u202fPM-1024x816.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.43.27\u202fPM-300x239.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.43.27\u202fPM-768x612.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/wp-content\/uploads\/sites\/100\/2024\/12\/Screenshot-2024-12-14-at-6.43.27\u202fPM.png 1062w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p class=\"has-small-font-size\">Figure 4:  SfM point clouds fail to produce points in planar textureless regions<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Gaussian splatting struggles to reconstruct indoor office environments with textureless planar surfaces, as shown in Figure 3. One key reason for reconstruction challenges is the poor point cloud initialization by Structure-from-Motion (SfM), which fails to generate reliable points in these textureless regions (Figure 4). Figure 3: Effect of initialization on reconstruction quality. The left image &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team2\/methods\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Challenges with existing methods&#8221;<\/span><\/a><\/p>\n","protected":false},"author":194,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-20","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Challenges with existing methods - Generating Real-time Egocentric Viewpoint Scenes<\/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\/2024team2\/methods\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Challenges with existing methods - Generating Real-time Egocentric Viewpoint Scenes\" \/>\n<meta property=\"og:description\" content=\"Gaussian splatting struggles to reconstruct indoor office environments with textureless planar surfaces, as shown in Figure 3. One key reason for reconstruction challenges is the poor point cloud initialization by Structure-from-Motion (SfM), which fails to generate reliable points in these textureless regions (Figure 4). Figure 3: Effect of initialization on reconstruction quality. 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One key reason for reconstruction challenges is the poor point cloud initialization by Structure-from-Motion (SfM), which fails to generate reliable points in these textureless regions (Figure 4). Figure 3: Effect of initialization on reconstruction quality. 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