{"id":35,"date":"2022-04-29T03:19:53","date_gmt":"2022-04-29T03:19:53","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2022team7\/?page_id=35"},"modified":"2022-12-16T03:02:49","modified_gmt":"2022-12-16T03:02:49","slug":"project-summary","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2022team7\/","title":{"rendered":"Project Summary"},"content":{"rendered":"\n
\n
\n
\n\"input\"<\/a>\n
Input thermal images at night time<\/i><\/font><\/figcaption>\n<\/figure>\n<\/div>\n\n\n\n
\n
\n\"output\"<\/a>\n
Our predicted disparity<\/i><\/font><\/figcaption>\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n

Motivation<\/strong>
Depth estimation is one of the most critical tasks in a perception pipeline as most downstream components such as SLAM, object detection, and motion planner all require high-confidence depth estimation in order to effectively operate. However, vision-based depth estimation approaches face two issues: (1) Requires ground truth depth<\/strong> for supervision, (2) Datasets are heavily focused on day-time only<\/strong>. As such, we focus on evaluating and extending self-supervised <\/strong>depth estimation approaches to handle night-time environments.<\/strong><\/p>\n\n\n\n

Problem<\/strong>
Our task is to evaluate the performance of existing depth estimation approaches on night-time specific data, and reduce the gap between day-light and low-light operation. <\/p>\n\n\n\n

\"\"
Example Night Time Image from Oxford RobotCar Dataset<\/figcaption><\/figure><\/div>\n\n\n\n

Solution<\/strong>
Our primary approach is to utilize existing works in depth estimation and leverage multiple passive sensors that could operate in low-light or no-light, including thermal, light intensified cameras, and other IR cameras. Lastly, we will explore fusing data across these sensors to create an effective multi-modal stereo depth estimation pipeline for night-time operation.

Our code is available at https:\/\/github.com\/night-time-stereo\/mono-thermal<\/a><\/em>.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Input thermal images at night time Our predicted disparity MotivationDepth estimation is one of the most critical tasks in a perception pipeline as most downstream components such as SLAM, object detection, and motion planner all require high-confidence depth estimation in order to effectively operate. However, vision-based depth estimation approaches face two issues: (1) Requires ground … <\/p>\n

Continue reading “Project Summary”<\/span><\/a><\/p>\n","protected":false},"author":121,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-35","page","type-page","status-publish","hentry"],"yoast_head":"\nSelf-Supervised Depth Estimation with Thermal Images<\/title>\n<meta name=\"description\" content=\"Depth Estimation, Low Light, Autonomous Navigation, Autonomous Vehicles Monocular Depth, Self-Supervised Learning, Domain Adaptation\" \/>\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\/2022team7\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Self-Supervised Depth Estimation with Thermal Images\" \/>\n<meta property=\"og:description\" content=\"Depth 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