{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pst900-rgb-thermal-calibration-dataset-and","title":"PST900: RGB-Thermal Calibration, Dataset and Segmentation Network","arxiv_id":"1909.10980","date":"2019-09-20","proceeding":null,"authors":["Shreyas S. Shivakumar","Neil Rodrigues","Alex Zhou","Ian D. Miller","Vijay Kumar","Camillo J. Taylor"],"abstract":"In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques. We first address the problem of RGB-thermal camera calibration by proposing a passive calibration target and procedure that is both portable and easy to use. Second, we present PST900, a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Lastly, we propose a CNN architecture for fast semantic segmentation that combines both RGB and Thermal imagery in a way that leverages RGB imagery independently. We compare our method against the state-of-the-art and show that our method outperforms them in our dataset.","url_abs":"https://arxiv.org/abs/1909.10980v1","url_pdf":"https://arxiv.org/pdf/1909.10980v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pst900-rgb-thermal-calibration-dataset-and","repo_url":"https://github.com/ShreyasSkandanS/pst900_thermal_rgb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"thermal-image-segmentation","task_name":"Thermal Image Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"pst900","name":"PST900","full_name":null}],"methods_introduced":[],"results":[{"leaderboard":"/sota/thermal-image-segmentation-on-mfn-dataset","task":"Thermal Image Segmentation","dataset":"MFN Dataset","model":"PST900","rank_in_archive_order":43,"of":55,"metrics":{"mIOU":"48.4"},"uses_additional_data":false},{"leaderboard":"/sota/thermal-image-segmentation-on-pst900","task":"Thermal Image Segmentation","dataset":"PST900","model":"PSTNet","rank_in_archive_order":18,"of":22,"metrics":{"mIoU":"68.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.10980","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}