{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:05:09Z","timestamp":1779260709737,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP22K18420"],"award-info":[{"award-number":["JP22K18420"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Multispectral long-wave infrared\u00a0(LWIR) ranging is a technique that estimates the distance to the object based on wavelength-dependent absorption of LWIR light through the air. Prior works require time-consuming measurements for calibration and solve non-linear inverse problems, which sometimes falls into a local minimum. In this paper, we propose a linear representation that connects the measurements and the scene parameters using the affine matrix. In this representation, the distance and the temperature of the object can be obtained as a closed-form solution and the calibration cost can be reduced to at least three observations. In real-world experiments, we demonstrate that our method is effective to reduce the calibration cost while keeping the precision of the depth estimation.<\/jats:p>","DOI":"10.1038\/s41598-024-77612-2","type":"journal-article","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T09:02:13Z","timestamp":1730538133000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Affine transform representation for reducing calibration cost on absorption-based LWIR depth sensing"],"prefix":"10.1038","volume":"14","author":[{"given":"Takahiro","family":"Kushida","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryutaro","family":"Nakamura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroaki","family":"Matsuda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenichiro","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,2]]},"reference":[{"key":"77612_CR1","doi-asserted-by":"crossref","unstructured":"Szeliski, R. Computer Vision: Algorithms and Applications Texts in Computer Science (Springer International Publishing, 2022).","DOI":"10.1007\/978-3-030-34372-9"},{"key":"77612_CR2","doi-asserted-by":"publisher","unstructured":"Nagase, Y., Kushida, T., Tanaka, K., Funatomi, T. & Mukaigawa, Y. Shape from thermal radiation: Passive ranging using multi-spectral lwir measurements. in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 12651\u201312661 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01233.","DOI":"10.1109\/CVPR52688.2022.01233"},{"key":"77612_CR3","unstructured":"Gallastegi, U.\u00a0D., Rueda-Chacon, H., Stevens, M. J. & Goyal, V. K. Absorption-based, passive range imaging from hyperspectral thermal measurements. arXiv preprint arXiv:2308.05818 (2023)."},{"key":"77612_CR4","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/TPAMI.2003.1217603","volume":"25","author":"M Brown","year":"2003","unstructured":"Brown, M., Burschka, D. & Hager, G. Advances in computational stereo. IEEE Trans. Patt. Anal. Mach. Intell. 25, 993\u20131008. https:\/\/doi.org\/10.1109\/TPAMI.2003.1217603 (2003).","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"77612_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3070917","author":"M Poggi","year":"2021","unstructured":"Poggi, M., Tosi, F., Batsos, K., Mordohai, P. & Mattoccia, S. On the synergies between machine learning and binocular stereo for depth estimation from images: A survey. IEEE Trans. Patt. Anal. Mach. Intell.[SPACE]https:\/\/doi.org\/10.1109\/TPAMI.2021.3070917 (2021).","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"77612_CR6","doi-asserted-by":"publisher","unstructured":"Lu, Y. & Lu, G. An alternative of LiDAR in nighttime: Unsupervised depth estimation based on single thermal image. in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) 3832\u20133842 (2021). https:\/\/doi.org\/10.1109\/WACV48630.2021.00388.","DOI":"10.1109\/WACV48630.2021.00388"},{"key":"77612_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1987.4767940","author":"AP Pentland","year":"1987","unstructured":"Pentland, A. P. A New sense for depth of field. IEEE Trans. Patt. Anal. Mach. Intell.[SPACE]https:\/\/doi.org\/10.1109\/TPAMI.1987.4767940 (1987).","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"77612_CR8","doi-asserted-by":"publisher","unstructured":"Carvalho, M., Le Saux, B., Trouv\u00e9-Peloux, P., Almansa, A. & Champagnat, F. Deep depth from defocus: How can defocus blur improve 3D estimation using dense neural networks? in Computer Vision: ECCV 2018 Workshops, vol. 11129, 307\u2013323 (2019). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_18.","DOI":"10.1007\/978-3-030-11009-3_18"},{"key":"77612_CR9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12297","author":"N Kim","year":"2018","unstructured":"Kim, N., Choi, Y., Hwang, S. & Kweon, I. S. Multispectral transfer network: Unsupervised depth estimation for all-day vision. Proc. AAAI Conf. Artif. Intell.[SPACE]https:\/\/doi.org\/10.1609\/aaai.v32i1.12297 (2018).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"77612_CR10","doi-asserted-by":"publisher","unstructured":"Yun, S. et al. STheReO: Stereo thermal dataset for research in odometry and mapping. in 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 3857\u20133864 (2022). https:\/\/doi.org\/10.1109\/IROS47612.2022.9981857.","DOI":"10.1109\/IROS47612.2022.9981857"},{"key":"77612_CR11","doi-asserted-by":"publisher","unstructured":"Shin, U., Park, J. & Kweon, I.\u00a0S. Deep depth estimation from thermal image. in 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 1043\u20131053 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.0010.","DOI":"10.1109\/CVPR52729.2023.0010"},{"key":"77612_CR12","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s00138-013-0570-5","volume":"25","author":"R Gade","year":"2014","unstructured":"Gade, R. & Moeslund, T. B. Thermal cameras and applications: A survey. Mach. Vis. Appl. 25, 245\u2013262. https:\/\/doi.org\/10.1007\/s00138-013-0570-5 (2014).","journal-title":"Mach. Vis. Appl."},{"key":"77612_CR13","doi-asserted-by":"publisher","unstructured":"Gallastegi, U. D., Rueda-Chacon, H., Stevens, M. J. & Goyal, V. K. Absorption-based ranging from ambient thermal radiation without known emissivities. in 2022 Conference on Lasers and Electro-Optics (CLEO) 1\u20132 (2022). https:\/\/doi.org\/10.1364\/CLEO_SI.2022.STh5J.3.","DOI":"10.1364\/CLEO_SI.2022.STh5J.3"},{"key":"77612_CR14","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1364\/OE.507927","volume":"32","author":"UD Gallastegi","year":"2024","unstructured":"Gallastegi, U. D., Rueda-Chac\u00f3n, H., Stevens, M. J. & Goyal, V. K. Absorption-based hyperspectral thermal ranging: Performance analyses, optimization, and simulations. Opt. Expr. 32, 151. https:\/\/doi.org\/10.1364\/OE.507927 (2024).","journal-title":"Opt. Expr."},{"key":"77612_CR15","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41586-023-06174-6","volume":"619","author":"F Bao","year":"2023","unstructured":"Bao, F. et al. Heat-assisted detection and ranging. Nature 619, 743\u2013748. https:\/\/doi.org\/10.1038\/s41586-023-06174-6 (2023).","journal-title":"Nature"},{"key":"77612_CR16","doi-asserted-by":"publisher","first-page":"103001","DOI":"10.1088\/2040-8986\/abb1cf","volume":"22","author":"N Hagen","year":"2020","unstructured":"Hagen, N. Survey of autonomous gas leak detection and quantification with snapshot infrared spectral imaging. J. Opt. 22, 103001. https:\/\/doi.org\/10.1088\/2040-8986\/abb1cf (2020).","journal-title":"J. Opt."},{"key":"77612_CR17","doi-asserted-by":"publisher","unstructured":"Tremblay, P. et al. Standoff gas identification and quantification from turbulent stack plumes with an imaging Fourier-transform spectrometer. in SPIE Defense, Security, and Sensing (eds Vo-Dinh, T., Lieberman, R.\u00a0A. & Gauglitz, G.) , 76730H (2010). https:\/\/doi.org\/10.1117\/12.850127.","DOI":"10.1117\/12.850127"},{"key":"77612_CR18","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1364\/OE.6.000092","volume":"6","author":"J Sandsten","year":"2000","unstructured":"Sandsten, J., Weibring, P., Edner, H. & Svanberg, S. Real-time gas-correlation imaging employing thermal background radiation. Opt. Expr. 6, 92. https:\/\/doi.org\/10.1364\/OE.6.000092 (2000).","journal-title":"Opt. Expr."},{"key":"77612_CR19","doi-asserted-by":"publisher","unstructured":"Naranjo, E., Baliga, S. & Bernascolle, P. IR gas imaging in an industrial setting. in SPIE Defense, Security, and Sensing (eds Dinwiddie, R.\u00a0B. & Safai, M.) , 76610K (Orlando, Florida, 2010) https:\/\/doi.org\/10.1117\/12.850137.","DOI":"10.1117\/12.850137"},{"key":"77612_CR20","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1364\/AO.32.004037","volume":"32","author":"TG McRae","year":"1993","unstructured":"McRae, T. G. & Kulp, T. J. Backscatter absorption gas imaging: A new technique for gas visualization. Appl. Opt. 32, 4037. https:\/\/doi.org\/10.1364\/AO.32.004037 (1993).","journal-title":"Appl. Opt."},{"key":"77612_CR21","doi-asserted-by":"publisher","unstructured":"Ohel, E. et al. IEEE 24th Convention of Electrical & Electronics Engineers in Israel 285\u2013289 (2006). https:\/\/doi.org\/10.1109\/EEEI.2006.321072.","DOI":"10.1109\/EEEI.2006.321072"},{"key":"77612_CR22","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.rse.2015.04.001","volume":"163","author":"A Eisele","year":"2015","unstructured":"Eisele, A. et al. Advantages using the thermal infrared (TIR) to detect and quantify semi-arid soil properties. Remote Sens. Environ. 163, 296\u2013311. https:\/\/doi.org\/10.1016\/j.rse.2015.04.001 (2015).","journal-title":"Remote Sens. Environ."},{"key":"77612_CR23","doi-asserted-by":"publisher","first-page":"3744","DOI":"10.1080\/01431161.2022.2105667","volume":"43","author":"L Cao","year":"2022","unstructured":"Cao, L. et al. LWIR hyperspectral image classification based on a temperature-emissivity residual network and conditional random field model. Int. J. Remote Sens. 43, 3744\u20133768. https:\/\/doi.org\/10.1080\/01431161.2022.2105667 (2022).","journal-title":"Int. J. Remote Sens."},{"key":"77612_CR24","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.jag.2011.08.002","volume":"14","author":"FD Van Der Meer","year":"2012","unstructured":"Van Der Meer, F. D. et al. Multi- and hyperspectral geologic remote sensing: A review. Int. J. Appl. Earth Obs. Geoinform. 14, 112\u2013128. https:\/\/doi.org\/10.1016\/j.jag.2011.08.002 (2012).","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"77612_CR25","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/S0034-4257(02)00186-4","volume":"85","author":"R Vaughan","year":"2003","unstructured":"Vaughan, R., Calvin, W. M. & Taranik, J. V. SEBASS hyperspectral thermal infrared data: Surface emissivity measurement and mineral mapping. Remote Sens. Environ. 85, 48\u201363. https:\/\/doi.org\/10.1016\/S0034-4257(02)00186-4 (2003).","journal-title":"Remote Sens. Environ."},{"key":"77612_CR26","doi-asserted-by":"publisher","first-page":"102415","DOI":"10.1016\/j.jag.2021.102415","volume":"102","author":"E Neinavaz","year":"2021","unstructured":"Neinavaz, E., Schlerf, M., Darvishzadeh, R., Gerhards, M. & Skidmore, A. K. Thermal infrared remote sensing of vegetation: Current status and perspectives. Int. J. Appl. Earth Obs. Geoinform. 102, 102415. https:\/\/doi.org\/10.1016\/j.jag.2021.102415 (2021).","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"77612_CR27","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MGRS.2018.2889610","volume":"7","author":"D Manolakis","year":"2019","unstructured":"Manolakis, D. et al. Longwave infrared hyperspectral imaging: Principles, progress, and challenges. IEEE Geosci. Remote Sens. Mag. 7, 72\u2013100. https:\/\/doi.org\/10.1109\/MGRS.2018.2889610 (2019).","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"77612_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/9783527693306","volume-title":"Infrared Thermal Imaging: Fundamentals, Research and Applications","author":"M Vollmer","year":"2017","unstructured":"Vollmer, M. & M\u00f6llmann, K.-P. Infrared Thermal Imaging: Fundamentals, Research and Applications 2nd edn. (Wiley-VCH Verlag GmbH & Co, KGaA, 2017).","edition":"2"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77612-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77612-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77612-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T12:05:29Z","timestamp":1730808329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77612-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,2]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["77612"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-77612-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,2]]},"assertion":[{"value":"22 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26429"}}