{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:29:56Z","timestamp":1763663396426,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2015,11,2]],"date-time":"2015-11-02T00:00:00Z","timestamp":1446422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2015,11,4]]},"abstract":"<jats:p>We propose a novel three-dimensional motion sensing method using lasers. Recently, object motion information is being used in various applications, and the types of targets that can be sensed continue to diversify. Nevertheless, conventional motion sensing systems have low universality because they require some devices to be mounted on the target, such as accelerometers and gyro sensors, or because they are based on cameras, which limits the types of targets that can be detected. Our method solves this problem and enables noncontact, high-speed, deterministic measurement of the velocity of a moving target without any prior knowledge about the target shape and texture, and can be applied to any unconstrained, unspecified target. These distinctive features are achieved by using a system consisting of a laser range finder, a laser Doppler velocimeter, and a beam controller, in addition to a robust 3D motion calculation method. The motion of the target is recovered from fragmentary physical information, such as the distance and speed of the target at the laser irradiation points. From the acquired laser information, our method can provide a numerically stable solution based on the generalized weighted Tikhonov regularization. Using this technique and a prototype system that we developed, we also demonstrated a number of applications, including motion capture, video game control, and 3D shape integration with everyday objects.<\/jats:p>","DOI":"10.1145\/2816795.2818133","type":"journal-article","created":{"date-parts":[[2015,10,27]],"date-time":"2015-10-27T12:36:39Z","timestamp":1445949399000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["3D motion sensing of any object without prior knowledge"],"prefix":"10.1145","volume":"34","author":[{"given":"Leo","family":"Miyashita","sequence":"first","affiliation":[{"name":"University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryota","family":"Yonezawa","sequence":"additional","affiliation":[{"name":"University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihiro","family":"Watanabe","sequence":"additional","affiliation":[{"name":"University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masatoshi","family":"Ishikawa","sequence":"additional","affiliation":[{"name":"University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,11,2]]},"reference":[{"volume-title":"Proceedings of the British Machine Vision Conference (BMVC '13)","author":"Alcantarilla P. 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Yagola A. and Yang C. 2012. Computational Methods for Applied Inverse Problems. Higher Education Press.   Wang Y. Yagola A. and Yang C. 2012. Computational Methods for Applied Inverse Problems. 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