{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:30:01Z","timestamp":1782282601951,"version":"3.54.5"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2010,12,1]],"date-time":"2010-12-01T00:00:00Z","timestamp":1291161600000},"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":[[2010,12]]},"abstract":"<jats:p>\n            We present a general framework for analyzing the transport matrix of a real-world scene at full resolution, without capturing many photos. The key idea is to use projectors and cameras to directly acquire eigenvectors and the Krylov subspace of the unknown transport matrix. To do this, we implement Krylov subspace methods partially in optics, by treating the scene as a \"black box subroutine\" that enables optical computation of arbitrary matrix-vector products. We describe two methods---\n            <jats:italic>optical Arnoldi<\/jats:italic>\n            to acquire a low-rank approximation of the transport matrix for relighting; and\n            <jats:italic>optical GMRES<\/jats:italic>\n            to invert light transport. Our experiments suggest that good quality relighting and transport inversion are possible from a few dozen low-dynamic range photos, even for scenes with complex shadows, caustics, and other challenging lighting effects.\n          <\/jats:p>","DOI":"10.1145\/1882261.1866165","type":"journal-article","created":{"date-parts":[[2010,12,1]],"date-time":"2010-12-01T20:18:10Z","timestamp":1291234690000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Optical computing for fast light transport analysis"],"prefix":"10.1145","volume":"29","author":[{"given":"Matthew","family":"O'Toole","sequence":"first","affiliation":[{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiriakos N.","family":"Kutulakos","sequence":"additional","affiliation":[{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2010,12,15]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1394622.1394629"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.849816"},{"key":"e_1_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Athale R. A. and Collins W. C. 1982. Optical matrixmatrix multiplier based on outer product decomposition.  Athale R. A. and Collins W. C. 1982. Optical matrixmatrix multiplier based on outer product decomposition.","DOI":"10.1364\/AO.21.002089"},{"key":"e_1_2_2_4_1","volume-title":"Proc. ECCV.","author":"Bai J."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1177153"},{"key":"e_1_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Casasent D. 1981. Hybrid processors. Optical Information Processing Fundamentals 181--233.  Casasent D. 1981. Hybrid processors. Optical Information Processing Fundamentals 181--233.","DOI":"10.1007\/3540105220_12"},{"key":"e_1_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Cederquist J. and Lee S. 1979. The use of feedback in optical information processing. Applied Physics A: Materials Science &amp; Processing.  Cederquist J. and Lee S. 1979. The use of feedback in optical information processing. 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