{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115440"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115440","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Single image scene relighting","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_has_math":false,"creators":["Asthana, Pranav Kumar"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Forsyth, David A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["computer graphics","computer vision","relighting","self-supervised relighting","light transport"],"languages":["en","eng"],"rights":["Copyright 2022 Pranav Asthana"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115440","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David A"]},{"key":"dc:creator","label":"Author","values":["Asthana, Pranav Kumar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-28"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer graphics","computer vision","relighting","self-supervised relighting","light transport"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Pranav Asthana"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115440"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Pranav Asthana, accepted the attached license on 2022-04-22 at 11:47.","The student, Pranav Asthana, submitted this Thesis for approval on 2022-04-22 at 16:31.","This Thesis was approved for publication on 2022-04-28 at 09:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17921 on 2022-11-11 at 13:43:06","This thesis shows a relighting method that can relight a scene from a single image of that scene. It is well established that multiple distinctly lit images of a scene yield a light transport matrix that maps illuminants to shading fields and so to relit images. The work presented in this theses uses novel theory which says that one can use images of similar scenes to estimate the different lightings that apply to a given scene, with bounded expected error and explores various ways of selecting those images of similar scenes. This theory yields losses to train a relighting method using only unpaired images of scenes – multiple view data, Computer Generated Imagery (CGI) data or multiple relight data is not required for training. The method learns to map an image to a scene-specific relighting model consisting of a shading estimate, a light transport matrix, and a probability distribution over illuminants. The resulting model allows us to produce random relightings of a given scene that are plausible. The probability representation also allows us to produce relightings that are (a) close to a demand shading and (b) likely according to the predicted probability distribution, yielding controllable relighting. Qualitatively, the method’s relightings are easy to get for found data, but are not as good qualitatively as those obtained when multiple view or CGI data is available."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Single image scene relighting"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David A"],"dc:creator":["Asthana, Pranav Kumar"],"dc:date":["2022-05","2022-04-28"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Pranav Asthana, accepted the attached license on 2022-04-22 at 11:47.","The student, Pranav Asthana, submitted this Thesis for approval on 2022-04-22 at 16:31.","This Thesis was approved for publication on 2022-04-28 at 09:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17921 on 2022-11-11 at 13:43:06","This thesis shows a relighting method that can relight a scene from a single image of that scene. It is well established that multiple distinctly lit images of a scene yield a light transport matrix that maps illuminants to shading fields and so to relit images. The work presented in this theses uses novel theory which says that one can use images of similar scenes to estimate the different lightings that apply to a given scene, with bounded expected error and explores various ways of selecting those images of similar scenes. This theory yields losses to train a relighting method using only unpaired images of scenes – multiple view data, Computer Generated Imagery (CGI) data or multiple relight data is not required for training. The method learns to map an image to a scene-specific relighting model consisting of a shading estimate, a light transport matrix, and a probability distribution over illuminants. The resulting model allows us to produce random relightings of a given scene that are plausible. The probability representation also allows us to produce relightings that are (a) close to a demand shading and (b) likely according to the predicted probability distribution, yielding controllable relighting. Qualitatively, the method’s relightings are easy to get for found data, but are not as good qualitatively as those obtained when multiple view or CGI data is available."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115440"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Pranav Asthana"],"dc:subject":["computer graphics","computer vision","relighting","self-supervised relighting","light transport"],"dc:title":["Single image scene relighting"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}