{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/114021"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/114021","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Not all views are equal: active neural radiance fields","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","abstract_has_math":false,"creators":["Bojja, Bharat"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:47:46Z","date_published":"2022-04-29T21:47:46Z","updated_at":"2026-07-22T22:24:54Z","subjects":["Computer science"],"languages":["en","eng"],"rights":["Copyright 2021 Bharat Bojja"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/114021","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Yuxiong"]},{"key":"dc:creator","label":"Author","values":["Bojja, Bharat"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:47:46Z","2024-04-29T21:47:53Z","2021-12","2021-12-09"]},{"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 science"]}]},{"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 2021 Bharat Bojja"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/114021"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","The student, Bharat Bojja, accepted the attached license on 2021-12-08 at 15:11.","The student, Bharat Bojja, submitted this Thesis for approval on 2021-12-08 at 15:16.","This Thesis was approved for publication on 2021-12-09 at 08:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17419 on 2022-04-06 at 17:18:03","Made available in DSpace on 2022-04-29T21:47:46Z (GMT). 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In this paper, we show that NeRF does not necessarily require many image views to achieve quality view synthesis if there is a way to identify the views that are most informative. There is currently no method that actively identifies how informative each view is to the eventual scene reconstruction. This is especially important when the dataset is extremely large, and it would be computationally expensive to train the model on all the images. We propose a reinforcement learning framework using the REINFORCE algorithm to actively select which viewpoints yield in the best view synthesis. We also investigate the Monte-Carlo Tree Search method as a potentially promising approach. Our methods demonstrates that it is possible achieve a dramatic improvement in performance by actively selecting a limited number of views."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Not all views are equal: active neural radiance fields"]}]}],"canonical_facts":{"dc:contributor":["Wang, Yuxiong"],"dc:creator":["Bojja, Bharat"],"dc:date":["2022-04-29T21:47:46Z","2024-04-29T21:47:53Z","2021-12","2021-12-09"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","The student, Bharat Bojja, accepted the attached license on 2021-12-08 at 15:11.","The student, Bharat Bojja, submitted this Thesis for approval on 2021-12-08 at 15:16.","This Thesis was approved for publication on 2021-12-09 at 08:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17419 on 2022-04-06 at 17:18:03","Made available in DSpace on 2022-04-29T21:47:46Z (GMT). No. of bitstreams: 2 BOJJA-THESIS-2021.pdf: 3056620 bytes, checksum: d44fafba3872d5082bbc1a9e1a0c8b70 (MD5) LICENSE.txt: 4209 bytes, checksum: 256e8c82c1a931da284d76da258a31af (MD5) Previous issue date: 2021-12-09","Embargo set by: Seth Robbins for item 123386 Lift date: 2024-04-29T21:47:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","By modelling complex scenes via a continuous volumetric scene function, neural radiance fields (NeRF) achieves state-of-the-art results in novel view synthesis. However, since NeRF does not take into account prior information about the object content in the scene, it requires many image views and significant training time to achieve high-quality view synthesis. To reduce the number of views required, some follow-up works build on the NeRF model to take into account depth and appearance information. In this paper, we show that NeRF does not necessarily require many image views to achieve quality view synthesis if there is a way to identify the views that are most informative. There is currently no method that actively identifies how informative each view is to the eventual scene reconstruction. This is especially important when the dataset is extremely large, and it would be computationally expensive to train the model on all the images. We propose a reinforcement learning framework using the REINFORCE algorithm to actively select which viewpoints yield in the best view synthesis. We also investigate the Monte-Carlo Tree Search method as a potentially promising approach. 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