{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115735"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115735","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Free space exploration from a single RGB image","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Issaranon, Theerasit"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Forsyth, David","Hoiem, Derek","Hasegawa-Johnson, Mark","Gupta, Saurabh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:55Z","subjects":["Occupancy Prediction","Single-Image","Space","Voxels","Depth"],"languages":["en","eng"],"rights":["Copyright 2022 Theerasit Issaranon"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115735","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David","Hoiem, Derek","Hasegawa-Johnson, Mark","Gupta, Saurabh"]},{"key":"dc:creator","label":"Author","values":["Issaranon, Theerasit"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-22"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Occupancy Prediction","Single-Image","Space","Voxels","Depth"]}]},{"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 Theerasit Issaranon"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115735"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Theerasit Issaranon, accepted the attached license on 2022-04-21 at 13:27.","The student, Theerasit Issaranon, submitted this Dissertation for approval on 2022-04-21 at 14:00.","This Dissertation was approved for publication on 2022-04-22 at 10:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17603 on 2022-11-11 at 12:57:55","From a single RGB image, we can infer many properties of the scene. One of them is depth. It is a well-established problem, and we are able to produce a result reliably. In our case, we are interested in the free space in the scene, whether it is visible or invisible in the RGB image. This dissertation reports the construction of network that is able to produce a high-quality free space map from a single RGB image. This is an interesting problem that minimal current research has explored. We also investigate several strategies to achieve this goal. From our experiments, we conclude the voxel occupancy map is the most suitable because it is the most flexible and a natural extension of a pixel depth map. Utilizing 3D convolution and separating features for depth and voxel yield the best result. With several techniques that we have experimented with, the result is an improvement of about 20% on the convolutional network baseline. Producing an occupancy map by using only an RGB is an interesting topic with many potential applications. Therefore, this topic is worth investigating in the future."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Free space exploration from a single RGB image"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David","Hoiem, Derek","Hasegawa-Johnson, Mark","Gupta, Saurabh"],"dc:creator":["Issaranon, Theerasit"],"dc:date":["2022-05","2022-04-22"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Theerasit Issaranon, accepted the attached license on 2022-04-21 at 13:27.","The student, Theerasit Issaranon, submitted this Dissertation for approval on 2022-04-21 at 14:00.","This Dissertation was approved for publication on 2022-04-22 at 10:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17603 on 2022-11-11 at 12:57:55","From a single RGB image, we can infer many properties of the scene. One of them is depth. It is a well-established problem, and we are able to produce a result reliably. In our case, we are interested in the free space in the scene, whether it is visible or invisible in the RGB image. This dissertation reports the construction of network that is able to produce a high-quality free space map from a single RGB image. This is an interesting problem that minimal current research has explored. We also investigate several strategies to achieve this goal. From our experiments, we conclude the voxel occupancy map is the most suitable because it is the most flexible and a natural extension of a pixel depth map. Utilizing 3D convolution and separating features for depth and voxel yield the best result. With several techniques that we have experimented with, the result is an improvement of about 20% on the convolutional network baseline. Producing an occupancy map by using only an RGB is an interesting topic with many potential applications. Therefore, this topic is worth investigating in the future."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115735"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Theerasit Issaranon"],"dc:subject":["Occupancy Prediction","Single-Image","Space","Voxels","Depth"],"dc:title":["Free space exploration from a single RGB image"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}