{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127291"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127291","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Image-based pose estimation of sub-centimeter industrial parts for robotic grasping","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Dai, Yangfei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Bretl, Timothy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-10","date_published":"2024-12-10","updated_at":"2026-07-22T22:25:03Z","subjects":["Computer Vision","6dof Pose Estimation","Synthetic Data Generation"],"languages":["eng","en"],"rights":["Copyright 2024 Yangfei Dai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127291","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bretl, Timothy"]},{"key":"dc:creator","label":"Author","values":["Dai, Yangfei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-10","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"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 Vision","6dof Pose Estimation","Synthetic Data Generation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng","en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Yangfei Dai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127291"]}]},{"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 2025-03-28 without embargo terms","The student, Yangfei Dai, accepted the attached license on 2024-12-10 at 11:16.","The student, Yangfei Dai, submitted this Thesis for approval on 2024-12-10 at 11:25.","This Thesis was approved for publication on 2024-12-10 at 16:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21561 on 2025-03-28 at 14:28:40","This work integrates existing machine vision techniques with proposed refinement method to estimate the 6DoF pose of sub-centimeter parts from images for high-mix, low-volume assembly lines. In this system, the 3D models of three parts are input to a BlenderProc2 rendering engine to generate a physically- and photometrically-realistic synthetic image dataset. Synthetic images are used to train a Mask R-CNN model for segmenting individual part instances in a scene, with automatically-generated instance mask labels, eliminating the need for manual labeling. Instance segmentation enables part selection for assembly when multiple parts are present. Additionally, a PVNet model is trained on cropped images of each part instance from synthetic data to estimate their positions and orientations. An additional pose refinement step adjusts PVNet pose estimates by aligning the orientation to the nearest physically-stable configuration on a planar surface and refining the translation using calibrated object-to-camera distances from the workspace. To evaluate robustness, noise is injected into the keypoint detection stage of the PVNet model in an ablation study to assess the impact of sensor noise on pose estimation. Real robot pick-and-place experiments demonstrate the system performance."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Image-based pose estimation of sub-centimeter industrial parts for robotic grasping"]}]}],"canonical_facts":{"dc:contributor":["Bretl, Timothy"],"dc:creator":["Dai, Yangfei"],"dc:date":["2024-12-10","2024-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Yangfei Dai, accepted the attached license on 2024-12-10 at 11:16.","The student, Yangfei Dai, submitted this Thesis for approval on 2024-12-10 at 11:25.","This Thesis was approved for publication on 2024-12-10 at 16:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21561 on 2025-03-28 at 14:28:40","This work integrates existing machine vision techniques with proposed refinement method to estimate the 6DoF pose of sub-centimeter parts from images for high-mix, low-volume assembly lines. In this system, the 3D models of three parts are input to a BlenderProc2 rendering engine to generate a physically- and photometrically-realistic synthetic image dataset. Synthetic images are used to train a Mask R-CNN model for segmenting individual part instances in a scene, with automatically-generated instance mask labels, eliminating the need for manual labeling. Instance segmentation enables part selection for assembly when multiple parts are present. Additionally, a PVNet model is trained on cropped images of each part instance from synthetic data to estimate their positions and orientations. An additional pose refinement step adjusts PVNet pose estimates by aligning the orientation to the nearest physically-stable configuration on a planar surface and refining the translation using calibrated object-to-camera distances from the workspace. To evaluate robustness, noise is injected into the keypoint detection stage of the PVNet model in an ablation study to assess the impact of sensor noise on pose estimation. Real robot pick-and-place experiments demonstrate the system performance."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127291"],"dc:language":["eng","en"],"dc:rights":["Copyright 2024 Yangfei Dai"],"dc:subject":["Computer Vision","6dof Pose Estimation","Synthetic Data Generation"],"dc:title":["Image-based pose estimation of sub-centimeter industrial parts for robotic grasping"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}