{"id":"W4413158040","doi":"10.1109/cvpr52734.2025.01568","title":"ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation","year":2025,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Pose; Artificial intelligence; Computer science; Domain adaptation; Computer vision; Object (grammar); 3D pose estimation; Adaptation (eye); Domain (mathematical analysis); Pattern recognition (psychology); Occlusion; Estimation; Artificial neural network; Mathematics; Psychology; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101283,0.001765606,0.001311818,0.0008168112,0.0003595417,0.0007466321,0.002678131,0.001363369,0.002978309],"category_scores_gemma":[0.002301761,0.000700628,0.001201537,0.0007560066,0.0007194342,0.001244424,0.001588405,0.001808857,0.001755817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005681206,"about_ca_system_score_gemma":0.0009053524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004286836,"about_ca_topic_score_gemma":0.007782536,"domain_scores_codex":[0.9992326,0.0001395931,0.00002980767,0.0003021942,0.0002171044,0.00007866565],"domain_scores_gemma":[0.9991165,0.0002284661,0.00010703,0.0002685199,0.0002151423,0.00006435798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002911524,0.0003871552,0.001801706,0.0001877759,0.0001653892,0.0001319384,0.0001388181,0.2548429,0.02103103,0.001989897,0.01067176,0.7083606],"study_design_scores_gemma":[0.00001616464,0.00005369547,0.0004734588,0.00001021279,0.00001131633,0.00006904887,0.00001853006,0.9924167,0.003721568,0.001351607,0.001844999,0.00001268797],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02431564,0.0005633024,0.9665719,0.0001002992,0.000137334,0.0001249242,0.0002662699,0.005913549,0.002006843],"genre_scores_gemma":[0.3868672,0.0006706489,0.593295,0.0007568933,0.000200171,0.000429096,0.005151969,0.0009583278,0.01167075],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004286836,"threshold_uncertainty_score":0.009963393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125116089557823,"score_gpt":0.2488395491798915,"score_spread":0.2363279402241092,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}