{"id":"W2022922662","doi":"10.1145/2508363.2508393","title":"Projective analysis for 3D shape segmentation","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Memorial University of Newfoundland","funders":"Science and Technology Planning Project of Guangdong Province; Israel Science Foundation; Ministry of Science and Technology of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Shape analysis (program analysis); Segmentation; Computer science; Projection (relational algebra); Artificial intelligence; Piecewise; Computer vision; Mathematics; Pattern recognition (psychology); Topology (electrical circuits); Algorithm; Combinatorics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001141478,0.0001083142,0.0001189179,0.0005536318,0.0003626217,0.000196816,0.0003219603,0.00006793715,0.00003528237],"category_scores_gemma":[0.00001609164,0.00009983483,0.0001656069,0.00157322,0.00004970529,0.0007797721,0.000003704947,0.0001266706,0.00001867217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002550427,"about_ca_system_score_gemma":0.00005269602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004620296,"about_ca_topic_score_gemma":0.0000284804,"domain_scores_codex":[0.9991721,0.00003261224,0.0001636384,0.0003116044,0.0001516258,0.0001684028],"domain_scores_gemma":[0.9991589,0.0001155803,0.0000729009,0.0003777171,0.0002289886,0.00004593143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008634423,0.0001364363,0.0005236191,0.0000258299,0.0003966639,2.644958e-7,0.0007549644,0.0008493944,0.0005462288,0.0008500463,0.00007667949,0.9958313],"study_design_scores_gemma":[0.0007507876,0.0002906362,0.005916452,0.00002104851,0.000444461,0.00001589816,0.0003408426,0.9466,0.0119912,0.03288237,0.0003215012,0.0004248715],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008806698,0.00001787333,0.9894413,0.0008664905,0.0002504983,0.0003379523,0.000006437913,0.0001671375,0.0001056006],"genre_scores_gemma":[0.5526223,0.00002282898,0.4465785,0.000287563,0.00002033363,0.0003148231,0.000007091676,0.000007592914,0.0001389505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9954064,"threshold_uncertainty_score":0.4071147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160198708998208,"score_gpt":0.2657111968470783,"score_spread":0.2441092097570962,"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."}}