{"id":"W4413145141","doi":"10.1109/cvpr52734.2025.01312","title":"Insightful Instance Features for 3D Instance Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Korea University","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology)","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":[0.0007722044,0.001308647,0.001388784,0.002405086,0.0004106796,0.002038614,0.002249369,0.001347883,0.003449394],"category_scores_gemma":[0.003440233,0.0006310224,0.001620108,0.002022753,0.0008265974,0.002998082,0.00229171,0.002004151,0.001668688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008233838,"about_ca_system_score_gemma":0.0008049135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003123735,"about_ca_topic_score_gemma":0.005979613,"domain_scores_codex":[0.9993522,0.00008980792,0.00003684371,0.0002013718,0.00024431,0.00007556062],"domain_scores_gemma":[0.9991,0.0002252195,0.0001121762,0.0003559447,0.0001397648,0.00006702088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003700171,0.0002008963,0.00396635,0.000307599,0.0001836029,0.0002375488,0.0002620508,0.2525606,0.03489653,0.0272683,0.01789743,0.6618491],"study_design_scores_gemma":[0.00001728537,0.0000330465,0.000604198,0.00001675089,0.00002479995,0.0001456041,0.00005063296,0.9716595,0.009291523,0.0137007,0.004436058,0.00001979488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01127905,0.0002245547,0.9798421,0.0001012447,0.00002394181,0.00005294508,0.0005408201,0.007117894,0.0008175428],"genre_scores_gemma":[0.246373,0.0002921417,0.7473506,0.0001899299,0.00004762478,0.0001002667,0.003676426,0.001033948,0.0009360814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003449394,"threshold_uncertainty_score":0.01153934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009723518938488768,"score_gpt":0.2474587054135016,"score_spread":0.2377351864750128,"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."}}