{"id":"W1992850481","doi":"10.3390/s141224156","title":"Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Clutter; Point cloud; Computer science; Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Feature (linguistics); Scale (ratio); Cognitive neuroscience of visual object recognition; Point (geometry); Object (grammar); Computer vision; Task (project management); Mathematics; Radar","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":[0.0007017854,0.001373565,0.001563951,0.003622408,0.0006673431,0.002001292,0.002354698,0.001528162,0.001716064],"category_scores_gemma":[0.001974042,0.0009859796,0.001929161,0.003413754,0.0007622485,0.002860549,0.002199454,0.001403269,0.002800374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006733779,"about_ca_system_score_gemma":0.0007314679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00668569,"about_ca_topic_score_gemma":0.01302779,"domain_scores_codex":[0.9986405,0.0000893018,0.00006916917,0.0003807101,0.0006892373,0.0001310286],"domain_scores_gemma":[0.9986714,0.0002386625,0.000200684,0.0005744578,0.0002429991,0.00007178767],"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":[0.0001590966,0.0001568052,0.003598508,0.0002515116,0.0001591126,0.000241982,0.0001683787,0.07063241,0.05885077,0.001224569,0.003295416,0.8612614],"study_design_scores_gemma":[0.00001850398,0.000103872,0.006641376,0.00004207767,0.00005074428,0.0007084243,0.0002272525,0.9410552,0.04190034,0.004671093,0.004516663,0.00006453451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01825476,0.0003246804,0.9747847,0.00006295332,0.00003425965,0.0001053525,0.0003081535,0.005560491,0.0005645745],"genre_scores_gemma":[0.2543139,0.0006012662,0.7412878,0.000125259,0.0000449883,0.0001417771,0.00199755,0.0003073704,0.00118009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00668569,"threshold_uncertainty_score":0.01329356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322148738787461,"score_gpt":0.2033312008258035,"score_spread":0.1901097134379289,"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."}}