{"id":"W4313397047","doi":"10.1049/ipr2.12729","title":"A survey on end‐to‐end point cloud learning","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Computer science; Cloud computing; Deep learning; Segmentation; Artificial intelligence; Focus (optics); Point (geometry); Domain (mathematical analysis); End-to-end principle; Data mining; Machine learning; Data science; Tracking (education)","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":[0.00171786,0.001613809,0.001762943,0.002291602,0.0004597297,0.002189178,0.004071333,0.002010213,0.004678315],"category_scores_gemma":[0.005323886,0.0007073905,0.001525398,0.004115093,0.0005419331,0.003604081,0.001937341,0.001696416,0.003789181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006986367,"about_ca_system_score_gemma":0.001264509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005553534,"about_ca_topic_score_gemma":0.002683566,"domain_scores_codex":[0.998449,0.0002304206,0.0001498526,0.0004228319,0.0006350574,0.000112729],"domain_scores_gemma":[0.9981825,0.0008027941,0.00007949071,0.00028106,0.0005935107,0.00006067489],"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":[0.0001266441,0.0001577344,0.001883815,0.001198523,0.0001150412,0.00007052597,0.00004389886,0.03616204,0.001178708,0.005568013,0.02043131,0.9330637],"study_design_scores_gemma":[0.00004047467,0.0003692485,0.004946062,0.001476303,0.0001325157,0.0005933478,0.000157853,0.8158904,0.01148966,0.03181452,0.1329701,0.0001195913],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.00987388,0.08939631,0.8819929,0.001069477,0.0006491555,0.0002207533,0.001860953,0.004462313,0.01047425],"genre_scores_gemma":[0.2041796,0.1808204,0.5771721,0.001777575,0.00149677,0.0007630337,0.01620661,0.00140109,0.01618277],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005553534,"threshold_uncertainty_score":0.01565051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461046973289131,"score_gpt":0.2596159498532288,"score_spread":0.2450054801203375,"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."}}