{"id":"W2127004595","doi":"10.1109/lgrs.2013.2285237","title":"Pairwise Three-Dimensional Shape Context for Partial Object Matching and Retrieval on Mobile Laser Scanning Data","year":2013,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Xiamen University","keywords":"Shape context; Point cloud; Pairwise comparison; Artificial intelligence; Context (archaeology); Computer vision; Active shape model; Histogram; Matching (statistics); Computer science; Orientation (vector space); Pattern recognition (psychology); Mathematics; Heat kernel signature; Topology (electrical circuits); Spatial contextual awareness; Manifold (fluid mechanics); Geometry; Image (mathematics); Segmentation; Combinatorics; Geography","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":[0.0003630067,0.0007258391,0.00148725,0.002796158,0.0006821644,0.0008487073,0.001587643,0.000808471,0.0009706661],"category_scores_gemma":[0.001549665,0.0003983161,0.001169167,0.003362583,0.0004113429,0.001551012,0.002261655,0.0007557282,0.000815568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005131023,"about_ca_system_score_gemma":0.0008299514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004340795,"about_ca_topic_score_gemma":0.008783774,"domain_scores_codex":[0.9992106,0.00008136502,0.00003759868,0.0002092383,0.0003724855,0.00008869288],"domain_scores_gemma":[0.999527,0.00008725005,0.00006926842,0.0001598649,0.0001235146,0.00003315766],"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":[0.000306053,0.0001156448,0.003488528,0.00013534,0.0001020626,0.0002454242,0.0001388908,0.04859202,0.1098072,0.003977307,0.002535116,0.8305563],"study_design_scores_gemma":[0.00002378579,0.0001583875,0.004081895,0.00001804587,0.00004793986,0.0006283522,0.0001260877,0.9495227,0.03596603,0.004501261,0.004874808,0.00005064091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06125221,0.0006505036,0.9351825,0.00006631941,0.00003901641,0.00009295059,0.0002832156,0.001831083,0.000602323],"genre_scores_gemma":[0.3900899,0.0004382197,0.6069287,0.0001139383,0.00007374007,0.0001953968,0.00132916,0.0001619267,0.0006689745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004340795,"threshold_uncertainty_score":0.008631051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126067828551109,"score_gpt":0.2487644831022524,"score_spread":0.2275038048167413,"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."}}