{"id":"W3024961437","doi":"10.1109/vrw50115.2020.00178","title":"Learning to Match 2D Images and 3D LiDAR Point Clouds for Outdoor Augmented Reality","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)","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":"","keywords":"Lidar; Point cloud; Computer science; Computer vision; Augmented reality; Artificial intelligence; Matching (statistics); Ranging; Point (geometry); Remote sensing; Feature (linguistics); Geography; Mathematics","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":[0.0007553487,0.001242208,0.001069089,0.001051664,0.0005710661,0.0009546799,0.002394446,0.001488731,0.002706527],"category_scores_gemma":[0.002263683,0.0007408166,0.00101851,0.001431649,0.0005455319,0.001942507,0.002482659,0.001208908,0.001251608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006303284,"about_ca_system_score_gemma":0.000944769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007139881,"about_ca_topic_score_gemma":0.01170435,"domain_scores_codex":[0.9991848,0.000110113,0.00003129589,0.0003567192,0.00021897,0.00009809619],"domain_scores_gemma":[0.9994668,0.0001111765,0.00006556275,0.0001746114,0.0001341688,0.00004775836],"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":[0.0004673405,0.000591071,0.00420671,0.0001069631,0.0001474635,0.0001680678,0.0001680804,0.2869261,0.0225769,0.002861443,0.005460251,0.6763196],"study_design_scores_gemma":[0.00001133776,0.00009760013,0.0006774204,0.000004249137,0.00001115818,0.00007324238,0.00004153151,0.9922264,0.004517991,0.00147005,0.0008598372,0.000009221803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04893785,0.0001929761,0.9460419,0.00009914715,0.00005886792,0.00009740768,0.0002546486,0.003124909,0.001192201],"genre_scores_gemma":[0.5394176,0.0002552682,0.4536832,0.0002347398,0.00006011763,0.000246186,0.002130566,0.0001973371,0.003774966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007139881,"threshold_uncertainty_score":0.01419669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05737359396329261,"score_gpt":0.2838452344023759,"score_spread":0.2264716404390832,"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."}}