{"id":"W4281695986","doi":"10.5194/isprs-archives-xliii-b2-2022-343-2022","title":"PROBABILISTIC SILHOUETTE-BASED CLOSE-RANGE PHOTOGRAMMETRY USING A NOVEL 3D OCCUPANCY-BASED RECONSTRUCTION","year":2022,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Silhouette; Artificial intelligence; Computer vision; Computer science; 3D reconstruction; Feature (linguistics); Occupancy grid mapping; Monocular; Object (grammar); Voxel; Probabilistic logic; Visual hull; Pattern recognition (psychology); Mobile robot","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005711559,0.0007558899,0.001247582,0.001180364,0.0003839804,0.001461235,0.00199662,0.0007801827,0.002618042],"category_scores_gemma":[0.002195364,0.0009080859,0.001197216,0.001596562,0.0006233524,0.001550487,0.001942291,0.001202172,0.001359242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004607482,"about_ca_system_score_gemma":0.001056453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003248763,"about_ca_topic_score_gemma":0.003058732,"domain_scores_codex":[0.9988237,0.0001810355,0.00005219154,0.0002684256,0.0005710912,0.0001035711],"domain_scores_gemma":[0.9992573,0.0002053211,0.00009834761,0.0001793825,0.0002070414,0.00005253215],"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":[0.0003989183,0.0001709493,0.003700623,0.0003529279,0.0001716318,0.0003490371,0.000474379,0.3265543,0.0469455,0.01535483,0.003997073,0.6015298],"study_design_scores_gemma":[0.000007775197,0.00003381933,0.0003663802,0.00001042931,0.00001352709,0.0001955137,0.0000259288,0.9890168,0.006760742,0.001509996,0.00203173,0.00002735484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0035114,0.0000688147,0.9953399,0.00002916928,0.0000152855,0.00002383011,0.00003730965,0.000456511,0.0005178206],"genre_scores_gemma":[0.2102915,0.000314772,0.7865416,0.0001038441,0.00004337526,0.0001404152,0.0005135012,0.0002536915,0.001797207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003248763,"threshold_uncertainty_score":0.008758247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569126897896749,"score_gpt":0.2421138357267692,"score_spread":0.2164225667478017,"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."}}