{"id":"W4248121078","doi":"10.5194/isprsarchives-xli-b5-621-2016","title":"THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES","year":2016,"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":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Computer science; Bundle adjustment; Computer vision; Orientation (vector space); Artificial intelligence; Global Positioning System; Cloud computing; Point (geometry); Noise (video); Remote sensing; Image (mathematics); Geography; Mathematics; Telecommunications","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":[0.0005243272,0.0004424814,0.0003295728,0.0006786045,0.0003036498,0.0004471901,0.0007063797,0.0005985601,0.001845207],"category_scores_gemma":[0.003013492,0.0002510718,0.0004639476,0.0007437025,0.0003047129,0.0008298506,0.0008612383,0.0004341063,0.00103073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002738475,"about_ca_system_score_gemma":0.0003908434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002238582,"about_ca_topic_score_gemma":0.002720475,"domain_scores_codex":[0.9991492,0.0001137318,0.00002967528,0.00009408039,0.0005702674,0.00004308517],"domain_scores_gemma":[0.9982902,0.0005087891,0.00009843363,0.0004140023,0.0006516135,0.00003696306],"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":[0.00047466,0.00009106295,0.01485924,0.0004744133,0.00007600471,0.0009829444,0.0005388958,0.06447477,0.23013,0.007966358,0.00589182,0.6740398],"study_design_scores_gemma":[0.00006869456,0.0004416196,0.02301098,0.00007494352,0.00005460203,0.001692213,0.0003543501,0.7580164,0.1851058,0.007212983,0.02386412,0.0001033677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07905693,0.0003739631,0.914887,0.0003593975,0.0001114857,0.0001380033,0.0004150254,0.001477538,0.003180703],"genre_scores_gemma":[0.6178957,0.0003826845,0.3796178,0.00009262434,0.00005689134,0.0001546595,0.0006345597,0.0001150376,0.001050085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002238582,"threshold_uncertainty_score":0.006172836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247170493504036,"score_gpt":0.2467776057460347,"score_spread":0.2243059008109943,"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."}}