{"id":"W4361855244","doi":"10.1109/icpeca56706.2023.10075875","title":"End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volume","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Metric (unit); Convolutional neural network; Structure from motion; Computer vision; Matching (statistics); Volume (thermodynamics); Feature (linguistics); Range (aeronautics); Deep learning; 3D reconstruction; Virtual reality; Iterative reconstruction; Solid modeling; Motion (physics); Pattern recognition (psychology); Mathematics","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":[0.0006585235,0.002004902,0.002013044,0.001268086,0.0004196919,0.001586981,0.002462379,0.001916818,0.006689783],"category_scores_gemma":[0.001739433,0.0009989362,0.001567099,0.00128459,0.000577756,0.001480503,0.003126085,0.002397643,0.0041508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005659108,"about_ca_system_score_gemma":0.001376372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004244182,"about_ca_topic_score_gemma":0.009964284,"domain_scores_codex":[0.9993023,0.00006800826,0.00002607989,0.0001556049,0.0003725864,0.00007540107],"domain_scores_gemma":[0.9994588,0.0001205025,0.00006448622,0.0002000423,0.00009430522,0.00006180666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007024307,0.0003275616,0.001631862,0.0004249998,0.0003760576,0.0005977227,0.0002355594,0.2055718,0.05776126,0.006780919,0.02415125,0.7014385],"study_design_scores_gemma":[0.00004106995,0.0001334534,0.0004764332,0.00002032969,0.00002217765,0.0005940948,0.00005155081,0.9683421,0.01940564,0.005894077,0.004981708,0.00003737273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009316975,0.0002661253,0.9811957,0.0001540284,0.00006069388,0.00009883141,0.0005138357,0.00747388,0.0009199849],"genre_scores_gemma":[0.1536703,0.0004475143,0.8365642,0.0003080194,0.00007257846,0.000176077,0.003673143,0.001163446,0.003924799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006689783,"threshold_uncertainty_score":0.02237958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925208741275214,"score_gpt":0.2722407113541249,"score_spread":0.2529886239413727,"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."}}