{"id":"W3121838925","doi":"10.15353/jcvis.v6i1.3562","title":"Constraints for Time-Multiplexed Structured Light with a Hand-held Camera","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reprojection error; Homography; Computer vision; Pinhole camera model; Artificial intelligence; Camera matrix; Projector; Computer science; Structured light; Camera auto-calibration; Camera resectioning; Pinhole camera; Pixel; Frame (networking); Point (geometry); Computer graphics (images); Mathematics; Image (mathematics); Projective test; Projective space; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000908698,0.0007944002,0.0005552383,0.0003893909,0.0004155011,0.001024422,0.0009330866,0.0006418087,0.004079779],"category_scores_gemma":[0.005073967,0.0004495483,0.0002965152,0.0003682704,0.0006562662,0.001488899,0.00118648,0.0008226392,0.0006765082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006303209,"about_ca_system_score_gemma":0.001200244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003667367,"about_ca_topic_score_gemma":0.005160994,"domain_scores_codex":[0.9985352,0.0002553774,0.0000651017,0.0002880242,0.0007479284,0.0001084698],"domain_scores_gemma":[0.9975646,0.001229671,0.0004794286,0.000264955,0.0003610323,0.0001003057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004845406,0.0001884749,0.001855818,0.0005840844,0.00005814335,0.0006985801,0.000385679,0.51741,0.329168,0.03348284,0.002574929,0.1131088],"study_design_scores_gemma":[0.00005970596,0.0004140417,0.002842968,0.00005947194,0.00001730324,0.0004223948,0.0001415133,0.8823415,0.09781058,0.01051284,0.005318267,0.00005933393],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0882551,0.0003565857,0.9054287,0.0001576608,0.00003386315,0.0001193709,0.0004103402,0.0004192576,0.004818985],"genre_scores_gemma":[0.7605115,0.0002844938,0.2346707,0.0001024973,0.00002792231,0.0002939476,0.0004120425,0.0001196313,0.003577265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004079779,"threshold_uncertainty_score":0.01364827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247303317234919,"score_gpt":0.2696571182916737,"score_spread":0.2571840851193245,"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."}}