{"id":"W2969559750","doi":"10.1364/oe.27.025265","title":"Real-time motion-induced-error compensation in 3D surface-shape measurement","year":2019,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Waterloo","keywords":"Structured-light 3D scanner; Optics; Profilometer; Phase (matter); Motion compensation; Observational error; Projection (relational algebra); Motion (physics); Computer vision; Compensation (psychology); Motion estimation; Computer science; Surface (topology); Artificial intelligence; Physics; Mathematics; Algorithm; Geometry; Scanner","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":[0.0006467266,0.0003698638,0.0002841707,0.0004967163,0.0002022299,0.0003700238,0.0006315853,0.0005177495,0.000546799],"category_scores_gemma":[0.001700777,0.0003358526,0.0001641523,0.0006744026,0.0004038821,0.0007658143,0.0005911449,0.0004585712,0.0002328764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003702231,"about_ca_system_score_gemma":0.000316142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007531372,"about_ca_topic_score_gemma":0.00112309,"domain_scores_codex":[0.9993667,0.000132299,0.00001966438,0.00009553062,0.0003544715,0.00003139082],"domain_scores_gemma":[0.9994732,0.0001940767,0.00008796545,0.0001059693,0.0001177533,0.0000211829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003298326,0.00004905763,0.00144043,0.0001283093,0.00002417975,0.00009737165,0.0002006529,0.0226831,0.7458024,0.002949167,0.0003755202,0.2259199],"study_design_scores_gemma":[0.00002873278,0.0001834556,0.004758146,0.00001426334,0.00001372524,0.000489282,0.00004350332,0.4334365,0.5561063,0.00173495,0.003120276,0.00007094617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1194718,0.0003716919,0.8786746,0.00007719574,0.00004246424,0.00004043851,0.00005665871,0.0006142833,0.0006509357],"genre_scores_gemma":[0.5135593,0.000205265,0.4853831,0.00003740914,0.00002207548,0.00004984436,0.00007195284,0.00006736228,0.0006037363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007531372,"threshold_uncertainty_score":0.003420234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06257618037465454,"score_gpt":0.2753245515700309,"score_spread":0.2127483711953764,"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."}}