{"id":"W2805721662","doi":"10.1109/3dv.2017.00080","title":"Motion Compensation for Phase-Shift Structured-Light Systems Based on a Total-Variation Framework","year":2017,"lang":"en","type":"article","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Residual; Projector; Offset (computer science); Computer vision; Motion compensation; Artificial intelligence; Profilometer; Computer science; Phase (matter); Structured light; Optics; Algorithm; Materials science; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003775724,0.0004263263,0.0002804245,0.0003390431,0.0002194963,0.0003264678,0.0006371803,0.000369163,0.0005989234],"category_scores_gemma":[0.0007542515,0.0002074926,0.0002819206,0.0003901967,0.0003865279,0.0005744469,0.0004247688,0.0004219355,0.0001702676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004042889,"about_ca_system_score_gemma":0.0004195985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198828,"about_ca_topic_score_gemma":0.001973443,"domain_scores_codex":[0.9996572,0.00009384229,0.00001102148,0.00004628139,0.000171221,0.00002046511],"domain_scores_gemma":[0.9997833,0.00008116406,0.00003787351,0.00003322815,0.00005209794,0.00001229636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000245293,0.00007849933,0.0008253386,0.0002248983,0.00007674067,0.0001178105,0.0002386375,0.2232824,0.4028956,0.04836645,0.001024505,0.3226238],"study_design_scores_gemma":[0.00001675449,0.0001208334,0.0004812054,0.000008758868,0.00001253204,0.00009036357,0.0000144618,0.95285,0.03958349,0.003984263,0.002812054,0.00002521995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0259048,0.0002718982,0.9729295,0.00004766192,0.0000160329,0.00001969339,0.00001454734,0.0001657703,0.0006302012],"genre_scores_gemma":[0.4196377,0.0004556175,0.5779985,0.00005013315,0.0000314842,0.0000663286,0.00007668787,0.00009473375,0.001588846],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001198828,"threshold_uncertainty_score":0.002933323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0526468795476842,"score_gpt":0.3211242697719629,"score_spread":0.2684773902242787,"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."}}