{"id":"W4399369002","doi":"10.21428/d82e957c.ad32c280","title":"Trini: An Efficient Representation of Dynamic Scenes for Sparse-View Camera Settings","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Representation (politics); Computer vision; Computer science; Artificial intelligence; Sparse approximation; Computer graphics (images); Pattern recognition (psychology)","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":[0.0003689906,0.0009518611,0.0008620934,0.001093999,0.0003507511,0.001357061,0.001647469,0.0005986069,0.00382854],"category_scores_gemma":[0.001139824,0.000668236,0.0007627473,0.001187998,0.000445033,0.00131591,0.001863233,0.001760705,0.001419327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005306742,"about_ca_system_score_gemma":0.001002543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765897,"about_ca_topic_score_gemma":0.005764604,"domain_scores_codex":[0.9997246,0.00003451401,0.000009892146,0.00003463215,0.0001622714,0.00003403065],"domain_scores_gemma":[0.9996947,0.00007380007,0.00003646901,0.00008287506,0.00007066371,0.00004145806],"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":[0.0002820587,0.0001090373,0.001076458,0.0004076742,0.0001073234,0.0004867839,0.0003883403,0.1831993,0.09309958,0.03085157,0.02896992,0.6610219],"study_design_scores_gemma":[0.00001086521,0.00003556651,0.0002990325,0.00002041428,0.00001400325,0.0004327789,0.00004899135,0.9663191,0.01474958,0.00422682,0.01380855,0.00003430787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002594214,0.00007962932,0.9946617,0.00005202395,0.00002441597,0.00003043654,0.0001744038,0.001533205,0.0008499722],"genre_scores_gemma":[0.05388331,0.0003574281,0.9414368,0.00006217883,0.00003430512,0.000111517,0.001126949,0.0007582352,0.002229114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00382854,"threshold_uncertainty_score":0.01280779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256881524925827,"score_gpt":0.3591646541341241,"score_spread":0.3334765016415414,"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."}}