{"id":"W4383109184","doi":"10.1109/icra48891.2023.10160794","title":"nerf2nerf: Pairwise Registration of Neural Radiance Fields","year":2023,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Vector Institute; University of Toronto","funders":"","keywords":"Radiance; Artificial intelligence; Computer science; Computer vision; Invariant (physics); Image registration; Pairwise comparison; Field (mathematics); Transformation (genetics); Artificial neural network; Object (grammar); Point (geometry); Pattern recognition (psychology); Image (mathematics); Mathematics; Remote sensing; Geography","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":[0.00135221,0.001961245,0.001255523,0.001982305,0.0006906172,0.00202184,0.002593598,0.001708513,0.006912605],"category_scores_gemma":[0.004434275,0.0008836725,0.00196536,0.001824432,0.0009564483,0.00281474,0.003280321,0.002452502,0.004335277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008736049,"about_ca_system_score_gemma":0.001363586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00421717,"about_ca_topic_score_gemma":0.007792413,"domain_scores_codex":[0.998701,0.0002052722,0.00004882863,0.0004198088,0.000482593,0.0001424528],"domain_scores_gemma":[0.9993078,0.0001147373,0.00008814519,0.0003062533,0.0001425453,0.00004045288],"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":[0.0002242205,0.0002716063,0.002340303,0.0003066967,0.0002595019,0.000229457,0.0003156674,0.1962376,0.04973453,0.02742033,0.0263122,0.6963478],"study_design_scores_gemma":[0.00004312553,0.0001390432,0.001716921,0.00005941166,0.00004698608,0.0006028873,0.0001230346,0.8770266,0.05483158,0.03345047,0.03184371,0.0001162505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006557785,0.0001218553,0.9831911,0.0001129311,0.00006596483,0.00008030878,0.0004189189,0.007026334,0.002424915],"genre_scores_gemma":[0.1333796,0.000234391,0.8539091,0.0002737418,0.00006636805,0.0003119894,0.003113251,0.003717154,0.004994344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006912605,"threshold_uncertainty_score":0.02312499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01785000174538988,"score_gpt":0.2153810255611686,"score_spread":0.1975310238157788,"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."}}