{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006059673,0.00004151172,0.00006811998,0.00005073766,0.00001286942,0.000006692528,0.00004669767,0.00003088429,0.00004863513],"category_scores_gemma":[0.00001169479,0.00003843544,0.00004116366,0.0002107951,0.000006536717,0.00003921703,0.000004223231,0.00004154711,0.00003675142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004285664,"about_ca_system_score_gemma":0.000003175217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004765035,"about_ca_topic_score_gemma":0.00004149012,"domain_scores_codex":[0.9996876,0.000004334489,0.0001100671,0.00005517125,0.00006288118,0.0000799045],"domain_scores_gemma":[0.9998325,0.00001549291,0.000009426955,0.0001084889,0.00001247193,0.00002169246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.79749e-7,0.000001946595,0.0002377881,0.00002220416,0.00001138342,0.000001946106,0.0000647471,0.9786345,0.0008008063,0.0001086899,0.01172919,0.008386174],"study_design_scores_gemma":[0.00004234216,0.000005664956,0.0003229156,0.000004821211,0.00000602441,2.918745e-7,0.00002539255,0.9977919,0.001456759,0.0001271279,0.0001712745,0.00004544225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9001476,0.0001590753,0.07794316,0.0006703384,0.0001768211,0.00003812485,0.000003355409,0.000809183,0.0200523],"genre_scores_gemma":[0.9969055,0.00005290901,0.0001915789,0.00002244065,0.00004262953,0.000002032221,0.000008249314,0.000006516721,0.002768126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09675787,"threshold_uncertainty_score":0.1567352,"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."}}