{"id":"W4403094418","doi":"10.1109/lra.2024.3474551","title":"Di-NeRF: Distributed NeRF for Collaborative Learning With Relative Pose Refinement","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102265,0.0001497275,0.0001346739,0.0001004887,0.0001450454,0.0001866674,0.00003441523,0.00004732871,0.00001002629],"category_scores_gemma":[0.00001804926,0.000130944,0.00002823599,0.0002764866,0.00002704332,0.00024878,0.000006266591,0.0001793383,0.00001014112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007478756,"about_ca_system_score_gemma":0.00001390402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000228399,"about_ca_topic_score_gemma":0.000002968619,"domain_scores_codex":[0.9993214,0.00002972851,0.0001874586,0.0001724121,0.0001219025,0.0001670868],"domain_scores_gemma":[0.9996765,0.0001188971,0.00003922772,0.00006624101,0.00004973743,0.00004935474],"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":[0.000006335784,0.000003852075,0.0001493362,0.00009675073,0.00007749118,0.000005073743,0.0007063159,0.989575,0.003515731,0.002612135,0.001949985,0.00130197],"study_design_scores_gemma":[0.0003115545,0.00006179937,0.001804879,0.0001338532,0.00004409306,0.000003784546,0.0001374474,0.9900076,0.0003062338,0.00003216304,0.006963025,0.0001935844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03780416,0.0001782693,0.9575245,0.003020531,0.0003820936,0.0002733155,0.000007332888,0.0006064173,0.0002033655],"genre_scores_gemma":[0.9916547,0.00002233444,0.007722772,0.0001142222,0.0001239172,0.00003378231,0.0001415339,0.00003927458,0.0001474475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9538506,"threshold_uncertainty_score":0.5339743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01174307422956253,"score_gpt":0.2233177176178642,"score_spread":0.2115746433883016,"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."}}