{"id":"W4404371600","doi":"10.1109/cyber63482.2024.10748903","title":"Visual SLAM Fusing Robust Segmentation of Moving Targets and NeRF","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Jiangsu Provincial Key Research and Development Program","keywords":"Computer vision; Artificial intelligence; Computer science; Segmentation; Image segmentation; Robustness (evolution); Biology","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":[0.0005716924,0.001131525,0.00149626,0.0009549396,0.0004980545,0.0008537243,0.001629581,0.0009031998,0.001655872],"category_scores_gemma":[0.002067469,0.0005579843,0.0006473816,0.001315435,0.0006423974,0.001508597,0.002359415,0.001140064,0.00084377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004727278,"about_ca_system_score_gemma":0.001330239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007790909,"about_ca_topic_score_gemma":0.01178299,"domain_scores_codex":[0.9992549,0.0000646219,0.00002882138,0.0002237261,0.000291077,0.0001368009],"domain_scores_gemma":[0.9993446,0.00008849987,0.00007438695,0.0002417338,0.000205323,0.00004534508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003690407,0.0001650118,0.001226104,0.0001524287,0.00008806642,0.0001485399,0.0001797042,0.310138,0.04713314,0.002946577,0.003815471,0.633638],"study_design_scores_gemma":[0.00001346496,0.0000526324,0.0005779471,0.000007858499,0.000009483554,0.000063539,0.00002556299,0.9865952,0.009458801,0.001986241,0.001192169,0.00001721001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02947523,0.0001756588,0.9658267,0.00007885267,0.0000798088,0.00003823065,0.0001311626,0.002621608,0.001572723],"genre_scores_gemma":[0.6772399,0.0001613707,0.3177348,0.0001779405,0.00006482717,0.0000930161,0.00080862,0.0003080453,0.00341146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007790909,"threshold_uncertainty_score":0.01549113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008657196594400623,"score_gpt":0.2204840324954982,"score_spread":0.2118268359010975,"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."}}