{"id":"W4403754887","doi":"10.48550/arxiv.2409.13055","title":"MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; HORIZON EUROPE Framework Programme","keywords":"Monocular; Computer science; Artificial intelligence; Computer vision; Gaussian; Computer graphics (images); Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001522435,0.0004266554,0.0003873393,0.0008741083,0.00009508093,0.0001209564,0.0003260536,0.0003305193,0.00008905464],"category_scores_gemma":[0.00001383261,0.0004518797,0.0001591593,0.001564128,0.00005657366,0.00004231147,0.0002947392,0.0006412338,0.0003196232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004054961,"about_ca_system_score_gemma":0.00007403632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001316843,"about_ca_topic_score_gemma":0.00001002039,"domain_scores_codex":[0.998435,0.00004343974,0.0002293149,0.0007411896,0.0001383777,0.000412654],"domain_scores_gemma":[0.9989448,0.00005913258,0.00008470372,0.0006461444,0.00009069805,0.0001745888],"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":[0.00001618258,0.00002935142,0.0002598836,0.000387596,0.0001862849,0.0006158922,0.00009587619,0.9960721,0.0003031778,0.001776583,0.0001049437,0.0001521579],"study_design_scores_gemma":[0.0002834407,0.00003640785,0.0002018035,0.0003222762,0.0002633273,0.000006106818,0.00004440638,0.9972869,0.0004928042,0.0003860013,0.0001405187,0.0005359803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8144953,0.0001605475,0.1629624,0.00001205888,0.0004928113,0.0004482393,0.00003205481,0.000922081,0.0204745],"genre_scores_gemma":[0.9969919,0.0002266969,0.00128971,0.000007943085,0.00009048534,0.00000111555,0.00007846701,0.0001148602,0.001198801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1824966,"threshold_uncertainty_score":0.9997933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02275519847503745,"score_gpt":0.157737873020711,"score_spread":0.1349826745456735,"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."}}