{"id":"W4413274159","doi":"10.1016/j.cag.2025.104293","title":"LiDAR-3DGS: LiDAR reinforcement for multimodal initialization of 3D Gaussian Splats","year":2025,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Lidar; Initialization; Computer science; Gaussian; Remote sensing; Ranging; Artificial intelligence; Computer vision; Geology; Physics; Telecommunications","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":[0.0006384158,0.0008234614,0.0005946777,0.00054283,0.0003701522,0.0005874473,0.001583522,0.001062801,0.007671848],"category_scores_gemma":[0.002349888,0.0005390942,0.0004790451,0.0003524384,0.000572908,0.0008192901,0.002303499,0.00110646,0.002037495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006061898,"about_ca_system_score_gemma":0.0008605939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004035271,"about_ca_topic_score_gemma":0.007594559,"domain_scores_codex":[0.9996068,0.0000760639,0.00001906248,0.00008635558,0.0001613952,0.00005041113],"domain_scores_gemma":[0.9993852,0.0001389103,0.00006393978,0.0001602872,0.0001749415,0.00007668216],"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":[0.0006146762,0.0002316236,0.002634808,0.0001700947,0.00005540279,0.0002212969,0.0003228544,0.3860835,0.04799364,0.008296581,0.01032192,0.5430537],"study_design_scores_gemma":[0.0000199578,0.00003133516,0.0001410367,0.000006963397,0.000002730221,0.00002732082,0.00001218163,0.9907794,0.006821869,0.0007378813,0.001412574,0.000006754939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01266585,0.00003721967,0.9810597,0.00005701421,0.00003260148,0.00004883553,0.00006376587,0.005241575,0.0007934417],"genre_scores_gemma":[0.3830257,0.0000393884,0.6131253,0.00009479854,0.00002633535,0.0001324341,0.0002885811,0.001017187,0.002250307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007671848,"threshold_uncertainty_score":0.02566493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185873635781502,"score_gpt":0.2388003415987981,"score_spread":0.2269416052409831,"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."}}