{"id":"W4365393262","doi":"10.1007/978-3-031-30111-7_40","title":"HPointLoc: Point-Based Indoor Place Recognition Using Synthetic RGB-D Images","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; RGB color model; Artificial intelligence; Computer vision; Modular design; Task (project management); Robot; Point (geometry); Pattern recognition (psychology)","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":[0.0001662864,0.0009194574,0.0006969044,0.0007552861,0.0002128522,0.0007760618,0.001389482,0.0005374595,0.01452038],"category_scores_gemma":[0.0003504374,0.0003941388,0.0005302459,0.001041883,0.0002514662,0.0007931692,0.001042615,0.0004395108,0.01085197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002371143,"about_ca_system_score_gemma":0.0002488391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003262225,"about_ca_topic_score_gemma":0.00484912,"domain_scores_codex":[0.9997582,0.00001467484,0.000005681386,0.00005490523,0.0001443223,0.000022144],"domain_scores_gemma":[0.9998673,0.00002334148,0.000009321115,0.00004160834,0.00004616463,0.00001231905],"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":[0.0002922814,0.00009619491,0.0009833323,0.0002706302,0.0000695457,0.0001595753,0.0001068275,0.02213904,0.07801581,0.00281878,0.04678297,0.848265],"study_design_scores_gemma":[0.00006437249,0.0001820688,0.005609327,0.00007085547,0.0000456431,0.0009294202,0.0001519663,0.7542922,0.1541949,0.006338144,0.07800313,0.0001178501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008837267,0.0003454404,0.9410579,0.00004806058,0.0001390252,0.0000852461,0.002646484,0.04043497,0.006405509],"genre_scores_gemma":[0.1458203,0.0007093892,0.8163608,0.00016478,0.00007744302,0.0002444733,0.01152554,0.002424225,0.0226731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01452038,"threshold_uncertainty_score":0.04857552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02260621180218195,"score_gpt":0.2241859690911616,"score_spread":0.2015797572889797,"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."}}