{"id":"W4417465952","doi":"10.1016/j.autcon.2025.106731","title":"Indoor scan-to-BIM automation: From mobile perception to 3D building modelling","year":2025,"lang":"en","type":"article","venue":"Automation in Construction","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"China Scholarship Council; National Natural Science Foundation of China; Ontario Ministry of Natural Resources and Forestry","keywords":"Benchmarking; Bridging (networking); Point cloud; Benchmark (surveying); Building information modeling; Identification (biology); Key (lock); Resource (disambiguation)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005008681,0.001316282,0.001313377,0.001307631,0.0003910998,0.001452852,0.001804856,0.0008003917,0.006567702],"category_scores_gemma":[0.001571849,0.0008637317,0.0009335486,0.001710455,0.0007136124,0.001597401,0.003481745,0.0007768887,0.003880792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003649869,"about_ca_system_score_gemma":0.0007201469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004682773,"about_ca_topic_score_gemma":0.006705839,"domain_scores_codex":[0.9990562,0.0001683588,0.00003018023,0.0002329223,0.0003996052,0.0001128285],"domain_scores_gemma":[0.9993086,0.0001346155,0.00004923299,0.0003045735,0.0001532139,0.00004976822],"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":[0.0003999502,0.0002929598,0.003919413,0.0003065029,0.0001232764,0.0003393118,0.0004990586,0.1031764,0.03500574,0.008917004,0.00774191,0.8392784],"study_design_scores_gemma":[0.00003739178,0.0001471075,0.006352211,0.00007166275,0.00005059976,0.0004462984,0.0003216114,0.9261248,0.02878153,0.01884768,0.0187587,0.00006037314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01893542,0.0003851806,0.9680462,0.0001097795,0.00005754198,0.00005530065,0.0004585825,0.007893302,0.004058608],"genre_scores_gemma":[0.5326375,0.000802607,0.4598364,0.0001670694,0.00007164037,0.0001215032,0.00190883,0.001098484,0.003355993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006567702,"threshold_uncertainty_score":0.02197123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348945244071359,"score_gpt":0.2464343827018644,"score_spread":0.2329449302611508,"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."}}