{"id":"W4406333137","doi":"10.3390/rs17020256","title":"Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Grid Zhejiang Electric Power Company","keywords":"Computer science; Offset (computer science); Point cloud; Encoder; Embedding; Encoding (memory); Segmentation; Position (finance); Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer vision; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002996909,0.001739248,0.0008857581,0.001316671,0.0003668297,0.0009597976,0.001859026,0.001062685,0.00377523],"category_scores_gemma":[0.001367498,0.0004020694,0.0007838412,0.001736542,0.0005184974,0.003280123,0.001576739,0.001448678,0.001762125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009401893,"about_ca_system_score_gemma":0.0008207592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008650735,"about_ca_topic_score_gemma":0.01273496,"domain_scores_codex":[0.9997266,0.00002500415,0.00001424597,0.0001056576,0.00008715387,0.00004126224],"domain_scores_gemma":[0.9997291,0.0000511087,0.00002710873,0.0000916405,0.00007845226,0.00002265592],"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":[0.000538015,0.0002429456,0.003804871,0.0002350967,0.0001251759,0.0002037728,0.0001656691,0.2621132,0.0217263,0.01030554,0.01794737,0.682592],"study_design_scores_gemma":[0.0000299064,0.0000894323,0.0009532938,0.00002414722,0.00003407844,0.0001013775,0.00006121438,0.9695999,0.01255817,0.01147927,0.00504633,0.00002295042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1185362,0.002066606,0.8401169,0.0006384119,0.0003385215,0.000159695,0.004100701,0.02863006,0.005412871],"genre_scores_gemma":[0.707887,0.0008904762,0.2723674,0.0003651915,0.0001373093,0.0001639451,0.01168053,0.0007690321,0.005739146],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008650735,"threshold_uncertainty_score":0.01720071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008446652053378603,"score_gpt":0.2390844033446577,"score_spread":0.2306377512912791,"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."}}