{"id":"W4385819743","doi":"10.1109/lgrs.2023.3303399","title":"Feature Graph Convolution Network With Attentive Fusion for Large-Scale Point Clouds Semantic Segmentation","year":2023,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Segmentation; Graph; Encoder; Convolution (computer science); Feature extraction; Semantic feature; Theoretical computer science; Artificial neural network","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":[0.0004684115,0.001257733,0.0009780606,0.00117379,0.000520389,0.0007721268,0.001720182,0.001231434,0.001835725],"category_scores_gemma":[0.001163523,0.0004368846,0.001199955,0.001433147,0.0007235907,0.00196635,0.001427206,0.001235921,0.0006691626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271164,"about_ca_system_score_gemma":0.001142324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01552218,"about_ca_topic_score_gemma":0.0177145,"domain_scores_codex":[0.9996075,0.00004303909,0.00001709165,0.000145693,0.0001107122,0.00007605337],"domain_scores_gemma":[0.9996951,0.00009066441,0.00003305767,0.00007197319,0.0000823345,0.00002700071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003939515,0.0002202159,0.002356783,0.0001491388,0.0001913681,0.0003133368,0.0001921498,0.4196594,0.03414558,0.0116385,0.009343258,0.5213963],"study_design_scores_gemma":[0.000005418552,0.00002510196,0.0003625645,0.00000465136,0.00001751901,0.00005131491,0.00001549268,0.9870978,0.006209884,0.005167257,0.001034233,0.000008734943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04811626,0.0005078215,0.943691,0.0002382601,0.00006636769,0.00006116772,0.0003898272,0.004617801,0.002311505],"genre_scores_gemma":[0.6675865,0.0005249952,0.3233066,0.0004675183,0.00009581757,0.0001251238,0.002560965,0.0003649734,0.004967358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01552218,"threshold_uncertainty_score":0.03086364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008343152335332442,"score_gpt":0.2119364990133445,"score_spread":0.2035933466780121,"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."}}