基于竞争注意力融合的深度三维点云分类网络
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陈涵娟,达飞鹏,盖绍彦
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Deep 3D point cloud classification network based on competitive attention fusion
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Han-juan CHEN,Fei-peng DA,Shao-yan GAI
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表 4 在S3DIS数据集上6折交叉验证的语义分割性能 |
Tab.4 Semantic segmentation performance on S3DIS dataset with 6-fold cross validation % |
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方法 | OA | mAcc | mIoU | IoU | ceiling | floor | wall | beam | column | window | door | table | chair | sofa | bookcase | board | clutter | PointNet[4] | 78.5 | 66.2 | 47.6 | 88.0 | 88.7 | 69.3 | 42.4 | 23.1 | 47.5 | 51.6 | 42.0 | 54.1 | 38.2 | 9.6 | 29.4 | 35.2 | A-CNN[13] | 87.3 | − | 62.9 | 92.4 | 96.4 | 79.2 | 59.5 | 34.2 | 56.3 | 65.0 | 66.5 | 78.0 | 28.5 | 56.9 | 48.0 | 56.8 | PointCNN[10] | 88.1 | 75.6 | 65.4 | 94.8 | 97.3 | 75.8 | 63.3 | 51.7 | 58.4 | 57.2 | 71.6 | 69.1 | 39.1 | 61.2 | 52.2 | 58.6 | PointWeb[14] | 87.3 | 76.2 | 66.7 | 93.5 | 94.2 | 80.8 | 52.4 | 41.3 | 64.9 | 68.1 | 71.4 | 67.1 | 50.3 | 62.7 | 62.2 | 58.5 | PointASNL[21] | 88.8 | 79.0 | 68.7 | 95.3 | 97.9 | 81.9 | 47.0 | 48.0 | 67.3 | 70.5 | 71.3 | 77.8 | 50.7 | 60.4 | 63.0 | 62.8 | 本研究 | 88.2 | 78.7 | 68.3 | 95.1 | 97.3 | 81.2 | 47.4 | 45.8 | 67.0 | 69.1 | 72.1 | 77.5 | 50.6 | 60.8 | 62.4 | 61.6 |
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