基于边界点估计与稀疏卷积神经网络的三维点云语义分割
|
|
杨军,张琛
|
Semantic segmentation of 3D point cloud based on boundary point estimation and sparse convolution neural network
|
|
Jun YANG,Chen ZHANG
|
|
| 表 1 不同方法在S3DIS数据集上的分割精度对比(以Area 5作为测试) |
| Tab.1 Comparison of segmentation accuracy of different methods on S3DIS dataset (Area 5 as a test) |
|
| 方法 | OA/% | mIoU/% | IoU/% | | ceiling | floor | wall | beam | column | window | door | table | chair | sofa | bookcase | board | clutter | | PointNet [5] | 79.3 | 41.1 | 88.8 | 97.3 | 69.8 | 0.1 | 3.9 | 46.3 | 10.8 | 59.0 | 52.6 | 5.9 | 40.3 | 26.4 | 33.2 | | TangentConv [28] | 82.5 | 52.6 | 90.5 | 97.7 | 74.0 | 0.0 | 20.7 | 39.0 | 31.3 | 77.5 | 69.4 | 57.3 | 38.5 | 48.8 | 39.8 | | PointCNN [29] | 85.9 | 57.3 | 92.3 | 98.2 | 79.4 | 0.0 | 17.6 | 22.8 | 62.1 | 74.4 | 80.6 | 31.7 | 66.7 | 62.1 | 56.7 | | SPG [30] | 86.4 | 58.0 | 89.4 | 96.9 | 78.1 | 0.0 | 42.8 | 48.9 | 61.6 | 84.7 | 75.4 | 69.8 | 52.6 | 2.1 | 52.2 | | PointWeb [31] | 87.0 | 60.3 | 92.0 | 98.5 | 79.4 | 0.0 | 21.1 | 59.7 | 34.8 | 76.3 | 88.3 | 46.9 | 69.3 | 64.9 | 52.5 | | HPEIN [32] | 87.2 | 61.9 | 91.5 | 98.2 | 81.4 | 0.0 | 23.3 | 65.3 | 40.0 | 75.5 | 87.7 | 58.5 | 67.8 | 65.6 | 49.4 | | RandLA-Net [18] | 87.2 | 62.4 | 91.1 | 95.6 | 80.2 | 0.0 | 24.7 | 62.3 | 47.7 | 76.2 | 83.7 | 60.2 | 71.1 | 65.7 | 53.8 | | GACNet [33] | 87.8 | 62.8 | 92.3 | 98.3 | 81.9 | 0.0 | 20.3 | 59.1 | 40.8 | 78.5 | 85.8 | 61.7 | 70.7 | 74.7 | 52.8 | | PPCNN++ [34] | — | 64.0 | 94.0 | 98.5 | 83.7 | 0.0 | 18.6 | 66.1 | 61.7 | 79.4 | 88.0 | 49.5 | 70.1 | 66.4 | 56.1 | | BAAF-Net [35] | 88.9 | 65.4 | 92.9 | 97.9 | 82.3 | 0.0 | 23.1 | 65.5 | 64.9 | 78.5 | 87.5 | 61.4 | 70.7 | 68.7 | 57.2 | | KPConv [36] | — | 67.1 | 92.8 | 97.3 | 82.4 | 0.0 | 23.9 | 58.0 | 69.0 | 81.5 | 91.0 | 75.4 | 75.3 | 66.7 | 58.9 | | AGConv [37] | 90.0 | 67.9 | 93.9 | 98.4 | 82.2 | 0.0 | 23.9 | 59.1 | 71.3 | 91.5 | 81.2 | 75.5 | 74.9 | 72.1 | 58.6 | | 本研究方法 | 90.8 | 69.5 | 94.4 | 99.2 | 87.2 | 0.0 | 27.2 | 62.2 | 72.8 | 91.8 | 85.8 | 79.0 | 66.7 | 74.4 | 62.9 |
|
|
|