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Table 11 Segmentation results on 8 real rosebush models for all architectures

From: Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods

  PointNet PointNet++ DGCNN PointCNN ShellNet RIConv
Flower III 14.94 72.72 6.96 49.76 47.26 53.42
S+III 7.54 79.17 51.42 60.55 55.80 52.93
Gain − 7.40 + 6.45 + 44.46 + 10.79 + 8.54 − 0.49
Leaf III 80.94 95.07 83.57 92.16 89.86 88.22
S+III 79.48 96.36 91.71 93.40 92.18 90.07
Gain − 1.46 + 1.29 + 8.14 + 1.24 + 2.32 + 1.85
Stem III 3.03 76.79 24.34 67.67 52.18 34.53
S+III 7.06 83.05 60.02 68.81 66.73 51.27
Gain + 4.03 + 6.26 + 35.68 + 1.14 + 14.55 + 16.74
MIoU III 32.97 81.53 38.29 69.86 63.10 58.72
S+III 31.36 86.19 67.72 74.26 71.57 64.76
Gain − 1.61 + 4.66 + 29,43 + 4.40 + 8.47 + 6.04
  1. Bold stands for "gain in IoU obtained by incorporating synthetic models" as expressed in the caption of the Figure
  2. The first row for each class corresponds to IoU results of networks trained with three real rosebush models (III). The second row for each class gives the IoU results for the case, where the networks were trained with synthetic models and updated with three real rosebush models (S+III). The third row for each class gives the gain in IoU obtained by incorporating synthetic models. The last three rows of the table corresponds to MIoU over all classes