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Table 7 Parameter studies on varying the number of initially annotated points, tested on the Oakland dataset

From: Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds

Method

Overall accuracy (%)

Average F-score (%)

Per-class F-scores(%)

   

Foliage

Wire

Pole

Ground

Façade

5 labels/class

Seg-aided [17]

95.1

58.9

95.1

4.1

14.3

93.9

87.1

Ours with kdist

80.6

45.2

0.0

33.9

32.7

99.4

60.2

15 labels/class

Seg-aided [17]

96.6

68.4

93.7

46.5

8.7

99.5

93.9

Ours with kdist

96.6

74.2

92.0

40.2

46.2

99.3

93.3

30 labels/class

Seg-aided [17]

96.2

67.8

94.8

33.6

19.3

98.9

92.2

Ours with kdist

95.9

70.2

90.5

34.1

35.5

99.3

91.4

100 labels/class

Seg-aided [17]

96.2

68.0

95.5

32.2

20.7

98.8

92.4

Ours with kdist

96.2

72.6

91.4

38.3

41.4

99.4

92.4

  1. Experiments were performed using our method and segmentation-aided classification