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Table 3 Semantic segmentation results on the S3DIS dataset (no RGB)

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

Method OA (%) AF (%) Per-class I-scores(%)
    (i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi)
Pointwise [1] 18.8 16.8 15.3 0.1 18.0 27.5 7.5 8.4 49.9 18.3 18.4 21.0 0.9
CRF-reg [22] 35.6 25.6 1 2 48.9 53.3 2.6 1 94.9 11.3 39.2 31.2 0.2
Seg-aided [17] 42.1 34.0 52.6 2 72.4 47.1 2 2 94.1 26.6 49.5 31.0 1.1
Supervised baseline 34.8 30.5 27.5 47.9 23.5 26.7 19.2 34.2 60.2 25.9 29.1 24.7 16.1
Ours no kdist 31.7 28.5 32.2 46.2 31.8 31.8 22.9 36.5 28.4 32.5 29.2 4.2 18.0
Ours with kdist 49.8 44.3 31.7 73.8 35.3 38.1 31.1 59.2 74.1 40.9 46.7 20.9 29.0
*1 no instances of class are predicted correctly; precision=0, recall=0 - F-score undefined, taken to be 0 for the average
*2 no instances of class are predicted at all; precision undefined, recall=0 - F-score undefined, taken to be 0 for average
  1. OA overall accuracy, AF average F-score. Classes are as follows: (i) door, (ii) floor, (iii) table, (iv) window, (v) beam, (vi) book-case, (vii) ceiling, (viii) clutter, (ix) chair, (x) board, (xi) wall