Fig 1.
Overview of our proposed network architecture.
This architecture added features on centroid and neighbour relationship. The input point clouds on the left are input points, and the output is the classification result.
Fig 2.
Overview of the ISPRS Vaihingen 3D labeling benchmark dataset.
The legend at the bottom indicates the classification labels rendered in colours.
Table 1.
The percent of different categories in ISPRS vaihingen benchmark dataset.
Table 2.
The comparison on different loss function module.
Table 3.
The comparison of adaptive elevation.
Table 4.
The quantitative results using different features on local region.
Fig 3.
Prediction map (a) and error map (b) of our proposed method on the ISPRS benchmark dataset.
Table 5.
Classification confusion matrix of our proposed method.
The evaluation metrics about precision, recall and F1 score of each class are reported. The numbers in the confusion matrix are normalised along each row.
Table 6.
Performance comparison between our proposed method and other state-of-art supervised models on the ISPRS Vaihingen test dataset.
The first nine columns in the table are the per-category F1 scores, and the last two columns are the OA and AvgF1.
Table 7.
Quantitative comparisons between our proposed method and other models on the GML(B) benchmark dataset.
The first four columns are the F1 scores for different classes, and the last two columns are the AvgF1 and compute time.
Fig 4.
Prediction map (a) and error map (b) of our proposed method on the GML(B) benchmark dataset.