Table 5 Comparison of studies
From: Implementing PointNet for point cloud segmentation in the heritage context
Year | Study | Method | Dataset | Performance accuracy (%) |
|---|---|---|---|---|
2016 | Generative and discriminative voxel modeling with convolutional neural networks | MVCNN [6] | ModelNet40 | 90.1 |
2016 | Fast semantic segmentation of 3D point clouds with strongly varying density | TMLC-MSR [27] | TerraMobilita | 90.28 |
2017 | A scalable active framework for region annotation in 3D shape collections | Yi [15] | ModelNet40 | 81.4 |
2017 | OctNet: learning deep 3D representations at high resolutions | Oct-Net [13] | ModelNet10 | 81.5 |
2017 | Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models | Kd-Net [14] | ModelNet40 | 82.3 |
2017 | PointNet: deep learning on point sets for 3d classification and segmentation | PointNet [16] | ModelNet40 | 83.7 |
2017 | PointNet++: deep hierarchical feature learning on point sets in a metric space | PointNet++ [17] | ModelNet40 | 90.7 |
2017 | SEGCloud: semantic segmentation of 3D point clouds | SegCloud [24] | S3DIS | 88.1 |
2017 | Dynamic edge-conditioned filters in convolutional neural networks on graphs | ECC [19] | ModelNet40 | 82.4 |
2017 | Unstructured point cloud semantic labeling using deep segmentation networks | SnapNet [25] | S3DIS | 88.6 |
2017 | Deep projective 3D semantic segmentation | DeePr3SS[26] | S3DIS | 88.9 |
2018 | Mining point cloud local structures by kernel correlation and graph pooling | KCNet [18] | ModelNet40 | 91.0 |
2018 | Large-scale point cloud semantic segmentation with superpoint graphs | SPG [20] | S3DIS | 85.5 |
2019 | Graph attention convolution for point cloud semantic segmentation | GACNet [21] | S3DIS | 87.79 |