一、Pointnet评价结果
1、Performance(classification)
Download alignment ModelNet here and save in data/modelnet40_normal_resampled/.
| Model | Accuracy |
|---|---|
| PointNet (Official) | 89.2 |
| PointNet2 (Official) | 91.9 |
| PointNet (Pytorch without normal) | 90.6 |
| PointNet (Pytorch with normal) | 91.4 |
| PointNet2_SSG (Pytorch without normal) | 92.2 |
| PointNet2_SSG (Pytorch with normal) | 92.4 |
| PointNet2_MSG (Pytorch with normal) | 92.8 |
2、Performance(Part Segmentation)
Download alignment ShapeNet here and save in data/shapenetcore_partanno_segmentation_benchmark_v0_normal/.
| Model | Inctance avg IoU | Class avg IoU |
|---|---|---|
| PointNet (Official) | 83.7 | 80.4 |
| PointNet2 (Official) | 85.1 | 81.9 |
| PointNet (Pytorch) | 84.3 | 81.1 |
| PointNet2_SSG (Pytorch) | 84.9 | 81.8 |
| PointNet2_MSG (Pytorch) | 85.4 | 82.5 |
3、Performance on sub-points of raw dataset (processed by official PointNet Link)
Download 3D indoor parsing dataset (S3DIS) here and save in data/Stanford3dDataset_v1.2_Aligned_Version/.
| Model | Class avg IoU |
|---|---|
| PointNet (Official) | 41.1 |
| PointNet (Pytorch) | 48.9 |
| PointNet2 (Official) | N/A |
| PointNet2_ssg (Pytorch) | 53.2 |
4、Performance on raw dataset
still on testing…
二、ldgcnn评价结果
1、Performance(classification)
2、Performance(Part Segmentation)
3、模型消融
