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3D Part Segmentation On Intra
Metrics
DSC (A)
DSC (V)
IoU (A)
IoU (V)
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||||
|---|---|---|---|---|---|---|
| 3DMedPT | 89.71 | 97.29 | 82.39 | 94.82 | 3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis | |
| SO-Net | 88.76 | 97.09 | 81.40 | 94.46 | SO-Net: Self-Organizing Network for Point Cloud Analysis | |
| PointConv | 86.52 | 97.18 | 79.53 | 94.65 | PointConv: Deep Convolutional Networks on 3D Point Clouds | |
| PointNet++ | 84.64 | 96.48 | 76.38 | 93.42 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | |
| PointCNN | 81.74 | 96.62 | 74.11 | 93.59 | PointCNN: Convolution On $\mathcal{X}$-Transformed Points | |
| SpiderCNN | 75.82 | 94.53 | 67.25 | 90.16 | SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters | |
| PointNet | 49.59 | 85.00 | 37.75 | 75.23 | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation |
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