3D Point Cloud Classification On Intra
Metriken
F1 score (5-fold)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | F1 score (5-fold) | Paper Title | Repository |
|---|---|---|---|
| PointConv | 0.883 | PointConv: Deep Convolutional Networks on 3D Point Clouds | - |
| AdaptConv | 0.858 | Adaptive Graph Convolution for Point Cloud Analysis | - |
| SO-Net | 0.868 | SO-Net: Self-Organizing Network for Point Cloud Analysis | - |
| PointCNN | 0.875 | PointCNN: Convolution On X-Transformed Points | |
| SpiderCNN | 0.872 | SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters | - |
| 3DMedPT | 0.936 | 3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis | - |
| PointNet++ | 0.903 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | - |
| GS-Net | 0.872 | Geometry Sharing Network for 3D Point Cloud Classification and Segmentation | - |
| DGCNN | 0.738 | Dynamic Graph CNN for Learning on Point Clouds | - |
| PCT | 0.914 | PCT: Point cloud transformer | - |
| PointNet | 0.684 | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation | - |
| PAConv | 0.906 | PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds | - |
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