3D Semantic Segmentation On Dales
Métriques
Model size
Overall Accuracy
mIoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Model size | Overall Accuracy | mIoU | Paper Title | Repository |
|---|---|---|---|---|---|
| Superpoint Transformer | 212K | 97.5 | 79.6 | Efficient 3D Semantic Segmentation with Superpoint Transformer | |
| PointCNN | N/A | 97.2 | 58.4 | PointCNN: Convolution On X-Transformed Points | |
| ConvPoint | 4.7M | 97.2 | 67.4 | ConvPoint: Continuous Convolutions for Point Cloud Processing | |
| SuperCluster | 210M | - | 77.3 | Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering | |
| PointNet++ | 3.0M | 95.7 | 68.3 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | |
| KPConv | 14M | 97.8 | 81.1 | KPConv: Flexible and Deformable Convolution for Point Clouds | |
| SPG | 280K | 95.5 | 60.6 | Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs | |
| ShellNet | N/A | 96.4 | 57.4 | ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics | - |
| EyeNet | - | - | 79.6 | Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene |
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