3D Semantic Segmentation On Sensaturban
Metriken
mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | mIoU | Paper Title | Repository |
|---|---|---|---|
| TangentConv | 33.30 | Tangent Convolutions for Dense Prediction in 3D | - |
| SPGraph | 37.29 | Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs | - |
| LCPFormer | 63.4 | LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers | - |
| KPConv | 57.58 | KPConv: Flexible and Deformable Convolution for Point Clouds | - |
| SparseConv | 42.66 | 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks | - |
| EyeNet | 62.30 | Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene | - |
| SCF-Net | 55.1 | SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation | |
| BEV-Seg3D-Net | 61.7 | Efficient Urban-scale Point Clouds Segmentation with BEV Projection | - |
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