3D Semantic Segmentation On Toronto 3D
Metriken
OA
mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | OA | mIoU | Paper Title | Repository |
|---|---|---|---|---|
| PointNet++ | 91.21 | 56.55 | Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | |
| KPFCNN | 91.71 | 60.30 | Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | |
| TGNet | 91.64 | 58.34 | Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | |
| RandLANet | 93.50 | 68.40 | RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds | |
| DGCNN | 89.00 | 49.60 | Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | |
| MS-PCNN | 91.53 | 58.01 | Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | |
| SCF-Net | 95.50 | 73.60 | SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation |
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