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3D Semantic Segmentation On Toronto 3D

Metrics

OA
mIoU

Results

Performance results of various models on this benchmark

Paper Title
SCF-Net95.5073.60SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
RandLANet93.5068.40RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
KPFCNN91.7160.30Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
TGNet91.6458.34Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
MS-PCNN91.5358.01Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
PointNet++91.2156.55Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
DGCNN89.0049.60Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
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3D Semantic Segmentation On Toronto 3D | SOTA | HyperAI