3D Semantic Segmentation On Toronto 3D

Métriques

OA

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
SCF-Net	95.50	73.60	SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
RandLANet	93.50	68.40	RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
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
MS-PCNN	91.53	58.01	Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
PointNet++	91.21	56.55	Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
DGCNN	89.00	49.60	Toronto-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