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