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SOTA
Semantic Segmentation
Semantic Segmentation On S3Dis
Semantic Segmentation On S3Dis
評価指標
Mean IoU
Number of params
oAcc
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Mean IoU
Number of params
oAcc
Paper Title
Repository
A-CNN
62.9
N/A
87.3
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
DeepViewAgg
74.7
41.2M
90.1
Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation
RandLA-Net
-
1.2M
87.1
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
MinkowskiNet
65.4
37.9M
-
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
MuGNet
69.8
N/A
88.5
MuGNet: Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
EQ-Net
77.5
N/A
-
A Unified Query-based Paradigm for Point Cloud Understanding
Feature Geometric Net (FG-Net)
70.8
N/A
88.2
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
Swin3D-L
79.8
N/A
92.4
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
JSENet
67.7
N/A
-
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
BIM-Net
-
-
-
Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation
PointCNN
65.4
N/A
88.1
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
PointASNL
68.7
N/A
88.8
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
KPConv
70.6
14.1M
-
KPConv: Flexible and Deformable Convolution for Point Clouds
PointCNN
65.4
N/A
-
Point Transformer
BAAF-Net
72.2
N/A
88.9
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion
PPT + SparseUNet
78.1
N/A
92.2
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
JSNet
61.7
N/A
88.7
JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds
ShellNet
66.8
N/A
-
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
-
SPGraph
62.1
N/A
85.5
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
3P-RNN
56.3
N/A
86.9
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
0 of 54 row(s) selected.
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