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プラットフォーム
ホーム
SOTA
セマンティックセグメンテーション
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
Sonata + PTv3
82.3
128M
93.3
Sonata: Self-Supervised Learning of Reliable Point Representations
PTv3 + PPT
80.8
24.1M
92.6
Point Transformer V3: Simpler, Faster, Stronger
PonderV2 + SparseUNet
79.9
-
92.5
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Swin3D-L
79.8
N/A
92.4
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
PointVector-XL
78.4
-
91.9
PointVector: A Vector Representation In Point Cloud Analysis
PPT + SparseUNet
78.1
N/A
92.2
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
WindowNorm+StratifiedTransformer
77.6
8.2M
91.7
Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities
EQ-Net
77.5
N/A
-
A Unified Query-based Paradigm for Point Cloud Understanding
PointMetaBase-XXL
77.0
19.7M
91.3
Meta Architecture for Point Cloud Analysis
Superpoint Transformer
76.0
0.212M
90.4
Efficient 3D Semantic Segmentation with Superpoint Transformer
SuperCluster
75.3
0.21M
-
Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
PointNeXt-XL
74.9
41.6M
90.3
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
DeepViewAgg
74.7
41.2M
90.1
Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation
RepSurf-U
74.3
0.97M
90.8
Surface Representation for Point Clouds
WindowNorm+PointTransformer
74.1
8.0M
90.2
Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities
PointNeXt-L
73.9
7.1M
89.9
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
PointTransformer
73.5
7.8M
90.2
Point Transformer
CBL
73.1
N/A
89.6
Contrastive Boundary Learning for Point Cloud Segmentation
BAAF-Net
72.2
N/A
88.9
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion
SCF-Net
71.6
N/A
88.4
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
0 of 54 row(s) selected.
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