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Models/DGCNN + MD

DGCNN + MD

Reported on 8 benchmarks across 5 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

Shape Representation Of 3D Point CloudsonModelNet40
Mean Accuracy· 2021-09-01
89.88
best: 92.4 (ULIP + PointMLP)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452
Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· 2021-09-01
93.39
best: 95.3 (PointGST)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452
3D Point Cloud ClassificationonModelNet40
Mean Accuracy· 2021-09-01
89.88
best: 92.4 (ULIP + PointMLP)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· 2021-09-01
93.39
best: 95.3 (PointGST)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452
3D Point Cloud ReconstructiononModelNet40
Mean Accuracy· 2021-09-01
89.88
best: 92.4 (ULIP + PointMLP)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· 2021-09-01
93.39
best: 95.3 (PointGST)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452

Medical1 result

Semantic SegmentationonShapeNet-Part
Instance Average IoU· 2021-09-01
85.5
best: 89.1 (GeomGCNN)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452

Audio1 result

10-shot image generationonShapeNet-Part
Instance Average IoU· 2021-09-01
85.5
best: 89.1 (GeomGCNN)
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes arXiv:2109.00452