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Models/InterpCNN

InterpCNN

Reported on 7 benchmarks across 5 tasks · 1 paper · 3 SOTA

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

Computer Vision3 results

Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· 2019-08-13
93
best: 95.3 (PointGST)
SOTA
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· 2019-08-13
93
best: 95.3 (PointGST)
SOTA
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· 2019-08-13
93
best: 95.3 (PointGST)
SOTA
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512

Medical2 results

Semantic SegmentationonShapeNet-Part
Class Average IoU· 2019-08-13
84
best: 87.7 (Feature Geometric Net (FG-Net))
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512
Semantic SegmentationonShapeNet-Part
Instance Average IoU· 2019-08-13
86.3
best: 89.1 (GeomGCNN)
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512

Audio2 results

10-shot image generationonShapeNet-Part
Class Average IoU· 2019-08-13
84
best: 87.7 (Feature Geometric Net (FG-Net))
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512
10-shot image generationonShapeNet-Part
Instance Average IoU· 2019-08-13
86.3
best: 89.1 (GeomGCNN)
Interpolated Convolutional Networks for 3D Point Cloud Understanding arXiv:1908.04512