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Models/SpiderCNN

SpiderCNN

Reported on 24 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 Vision12 results

Shape Representation Of 3D Point CloudsonScanObjectNN
Mean Accuracy· 2018-03-30
69.8
best: 93.8 (GPSFormer)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Shape Representation Of 3D Point CloudsonScanObjectNN
Overall Accuracy· 2018-03-30
73.7
best: 97.2 (OmniVec2)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Shape Representation Of 3D Point CloudsonIntrA
F1 score (5-fold)· 2018-03-30
0.872
best: 0.936 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· 2018-03-30
92.4
best: 95.3 (PointGST)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ClassificationonScanObjectNN
Mean Accuracy· 2018-03-30
69.8
best: 93.8 (GPSFormer)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ClassificationonScanObjectNN
Overall Accuracy· 2018-03-30
73.7
best: 97.2 (OmniVec2)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ClassificationonIntrA
F1 score (5-fold)· 2018-03-30
0.872
best: 0.936 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· 2018-03-30
92.4
best: 95.3 (PointGST)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ReconstructiononScanObjectNN
Mean Accuracy· 2018-03-30
69.8
best: 93.8 (GPSFormer)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ReconstructiononScanObjectNN
Overall Accuracy· 2018-03-30
73.7
best: 97.2 (OmniVec2)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ReconstructiononIntrA
F1 score (5-fold)· 2018-03-30
0.872
best: 0.936 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· 2018-03-30
92.4
best: 95.3 (PointGST)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527

Medical6 results

Semantic SegmentationonIntrA
DSC (A)· 2018-03-30
75.82
best: 89.71 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Semantic SegmentationonIntrA
DSC (V)· 2018-03-30
94.53
best: 97.29 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Semantic SegmentationonIntrA
IoU (A)· 2018-03-30
67.25
best: 82.39 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Semantic SegmentationonIntrA
IoU (V)· 2018-03-30
90.16
best: 94.82 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Semantic SegmentationonShapeNet-Part
Class Average IoU· 2018-03-30
82.4
best: 87.7 (Feature Geometric Net (FG-Net))
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
Semantic SegmentationonShapeNet-Part
Instance Average IoU· 2018-03-30
85.3
best: 89.1 (GeomGCNN)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527

Audio6 results

10-shot image generationonIntrA
DSC (A)· 2018-03-30
75.82
best: 89.71 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
10-shot image generationonIntrA
DSC (V)· 2018-03-30
94.53
best: 97.29 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
10-shot image generationonIntrA
IoU (A)· 2018-03-30
67.25
best: 82.39 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
10-shot image generationonIntrA
IoU (V)· 2018-03-30
90.16
best: 94.82 (3DMedPT)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
10-shot image generationonShapeNet-Part
Class Average IoU· 2018-03-30
82.4
best: 87.7 (Feature Geometric Net (FG-Net))
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527
10-shot image generationonShapeNet-Part
Instance Average IoU· 2018-03-30
85.3
best: 89.1 (GeomGCNN)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters arXiv:1803.11527