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Models/SSFSL+DGCNN

SSFSL+DGCNN

Reported on 18 benchmarks across 3 tasks · 1 paper

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

Computer Vision18 results

Shape Representation Of 3D Point CloudsonModelNet40 5-way (10-shot)
Overall Accuracy· 2020-09-29
60
best: 98 (PointGPT)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
Shape Representation Of 3D Point CloudsonModelNet40 5-way (10-shot)
Standard Deviation· 2020-09-29
8.9
best: 16 (PointNet++)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
Shape Representation Of 3D Point CloudsonModelNet40 10-way (10-shot)
Overall Accuracy· 2020-09-29
48.5
best: 95 (Point-JEPA)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
Shape Representation Of 3D Point CloudsonModelNet40 10-way (10-shot)
Standard Deviation· 2020-09-29
5.6
best: 13.5 (PointNet)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
Shape Representation Of 3D Point CloudsonModelNet40 5-way (20-shot)
Overall Accuracy· 2020-09-29
65.7
best: 99.5 (ReCon++)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
Shape Representation Of 3D Point CloudsonModelNet40 5-way (20-shot)
Standard Deviation· 2020-09-29
8.4
best: 15.5 (PointNet)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ClassificationonModelNet40 5-way (10-shot)
Overall Accuracy· 2020-09-29
60
best: 98 (PointGPT)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ClassificationonModelNet40 5-way (10-shot)
Standard Deviation· 2020-09-29
8.9
best: 16 (PointNet++)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ClassificationonModelNet40 10-way (10-shot)
Overall Accuracy· 2020-09-29
48.5
best: 95 (Point-JEPA)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ClassificationonModelNet40 10-way (10-shot)
Standard Deviation· 2020-09-29
5.6
best: 13.5 (PointNet)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ClassificationonModelNet40 5-way (20-shot)
Overall Accuracy· 2020-09-29
65.7
best: 99.5 (ReCon++)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ClassificationonModelNet40 5-way (20-shot)
Standard Deviation· 2020-09-29
8.4
best: 15.5 (PointNet)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ReconstructiononModelNet40 5-way (10-shot)
Overall Accuracy· 2020-09-29
60
best: 98 (PointGPT)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ReconstructiononModelNet40 5-way (10-shot)
Standard Deviation· 2020-09-29
8.9
best: 16 (PointNet++)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ReconstructiononModelNet40 10-way (10-shot)
Overall Accuracy· 2020-09-29
48.5
best: 95 (Point-JEPA)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ReconstructiononModelNet40 10-way (10-shot)
Standard Deviation· 2020-09-29
5.6
best: 13.5 (PointNet)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ReconstructiononModelNet40 5-way (20-shot)
Overall Accuracy· 2020-09-29
65.7
best: 99.5 (ReCon++)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168
3D Point Cloud ReconstructiononModelNet40 5-way (20-shot)
Standard Deviation· 2020-09-29
8.4
best: 15.5 (PointNet)
Self-Supervised Few-Shot Learning on Point Clouds arXiv:2009.14168