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Models/Point-RAE

Point-RAE

Reported on 27 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 Vision27 results

Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· uses extra data· 2023-09-25
94.1
best: 95.3 (PointGST)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 10-way (20-shot)
Overall Accuracy· uses extra data· 2023-09-25
95.8
best: 96.5 (ReCon++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 10-way (20-shot)
Standard Deviation· uses extra data· 2023-09-25
3
best: 13.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 5-way (10-shot)
Overall Accuracy· uses extra data· 2023-09-25
97.3
best: 98 (PointGPT)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 5-way (10-shot)
Standard Deviation· uses extra data· 2023-09-25
1.6
best: 16 (PointNet++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 10-way (10-shot)
Overall Accuracy· uses extra data· 2023-09-25
93.3
best: 95 (Point-JEPA)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 10-way (10-shot)
Standard Deviation· uses extra data· 2023-09-25
4
best: 13.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 5-way (20-shot)
Overall Accuracy· uses extra data· 2023-09-25
98.7
best: 99.5 (ReCon++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
Shape Representation Of 3D Point CloudsonModelNet40 5-way (20-shot)
Standard Deviation· uses extra data· 2023-09-25
1.3
best: 15.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· uses extra data· 2023-09-25
94.1
best: 95.3 (PointGST)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 10-way (20-shot)
Overall Accuracy· uses extra data· 2023-09-25
95.8
best: 96.5 (ReCon++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 10-way (20-shot)
Standard Deviation· uses extra data· 2023-09-25
3
best: 13.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 5-way (10-shot)
Overall Accuracy· uses extra data· 2023-09-25
97.3
best: 98 (PointGPT)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 5-way (10-shot)
Standard Deviation· uses extra data· 2023-09-25
1.6
best: 16 (PointNet++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 10-way (10-shot)
Overall Accuracy· uses extra data· 2023-09-25
93.3
best: 95 (Point-JEPA)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 10-way (10-shot)
Standard Deviation· uses extra data· 2023-09-25
4
best: 13.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 5-way (20-shot)
Overall Accuracy· uses extra data· 2023-09-25
98.7
best: 99.5 (ReCon++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ClassificationonModelNet40 5-way (20-shot)
Standard Deviation· uses extra data· 2023-09-25
1.3
best: 15.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· uses extra data· 2023-09-25
94.1
best: 95.3 (PointGST)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 10-way (20-shot)
Overall Accuracy· uses extra data· 2023-09-25
95.8
best: 96.5 (ReCon++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 10-way (20-shot)
Standard Deviation· uses extra data· 2023-09-25
3
best: 13.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 5-way (10-shot)
Overall Accuracy· uses extra data· 2023-09-25
97.3
best: 98 (PointGPT)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 5-way (10-shot)
Standard Deviation· uses extra data· 2023-09-25
1.6
best: 16 (PointNet++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 10-way (10-shot)
Overall Accuracy· uses extra data· 2023-09-25
93.3
best: 95 (Point-JEPA)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 10-way (10-shot)
Standard Deviation· uses extra data· 2023-09-25
4
best: 13.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 5-way (20-shot)
Overall Accuracy· uses extra data· 2023-09-25
98.7
best: 99.5 (ReCon++)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670
3D Point Cloud ReconstructiononModelNet40 5-way (20-shot)
Standard Deviation· uses extra data· 2023-09-25
1.3
best: 15.5 (PointNet)
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning arXiv:2310.03670