Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Point-M2AE

Point-M2AE

Reported on 37 benchmarks across 4 tasks · 1 paper · 7 SOTA

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

Computer Vision37 results

Shape Representation Of 3D Point CloudsonModelNet40 5-way (10-shot)
Overall Accuracy· uses extra data· 2022-05-28
96.8
best: 98 (PointGPT)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 5-way (20-shot)
Overall Accuracy· uses extra data· 2022-05-28
98.3
best: 99.5 (ReCon++)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 5-way (10-shot)
Overall Accuracy· uses extra data· 2022-05-28
96.8
best: 98 (PointGPT)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 5-way (20-shot)
Overall Accuracy· uses extra data· 2022-05-28
98.3
best: 99.5 (ReCon++)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud Linear ClassificationonModelNet40
Overall Accuracy· uses extra data· 2022-05-28
92.9
best: 93.6 (ReCon++)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 5-way (10-shot)
Overall Accuracy· uses extra data· 2022-05-28
96.8
best: 98 (PointGPT)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 5-way (20-shot)
Overall Accuracy· uses extra data· 2022-05-28
98.3
best: 99.5 (ReCon++)
SOTA
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonScanObjectNN
OBJ-BG (OA)· uses extra data· 2022-05-28
91.22
best: 99.48 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonScanObjectNN
OBJ-ONLY (OA)· uses extra data· 2022-05-28
88.81
best: 97.76 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonScanObjectNN
Overall Accuracy· uses extra data· 2022-05-28
86.43
best: 97.2 (OmniVec2)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· uses extra data· 2022-05-28
94
best: 95.3 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 10-way (20-shot)
Overall Accuracy· uses extra data· 2022-05-28
95
best: 96.5 (ReCon++)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 10-way (20-shot)
Standard Deviation· uses extra data· 2022-05-28
3
best: 13.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 5-way (10-shot)
Standard Deviation· uses extra data· 2022-05-28
1.8
best: 16 (PointNet++)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 10-way (10-shot)
Overall Accuracy· uses extra data· 2022-05-28
92.3
best: 95 (Point-JEPA)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 10-way (10-shot)
Standard Deviation· uses extra data· 2022-05-28
4.5
best: 13.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
Shape Representation Of 3D Point CloudsonModelNet40 5-way (20-shot)
Standard Deviation· uses extra data· 2022-05-28
1.4
best: 15.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonScanObjectNN
OBJ-BG (OA)· uses extra data· 2022-05-28
91.22
best: 99.48 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonScanObjectNN
OBJ-ONLY (OA)· uses extra data· 2022-05-28
88.81
best: 97.76 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonScanObjectNN
Overall Accuracy· uses extra data· 2022-05-28
86.43
best: 97.2 (OmniVec2)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· uses extra data· 2022-05-28
94
best: 95.3 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 10-way (20-shot)
Overall Accuracy· uses extra data· 2022-05-28
95
best: 96.5 (ReCon++)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 10-way (20-shot)
Standard Deviation· uses extra data· 2022-05-28
3
best: 13.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 5-way (10-shot)
Standard Deviation· uses extra data· 2022-05-28
1.8
best: 16 (PointNet++)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 10-way (10-shot)
Overall Accuracy· uses extra data· 2022-05-28
92.3
best: 95 (Point-JEPA)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 10-way (10-shot)
Standard Deviation· uses extra data· 2022-05-28
4.5
best: 13.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ClassificationonModelNet40 5-way (20-shot)
Standard Deviation· uses extra data· 2022-05-28
1.4
best: 15.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononScanObjectNN
OBJ-BG (OA)· uses extra data· 2022-05-28
91.22
best: 99.48 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononScanObjectNN
OBJ-ONLY (OA)· uses extra data· 2022-05-28
88.81
best: 97.76 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononScanObjectNN
Overall Accuracy· uses extra data· 2022-05-28
86.43
best: 97.2 (OmniVec2)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· uses extra data· 2022-05-28
94
best: 95.3 (PointGST)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 10-way (20-shot)
Overall Accuracy· uses extra data· 2022-05-28
95
best: 96.5 (ReCon++)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 10-way (20-shot)
Standard Deviation· uses extra data· 2022-05-28
3
best: 13.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 5-way (10-shot)
Standard Deviation· uses extra data· 2022-05-28
1.8
best: 16 (PointNet++)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 10-way (10-shot)
Overall Accuracy· uses extra data· 2022-05-28
92.3
best: 95 (Point-JEPA)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 10-way (10-shot)
Standard Deviation· uses extra data· 2022-05-28
4.5
best: 13.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401
3D Point Cloud ReconstructiononModelNet40 5-way (20-shot)
Standard Deviation· uses extra data· 2022-05-28
1.4
best: 15.5 (PointNet)
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training arXiv:2205.14401