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Models/ULIP + PointMLP

ULIP + PointMLP

Reported on 15 benchmarks across 3 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 Vision15 results

Shape Representation Of 3D Point CloudsonModelNet40
Mean Accuracy· uses extra data· 2022-12-10
92.4
SOTA
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ClassificationonModelNet40
Mean Accuracy· uses extra data· 2022-12-10
92.4
SOTA
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ReconstructiononModelNet40
Mean Accuracy· uses extra data· 2022-12-10
92.4
SOTA
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
Shape Representation Of 3D Point CloudsonScanObjectNN
Mean Accuracy· uses extra data· 2022-12-10
88.5
best: 93.8 (GPSFormer)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
Shape Representation Of 3D Point CloudsonScanObjectNN
Overall Accuracy· uses extra data· 2022-12-10
89.4
best: 97.2 (OmniVec2)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· uses extra data· 2022-12-10
94.7
best: 95.3 (PointGST)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
Shape Representation Of 3D Point CloudsonModelNet40
Accuracy (%)· uses extra data· 2022-12-10
61.5
best: 88.2 (Uni3D)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ClassificationonScanObjectNN
Mean Accuracy· uses extra data· 2022-12-10
88.5
best: 93.8 (GPSFormer)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ClassificationonScanObjectNN
Overall Accuracy· uses extra data· 2022-12-10
89.4
best: 97.2 (OmniVec2)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· uses extra data· 2022-12-10
94.7
best: 95.3 (PointGST)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ClassificationonModelNet40
Accuracy (%)· uses extra data· 2022-12-10
61.5
best: 88.2 (Uni3D)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ReconstructiononScanObjectNN
Mean Accuracy· uses extra data· 2022-12-10
88.5
best: 93.8 (GPSFormer)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ReconstructiononScanObjectNN
Overall Accuracy· uses extra data· 2022-12-10
89.4
best: 97.2 (OmniVec2)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· uses extra data· 2022-12-10
94.7
best: 95.3 (PointGST)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171
3D Point Cloud ReconstructiononModelNet40
Accuracy (%)· uses extra data· 2022-12-10
61.5
best: 88.2 (Uni3D)
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding arXiv:2212.05171