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Models/ULIP-2 + PointNeXt (no voting)

ULIP-2 + PointNeXt (no voting)

Reported on 6 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 Vision6 results

  • Shape Representation Of 3D Point CloudsonScanObjectNN
    Mean Accuracy· uses extra data· 2023-05-14
    90.3
    best: 93.8 (GPSFormer)
    ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingarXiv:2305.08275
  • Shape Representation Of 3D Point CloudsonScanObjectNN
    Overall Accuracy· uses extra data· 2023-05-14
    90.8
    best: 97.2 (OmniVec2)
    ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingarXiv:2305.08275
  • 3D Point Cloud ClassificationonScanObjectNN
    Mean Accuracy· uses extra data· 2023-05-14
    90.3
    best: 93.8 (GPSFormer)
    ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingarXiv:2305.08275
  • 3D Point Cloud ClassificationonScanObjectNN
    Overall Accuracy· uses extra data· 2023-05-14
    90.8
    best: 97.2 (OmniVec2)
    ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingarXiv:2305.08275
  • 3D Point Cloud ReconstructiononScanObjectNN
    Mean Accuracy· uses extra data· 2023-05-14
    90.3
    best: 93.8 (GPSFormer)
    ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingarXiv:2305.08275
  • 3D Point Cloud ReconstructiononScanObjectNN
    Overall Accuracy· uses extra data· 2023-05-14
    90.8
    best: 97.2 (OmniVec2)
    ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingarXiv:2305.08275