TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

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

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

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

Models/ULIP + PointNet++(ssg)

ULIP + PointNet++(ssg)

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 CloudsonModelNet40
    Mean Accuracy· uses extra data· 2022-12-10
    91.2
    best: 92.4 (ULIP + PointMLP)
    ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingarXiv:2212.05171
  • Shape Representation Of 3D Point CloudsonModelNet40
    Overall Accuracy· uses extra data· 2022-12-10
    93.4
    best: 95.3 (PointGST)
    ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingarXiv:2212.05171
  • 3D Point Cloud ClassificationonModelNet40
    Mean Accuracy· uses extra data· 2022-12-10
    91.2
    best: 92.4 (ULIP + PointMLP)
    ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingarXiv:2212.05171
  • 3D Point Cloud ClassificationonModelNet40
    Overall Accuracy· uses extra data· 2022-12-10
    93.4
    best: 95.3 (PointGST)
    ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingarXiv:2212.05171
  • 3D Point Cloud ReconstructiononModelNet40
    Mean Accuracy· uses extra data· 2022-12-10
    91.2
    best: 92.4 (ULIP + PointMLP)
    ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingarXiv:2212.05171
  • 3D Point Cloud ReconstructiononModelNet40
    Overall Accuracy· uses extra data· 2022-12-10
    93.4
    best: 95.3 (PointGST)
    ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingarXiv:2212.05171