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/PointTransformer

PointTransformer

Reported on 30 benchmarks across 6 tasks · 1 paper · 16 SOTA

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

Medical11 results

  • Semantic SegmentationonS3DIS Area5
    mAcc· 2020-12-16
    76.5
    best: 81.6 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS Area5
    oAcc· 2020-12-16
    90.8
    best: 93 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS
    Mean IoU· 2020-12-16
    73.5
    best: 82.3 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS
    mAcc· 2020-12-16
    81.9
    best: 89.9 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS
    oAcc· 2020-12-16
    90.2
    best: 93.3 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS
    mIoU (6-Fold)· 2020-12-16
    73.5
    best: 76 (Superpoint Transformer)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS
    mIoU (Area-5)· 2020-12-16
    70.4
    best: 72.4 (OneFormer3D)
    SOTA
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS Area5
    mIoU· 2020-12-16
    70.4
    best: 76 (Sonata + PTv3)
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonS3DIS
    Params (M)· 2020-12-16
    7.8
    best: 41.6 (PointNeXt-XL)
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonShapeNet-Part
    Class Average IoU· 2020-12-16
    83.7
    best: 87.7 (Feature Geometric Net (FG-Net))
    Point TransformerarXiv:2012.09164
  • Semantic SegmentationonShapeNet-Part
    Instance Average IoU· 2020-12-16
    86.6
    best: 89.1 (GeomGCNN)
    Point TransformerarXiv:2012.09164

Audio11 results

  • 10-shot image generationonS3DIS Area5
    mAcc· 2020-12-16
    76.5
    best: 81.6 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS Area5
    oAcc· 2020-12-16
    90.8
    best: 93 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS
    Mean IoU· 2020-12-16
    73.5
    best: 82.3 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS
    mAcc· 2020-12-16
    81.9
    best: 89.9 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS
    oAcc· 2020-12-16
    90.2
    best: 93.3 (Sonata + PTv3)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS
    mIoU (6-Fold)· 2020-12-16
    73.5
    best: 76 (Superpoint Transformer)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS
    mIoU (Area-5)· 2020-12-16
    70.4
    best: 72.4 (OneFormer3D)
    SOTA
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS Area5
    mIoU· 2020-12-16
    70.4
    best: 76 (Sonata + PTv3)
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonS3DIS
    Params (M)· 2020-12-16
    7.8
    best: 41.6 (PointNeXt-XL)
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonShapeNet-Part
    Class Average IoU· 2020-12-16
    83.7
    best: 87.7 (Feature Geometric Net (FG-Net))
    Point TransformerarXiv:2012.09164
  • 10-shot image generationonShapeNet-Part
    Instance Average IoU· 2020-12-16
    86.6
    best: 89.1 (GeomGCNN)
    Point TransformerarXiv:2012.09164

Computer Vision8 results

  • 3D Semantic SegmentationonS3DIS
    mIoU (6-Fold)· 2020-12-16
    73.5
    best: 76 (Superpoint Transformer)
    SOTA
    Point TransformerarXiv:2012.09164
  • 3D Semantic SegmentationonS3DIS
    mIoU (Area-5)· 2020-12-16
    70.4
    best: 72.4 (OneFormer3D)
    SOTA
    Point TransformerarXiv:2012.09164
  • Shape Representation Of 3D Point CloudsonModelNet40
    Mean Accuracy· 2020-12-16
    90.6
    best: 92.4 (ULIP + PointMLP)
    Point TransformerarXiv:2012.09164
  • Shape Representation Of 3D Point CloudsonModelNet40
    Overall Accuracy· 2020-12-16
    93.7
    best: 95.3 (PointGST)
    Point TransformerarXiv:2012.09164
  • 3D Point Cloud ClassificationonModelNet40
    Mean Accuracy· 2020-12-16
    90.6
    best: 92.4 (ULIP + PointMLP)
    Point TransformerarXiv:2012.09164
  • 3D Point Cloud ClassificationonModelNet40
    Overall Accuracy· 2020-12-16
    93.7
    best: 95.3 (PointGST)
    Point TransformerarXiv:2012.09164
  • 3D Point Cloud ReconstructiononModelNet40
    Mean Accuracy· 2020-12-16
    90.6
    best: 92.4 (ULIP + PointMLP)
    Point TransformerarXiv:2012.09164
  • 3D Point Cloud ReconstructiononModelNet40
    Overall Accuracy· 2020-12-16
    93.7
    best: 95.3 (PointGST)
    Point TransformerarXiv:2012.09164