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Models/CLIP2Point

CLIP2Point

Reported on 17 benchmarks across 4 tasks · 1 paper · 17 SOTA

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

Computer Vision17 results

  • Shape Representation Of 3D Point CloudsonScanObjectNN
    OBJ_BG Accuracy(%)· uses extra data· 2022-10-03
    35.46
    best: 41.22 (PointCLIP V2)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • Shape Representation Of 3D Point CloudsonScanObjectNN
    OBJ_ONLY Accuracy(%)· uses extra data· 2022-10-03
    30.46
    best: 65.4 (ReCon++)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • Shape Representation Of 3D Point CloudsonScanObjectNN
    PB_T50_RS Accuracy (%)· uses extra data· 2022-10-03
    23.32
    best: 35.36 (PointCLIP V2)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • Shape Representation Of 3D Point CloudsonModelNet40
    Accuracy (%)· uses extra data· 2022-10-03
    49.38
    best: 88.2 (Uni3D)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • Shape Representation Of 3D Point CloudsonModelNet10
    Accuracy (%)· uses extra data· 2022-10-03
    66.63
    best: 75.6 (ReCon)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ClassificationonScanObjectNN
    OBJ_BG Accuracy(%)· uses extra data· 2022-10-03
    35.46
    best: 41.22 (PointCLIP V2)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ClassificationonScanObjectNN
    OBJ_ONLY Accuracy(%)· uses extra data· 2022-10-03
    30.46
    best: 65.4 (ReCon++)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ClassificationonScanObjectNN
    PB_T50_RS Accuracy (%)· uses extra data· 2022-10-03
    23.32
    best: 35.36 (PointCLIP V2)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ClassificationonModelNet40
    Accuracy (%)· uses extra data· 2022-10-03
    49.38
    best: 88.2 (Uni3D)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ClassificationonModelNet10
    Accuracy (%)· uses extra data· 2022-10-03
    66.63
    best: 75.6 (ReCon)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • Training-free 3D Point Cloud ClassificationonModelNet40
    Accuracy (%)· uses extra data· 2022-10-03
    49.4
    best: 85.3 (Point-GN)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • Training-free 3D Point Cloud ClassificationonScanObjectNN
    Accuracy (%)· uses extra data· 2022-10-03
    23.2
    best: 86.4 (Point-GN)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ReconstructiononScanObjectNN
    OBJ_BG Accuracy(%)· uses extra data· 2022-10-03
    35.46
    best: 41.22 (PointCLIP V2)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ReconstructiononScanObjectNN
    OBJ_ONLY Accuracy(%)· uses extra data· 2022-10-03
    30.46
    best: 65.4 (ReCon++)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ReconstructiononScanObjectNN
    PB_T50_RS Accuracy (%)· uses extra data· 2022-10-03
    23.32
    best: 35.36 (PointCLIP V2)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ReconstructiononModelNet40
    Accuracy (%)· uses extra data· 2022-10-03
    49.38
    best: 88.2 (Uni3D)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055
  • 3D Point Cloud ReconstructiononModelNet10
    Accuracy (%)· uses extra data· 2022-10-03
    66.63
    best: 75.6 (ReCon)
    SOTA
    CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingarXiv:2210.01055