I built a pytorch library of pretrained models for 3D point clouds following timm standards [P]

Key points

• I started this project after being unable to use torch-points3d at the time (too many installation errors).1
• import torch import torchpointcloud as tp model = tp.createmodel( "pointnext-sm.scanobjectnn-hardest.openpoints", task="classification", pretrained=True, ).eval() pos = torch.randn(2048, 3) x = torch.cat([pos, pos[:, 1:2] - pos[:, 1].min()], dim=1) # xyz + height batch = torch.zeros(2048, dtype=torch.long) logits = model(x, pos, batch) # (1, 15)1
• Classification, semantic and part segmentation, detection, and self-supervised pretraining: PointNet++, DGCNN, KPConv, RandLA-Net, PointNeXt, PointMLP, Point Transformer v1/v2/v3, Sonata, PointMamba, VoteNet, 3DETR, PointPillars, SECOND, PointRCNN, SPVCNN, OctFormer, Point-MAE/BERT/GPT and more.1
• Packed-batch tensors (the PyTorch Geometric convention).1

Sources (1)

• [1]I built a pytorch library of pretrained models for 3D point clouds following timm standards [P]
  r/MachineLearning (top, daily) · Oct 13, 01:50 PM

  What it is: torch-pointcloud is a plain-PyTorch library that puts multiple point cloud architectures and their pretrained checkpoints behind one create_model() factory, in the spirit of timm.

  I started this project after being unable to use torch-points3d at the time (too many installation errors).

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