Point cloud classification
Collection
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A Point-MAE point cloud classification model (masked point autoencoder). Trained on ModelNet40.
pip install torch-pointcloud
import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate
model, info = tp.create_model(
"point-mae-base.modelnet40-8k.yatian-pang",
task="classification",
pretrained=True,
return_info=True,
)
model = model.eval()
# synthetic sample with the keys a dataset provides
num_points = 8192
sample = {
"pos": torch.randn(num_points, 3),
"normal": torch.randn(num_points, 3),
}
data = info["transform"](sample)
data = collate([data])
with torch.no_grad():
logits = model(data.get("x"), data["pos"], data["batch"])
with torch.no_grad():
embeddings = model.forward_features(data.get("x"), data["pos"], data["batch"])
model.reset_classifier(num_classes=0)
with torch.no_grad():
embeddings = model(data.get("x"), data["pos"], data["batch"]) # (B, 768)
@inproceedings{pang2022pointmae,
title = {Masked Autoencoders for Point Cloud Self-supervised Learning},
author = {Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
booktitle = {ECCV},
year = {2022}
}
@inproceedings{wu2015modelnet,
title = {3D ShapeNets: A Deep Representation for Volumetric Shapes},
author = {Zhirong Wu and Shuran Song and Aditya Khosla and Fisher Yu and Linguang Zhang and Xiaoou Tang and Jianxiong Xiao},
booktitle = {CVPR},
year = {2015}
}
@software{dujardin2026pytorchpointcloud,
author = {Arthur Dujardin},
title = {PyTorch PointCloud},
year = {2026},
doi = {10.5281/zenodo.22159632},
url = {https://github.com/arthurdjn/pytorch-pointcloud},
}