Point cloud classification
Collection
47 items • Updated
A DGCNN point cloud classification model (dynamic graph convolution over EdgeConv features). Trained on ModelNet40.
pip install torch-pointcloud
This checkpoint also needs pyg-lib, which needs a build matching your torch and CUDA: see the installation guide.
import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate
model, info = tp.create_model(
"dgcnn.modelnet40-1024.an-tao",
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, 2048)
@article{wang2019dgcnn,
title = {Dynamic Graph CNN for Learning on Point Clouds},
author = {Yue Wang and Yongbin Sun and Ziwei Liu and Sanjay E. Sarma and Michael M. Bronstein and Justin M. Solomon},
journal = {ACM Transactions on Graphics},
volume = {38},
number = {5},
year = {2019}
}
@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},
}