Point cloud classification
Collection
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A PointConv point cloud classification model (density-reweighted continuous convolution). 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(
"pointconv-density-base.modelnet40.wenxuan-wu",
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, 1024)
@inproceedings{wu2019pointconv,
title = {PointConv: Deep Convolutional Networks on 3D Point Clouds},
author = {Wenxuan Wu and Zhongang Qi and Li Fuxin},
booktitle = {CVPR},
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},
}