dgcnn.shapenetpart.an-tao

Model card for dgcnn.shapenetpart.an-tao

A DGCNN point cloud part segmentation model (dynamic graph convolution over EdgeConv features). Trained on ShapeNetPart.

Model Details

• Model Type: Point cloud part segmentation
• Model Stats:
  • Params (M): 1.5
  • Classes: 50
  • Features: 1280
• Dataset: ShapeNetPart
• Metrics: ins_mIoU 85.23, cls_mIoU 80.92
• Paper: Dynamic Graph CNN for Learning on Point Clouds
• Converted from: antao97/dgcnn.pytorch (MIT)
• Library: torch-pointcloud

Install

pip install torch-pointcloud

This checkpoint also needs pyg-lib, which needs a build matching your torch and CUDA: see the installation guide.

Usage

import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate

model, info = tp.create_model(
    "dgcnn.shapenetpart.an-tao",
    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),
    "category": torch.tensor(0),
    "segment": torch.zeros(num_points, dtype=torch.long),
}
data = info["transform"](sample)
data = collate([data])

with torch.no_grad():
    logits = model(data.get("x"), data["pos"], data["batch"], data["category"])

Feature extraction

with torch.no_grad():
    features = model.forward_features(data.get("x"), data["pos"], data["batch"], data["category"])

model.reset_classifier(num_classes=0)
with torch.no_grad():
    features = model(data.get("x"), data["pos"], data["batch"], data["category"])  # (N, 1280)

Citation

@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}
}

@article{yi2016shapenetpart,
  title   = {A Scalable Active Framework for Region Annotation in {3D} Shape Collections},
  author  = {Yi, Li and Kim, Vladimir G. and Ceylan, Duygu and Shen, I-Chao and Yan, Mengyan and Su, Hao and Lu, Cewu and Huang, Qixing and Sheffer, Alla and Guibas, Leonidas},
  journal = {ACM Transactions on Graphics (TOG)},
  volume  = {35},
  number  = {6},
  year    = {2016}
}

@software{dujardin2026pytorchpointcloud,
  author  = {Arthur Dujardin},
  title   = {PyTorch PointCloud},
  year    = {2026},
  doi     = {10.5281/zenodo.22159632},
  url     = {https://github.com/arthurdjn/pytorch-pointcloud},
}