Model card for recon.modelnet40.zekun-qi

A ReCon point cloud classification model (masked point transformer with cross-modal contrastive queries). Trained on ModelNet40.

Model Details

• Model Type: Point cloud classification
• Model Stats:
  • Params (M): 43.6
  • Classes: 40
  • Features: 1536
• Dataset: ModelNet40
• Metrics: OA 93.68, mAcc 90.73
• Paper: Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
• Converted from: qizekun/ReCon (MIT)
• Library: torch-pointcloud

Install

pip install torch-pointcloud

Usage

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

model, info = tp.create_model(
    "recon.modelnet40.zekun-qi",
    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"])

Feature extraction

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, 1536)

Citation

@inproceedings{qi2023recon,
  title     = {Contrast with Reconstruct: Contrastive {3D} Representation Learning Guided by Generative Pretraining},
  author    = {Qi, Zekun and Dong, Runpei and Fan, Guofan and Ge, Zheng and Zhang, Xiangyu and Ma, Kaisheng and Yi, Li},
  booktitle = {ICML},
  year      = {2023}
}

@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},
}