Model card for point-m2ae-base.scanobjectnn-objbg.renrui-zhang

A Point-M2AE point cloud classification model (multi-scale masked autoencoder). Trained on ScanObjectNN (OBJ_BG).

Model Details

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(
    "point-m2ae-base.scanobjectnn-objbg.renrui-zhang",
    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),
}
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, 768)

Citation

@inproceedings{zhang2022pointm2ae,
  title   = {Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training},
  author  = {Renrui Zhang and Ziyu Guo and Rongyao Fang and Bin Zhao and Dong Wang and Yu Qiao and Hongsheng Li and Peng Gao},
  booktitle = {NeurIPS},
  year    = {2022}
}

@inproceedings{uy2019scanobjectnn,
  title   = {Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data},
  author  = {Mikaela Angelina Uy and Quang-Hieu Pham and Binh-Son Hua and Duc Thanh Nguyen and Sai-Kit Yeung},
  booktitle = {ICCV},
  year    = {2019}
}

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