Upload point-bert-base.pretrain.xumin-yu

README.md

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+---
+license: mit
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- point-bert
+- self-supervised
+datasets:
+- shapenet
+---
+
+# Model card for point-bert-base.pretrain.xumin-yu
+
+A Point-BERT self-supervised pretraining model (masked point modeling transformer). Pretrained on ShapeNet-55.
+
+## Model Details
+
+- **Model Type:** Self-supervised pretraining
+- **Model Stats:**
+  - Params (M): 25.5
+- **Dataset:** ShapeNet-55
+- **Paper:** [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://arxiv.org/abs/2111.14819)
+- **Converted from:** [Julie-tang00/Point-BERT](https://github.com/Julie-tang00/Point-BERT) (MIT)
+- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)
+
+## Install
+
+```bash
+pip install torch-pointcloud
+```
+
+## Usage
+
+```python
+import torch
+import torch_pointcloud as tp
+from torch_pointcloud.utils.data import collate
+
+model, info = tp.create_model(
+    "point-bert-base.pretrain.xumin-yu",
+    task="base",
+    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 = collate([sample])
+
+with torch.no_grad():
+    out = model(data.get("x"), data["pos"], data["batch"])
+```
+
+## Citation
+
+```bibtex
+@inproceedings{yu2022pointbert,
+  title   = {Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling},
+  author  = {Xumin Yu and Lulu Tang and Yongming Rao and Tiejun Huang and Jie Zhou and Jiwen Lu},
+  booktitle = {CVPR},
+  year    = {2022}
+}
+```
+
+```bibtex
+@article{chang2015shapenet,
+  author  = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
+  title   = {{ShapeNet}: An Information-Rich {3D} Model Repository},
+  journal = {arXiv preprint arXiv:1512.03012},
+  year    = {2015},
+}
+```

model.safetensors

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