Upload pointnet2.s3dis-area4.openpoints

README.md

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+---
+license: mit
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- pointnet2
+- segmentation
+datasets:
+- s3dis
+---
+
+# Model card for pointnet2.s3dis-area4.openpoints
+
+A PointNet++ point cloud segmentation model (hierarchical set abstraction). Trained on S3DIS (Area 4).
+
+## Model Details
+
+- **Model Type:** Point cloud semantic segmentation
+- **Model Stats:**
+  - Params (M): 1.0
+  - Input channels: 4
+  - Classes: 13
+  - Features: 128
+- **Dataset:** S3DIS (Area 4)
+- **Paper:** [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413)
+- **Converted from:** [guochengqian/PointNeXt](https://github.com/guochengqian/PointNeXt) (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(
+    "pointnet2.s3dis-area4.openpoints",
+    task="segmentation",
+    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),
+    "color": torch.rand(num_points, 3) * 255,
+}
+data = info["transform"](sample)
+data = collate([data])
+
+with torch.no_grad():
+    logits = model(data.get("x"), data["pos"], data["batch"])
+```
+
+## Feature extraction
+
+```python
+with torch.no_grad():
+    features = model.forward_features(data.get("x"), data["pos"], data["batch"])
+
+model.reset_classifier(num_classes=0)
+with torch.no_grad():
+    features = model(data.get("x"), data["pos"], data["batch"])  # (N, 128)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{qi2017pointnet2,
+  title   = {PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space},
+  author  = {Charles R. Qi and Li Yi and Hao Su and Leonidas J. Guibas},
+  booktitle = {NeurIPS},
+  year    = {2017}
+}
+```
+
+```bibtex
+@inproceedings{armeni2016s3dis,
+  title     = {{3D} Semantic Parsing of Large-Scale Indoor Spaces},
+  author    = {Armeni, Iro and Sener, Ozan and Zamir, Amir R. and Jiang, Helen and Brilakis, Ioannis and Fischer, Martin and Savarese, Silvio},
+  booktitle = {CVPR},
+  year      = {2016}
+}
+```

model.safetensors

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