Update README.md and model.safetensors for randlanet.toronto3d.open3d

README.md

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+---
+license: mit
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- randlanet
+- segmentation
+datasets:
+- toronto3d
+model-index:
+- name: randlanet.toronto3d.open3d
+  results:
+  - task:
+      type: point-cloud-segmentation
+    dataset:
+      name: Toronto-3D
+      type: toronto3d
+    metrics:
+    - name: mIoU
+      type: mean_iou
+      value: 73.88
+    - name: OA
+      type: accuracy
+      value: 95.85
+---
+
+# Model card for randlanet.toronto3d.open3d
+
+A RandLA-Net point cloud semantic segmentation model (random sampling with local feature aggregation). Trained on Toronto-3D.
+
+## Model Details
+
+- **Model Type:** Point cloud semantic segmentation
+- **Model Stats:**
+  - Params (M): 5.0
+  - Input channels: 6
+  - Classes: 8
+  - Features: 32
+- **Dataset:** Toronto-3D
+- **Metrics:** mIoU 73.88, OA 95.85
+- **Paper:** [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://arxiv.org/abs/1911.11236)
+- **Converted from:** [isl-org/Open3D-ML](https://github.com/isl-org/Open3D-ML) (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(
+    "randlanet.toronto3d.open3d",
+    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,
+    "intensity": torch.rand(num_points, 1),
+    "gps_time": torch.zeros(num_points, 1),
+    "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"])
+```
+
+## 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, 32)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{hu2020randlanet,
+  title   = {RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds},
+  author  = {Qingyong Hu and Bo Yang and Linhai Xie and Stefano Rosa and Yulan Guo and Zhihua Wang and Niki Trigoni and Andrew Markham},
+  booktitle = {CVPR},
+  year    = {2020}
+}
+
+@inproceedings{tan2020toronto3d,
+  title   = {Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways},
+  author  = {Weikai Tan and Nannan Qin and Lingfei Ma and Ying Li and Jing Du and Guorong Cai and Ke Yang and Jonathan Li},
+  booktitle = {CVPR Workshops},
+  year    = {2020}
+}
+
+@software{dujardin2026pytorchpointcloud,
+  author  = {Arthur Dujardin},
+  title   = {PyTorch PointCloud},
+  year    = {2026},
+  doi     = {10.5281/zenodo.22159632},
+  url     = {https://github.com/arthurdjn/pytorch-pointcloud},
+}
+```

model.safetensors

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