Upload pointrcnn.kitti.openpcdet

README.md

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+---
+license: apache-2.0
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- pointrcnn
+- object-detection
+datasets:
+- kitti
+---
+
+# Model card for pointrcnn.kitti.openpcdet
+
+A PointRCNN 3D object detection model (two-stage point-based proposal and refinement). Trained on KITTI.
+
+## Model Details
+
+- **Model Type:** 3D object detection
+- **Model Stats:**
+  - Params (M): 4.0
+  - Input channels: 4
+  - Classes: 3
+  - Features: 128
+- **Dataset:** KITTI
+- **Paper:** [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://arxiv.org/abs/1812.04244)
+- **Converted from:** [open-mmlab/OpenPCDet](https://github.com/open-mmlab/OpenPCDet) (Apache-2.0)
+- **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(
+    "pointrcnn.kitti.openpcdet",
+    task="detection",
+    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),
+    "intensity": torch.rand(num_points, 1),
+}
+data = info["transform"](sample)
+data = collate([data])
+
+with torch.no_grad():
+    out = 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"])  # 128 channels
+```
+
+## Citation
+
+```bibtex
+@inproceedings{shi2019pointrcnn,
+  title   = {PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud},
+  author  = {Shaoshuai Shi and Xiaogang Wang and Hongsheng Li},
+  booktitle = {CVPR},
+  year    = {2019}
+}
+```
+
+```bibtex
+@inproceedings{geiger2012kitti,
+  title     = {Are we ready for Autonomous Driving? The {KITTI} Vision Benchmark Suite},
+  author    = {Geiger, Andreas and Lenz, Philip and Urtasun, Raquel},
+  booktitle = {CVPR},
+  year      = {2012}
+}
+```

model.safetensors

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