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+# Quick Demo
+
+Here we provide a quick demo to test a pretrained model on the custom point cloud data and visualize the predicted results. 
+
+We suppose you already followed the [INSTALL.md](INSTALL.md) to install the `OpenPCDet` repo successfully. 
+
+1. Download the provided pretrained models as shown in the [README.md](../README.md). 
+
+2. Make sure you have already installed the [`Open3D`](https://github.com/isl-org/Open3D) (faster) or `mayavi` visualization tools. 
+If not, you could install it as follows:
+   ```
+   pip install open3d
+   # or 
+   pip install mayavi
+   ```
+
+3. Prepare your custom point cloud data (skip this step if you use the original KITTI data). 
+   * You need to transform the coordinate of your custom point cloud to 
+the unified normative coordinate of `OpenPCDet`, that is, x-axis points towards to front direction, 
+y-axis points towards to the left direction, and z-axis points towards to the top direction.
+   * (Optional) the z-axis origin of your point cloud coordinate should be about 1.6m above the ground surface, 
+   since currently the provided models are trained on the KITTI dataset. 
+   * Set the intensity information, and save your transformed custom data to `numpy file`: 
+   ```python
+   # Transform your point cloud data
+   ...
+   
+   # Save it to the file. 
+   # The shape of points should be (num_points, 4), that is [x, y, z, intensity] (Only for KITTI dataset).  
+   # If you doesn't have the intensity information, just set them to zeros. 
+   # If you have the intensity information, you should normalize them to [0, 1].
+   points[:, 3] = 0 
+   np.save(`my_data.npy`, points) 
+   ```      
+  
+4. Run the demo with a pretrained model (e.g. PV-RCNN) and your custom point cloud data as follows:
+```shell
+python demo.py --cfg_file cfgs/kitti_models/pv_rcnn.yaml \
+    --ckpt pv_rcnn_8369.pth \
+    --data_path ${POINT_CLOUD_DATA}
+```
+Here `${POINT_CLOUD_DATA}` could be in any of the following format: 
+* Your transformed custom data with a single numpy file like `my_data.npy`.  
+* Your transformed custom data with a directory to test with multiple point cloud data.
+* The original KITTI `.bin` data within `data/kitti`, like `data/kitti/training/velodyne/000008.bin`.   
+
+Then you could see the predicted results with visualized point cloud as follows:
+
+<p align="center">
+  <img src="demo.png" width="99%">
+</p>