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 to install the PDV repo successfully.

Download the provided pretrained models as shown in the README.md.
Make sure you have already installed the mayavi visualization tools. If not, you could install it as follows:
```
pip install mayavi
```
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 PDV, 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:
```
# 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)
```
Run the demo with a pretrained model (e.g. PV-RCNN) and your custom point cloud data as follows:

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:

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