I am currently working on a project that involves extracting features from the LiDAR point cloud data from the KITTI dataset. Despite exploring various resources and methods, I have not been able to find a solid/easy-to-implement solution. I am particularly interested in:

• Using pre-trained deep learning models for this task, such as *PointNet*, *PointNet++*, or other models available.
• Using `mmdetection3d`, but other libraries/solutions will also work. Although there is `extract_feat` function in `mmdetection3d`, I couldn't find any documentation on how to implement that.

For example, to extract feature representation from the point cloud data (.bin files), I was expecting a code similar to this using openmmlab. However, I got an error: `batch_inputs_dict['voxels']` does not exist. This indicates I may need to voxelize the data before passing it to the `extract_feat` function, but I can't find a workaround for that.

```python

from mmdet3d.apis import init_model, inference_detector

config_file = 'xyz.py'

checkpoint_file = 'xyz.pth'

point_cloud_path = 'abc.bin'

model = init_model(config_file, checkpoint_file, device='cuda:0')

result, data = inference_detector(model, point_cloud_path)

pcd_features = model.extract_feat(data)

```

I am reaching out to this community to seek any pointers to resources, code examples, or detailed explanations. Thanks