<p>According to your question, I assume that you are working on your own LiDAR point cloud data rather than the public datasets.
Initially, I would suggest annotating the data with 3D bounding boxes on point cloud data.</p>
<p>As concerned about deep learning algorithms I would prefer understanding <a href="https://arxiv.org/abs/1803.06199" rel="nofollow noreferrer">Complex YOLO</a>, <a href="https://arxiv.org/abs/1711.06396" rel="nofollow noreferrer">VoxelNet</a>, and PointNet.
To understand the implementation PointNet algorithm you can refer to the <a href="https://keras.io/examples/vision/pointnet/" rel="nofollow noreferrer">Keras webpage</a> trained on the ModelNet10 dataset.</p>
How to train 3D point cloud data? Where should I beginn?
<p>I'm an automotive engineer student, and at the moment I'm working in a project for an autonomous bus at the university with 3D point clouds from a lidar sensor. My job here is to train the point cloud with deep learning algorithms. But I do not know exactly how to start. I found many sources on the internet. But it is also too diverse for me as a beginner, I do not know how to start first. Can someone give me some tips? Or good source for beginners. </p>

<p>Thank you in advance!</p>