<p>There are two possible approaches: </p>

<ul>
<li><strong><em>Feature engineering</em></strong> (user-defined global and/or <a href="https://doi.org/10.5194/isprsannals-II-3-181-2014" rel="nofollow noreferrer">local features</a>) </li>
<li><strong><em>Feature learning</em></strong> (models such as <a href="https://arxiv.org/abs/1612.00593" rel="nofollow noreferrer">PointNet</a>, <a href="https://arxiv.org/abs/1706.02413" rel="nofollow noreferrer">PointNet++</a>, and <a href="https://doi.org/10.1109/IROS.2015.7353481" rel="nofollow noreferrer">VoxNet</a>)</li>
</ul>

<p>If the feature engineering approach is selected, you can train your model with several different classifiers, such as Multilayer Perceptron (MLP), Support Vector Machines (SVM), and Random Forest (RF), for instance. </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>