Classification frameworks comparison on 3D point clouds

We present a preliminary comparison study for various classification frameworks that includes several types of feature engineering. The presentation is this paper is based on our work in [10]. We demonstrate that providing context by augmenting each point in the data about its neighboring points can improve the performance of downstream learning algorithms. We also experiment with several dimension reduction strategies, ranging from Principal Component Analysis (PCA) to neural network based auto-encoders, and demonstrate how they affect classification performance in LiDAR point clouds. For example, we observe that combining feature engineering with a dimension reduction method such as PCA, there is an improvement in the accuracy of the classification with respect to doing a straightforward classification with the raw data. We test such classification strategies in 3D Lidar point clouds.