3D Point Cloud Semantic Segmentation Through Functional Data Analysis

Abstract

Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a number of handcrafted features representing the local geometry around it are calculated at different scales, that is, varying the spatial extension of the local analysis. Calculating the scales at small intervals allows each feature to be accurately approximated using a smooth function and, for the problem of semantic segmentation, to be tackled using functional data analysis. We also present a step-wise method to select the optimal features to include in the model based on the calculation of the distance correlation between each feature and the response variable. The algorithm showed promising results when applied to simulated data. When applied to the semantic segmentation of a point cloud of a forested plot, the results proved better than when using a standard multiscale semantic segmentation method. The comparison with two popular deep learning models showed that our proposal requires smaller training samples sizes and that it can compete with these methods in terms of prediction.