A Hybrid Semantic Point Cloud Classification-Segmentation Framework Based on Geometric Features and Semantic Rules

In this paper, we focus on semantic point cloud classification taking into account standard failure cases reported in a variety of investigations. We present a hybrid two-step framework integrating classification, segmentation and semantic rules in a common end-to-end processing pipeline from irregularly distributed points to semantically labelled point clouds. The first step of our framework consists of a point-wise semantic point cloud classification based on rather intuitive, handcrafted, low-level geometric features extracted from local neighbourhoods of locally adaptive size. The second step of our framework consists of refining the point-wise classification results by considering semantic rules applied to geometric features extracted on the basis of an over-segmentation of the derived class-wise point clouds. We demonstrate the performance of our framework on a standard benchmark dataset for which we obtain a semantic labelling of high accuracy and high plausibility.