Dynamic-Scale Graph Convolutional Network for Semantic Segmentation of 3D Point Cloud

Abstract

Semantic segmentation of 3D point cloud data is a crucial step for various applications such as autonomous driving. Conventional methods for semantic segmentation of point cloud heavily rely on numerous hand-crafted features often derived from the 3D covariance matrix, which are typically time-consuming. In this work, a dynamic scale graph convolutional neural network is proposed to perform semantic segmentation on 3D point cloud without relying on extensive exploitation of hand-crafted features. Using only spatial coordinates, backscattered intensity and spectral information derived from aerial images, our proposed method aims at modeling multi-scale local structural information by combining dynamic scale sampling and multi-scale neighbor graphs. Sampling and neighbor graph construction are both implemented on the fly such that no preprocessing or data augmentation is needed before training. Evaluated using the ISPRS 3D Semantic Labeling Contest, our approach has achieved the performance on par with the current state-of-the-art in terms of average f1-score without hand-crafted features and less computational overhead.