  Semantic segmentation of raw 3D point clouds is an essential component in 3D
scene analysis, but it poses several challenges, primarily due to the
non-Euclidean nature of 3D point clouds. Although, several deep learning based
approaches have been proposed to address this task, but almost all of them
emphasized on using the latent (global) feature representations from
traditional convolutional neural networks (CNN), resulting in severe loss of
spatial information, thus failing to model the geometry of the underlying 3D
objects, that plays an important role in remote sensing 3D scenes. In this
letter, we have proposed an alternative approach to overcome the limitations of
CNN based approaches by encoding the spatial features of raw 3D point clouds
into undirected symmetrical graph models. These encodings are then combined
with a high-dimensional feature vector extracted from a traditional CNN into a
localized graph convolution operator that outputs the required 3D segmentation
map. We have performed experiments on two standard benchmark datasets
(including an outdoor aerial remote sensing dataset and an indoor synthetic
dataset). The proposed method achieves on par state-of-the-art accuracy with
improved training time and model stability thus indicating strong potential for
further research towards a generalized state-of-the-art method for 3D scene
understanding.
