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

Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding