Abstract
We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on
tangent convolutions – a new construction for convolutional
networks on 3D data. In contrast to volumetric approaches,
our method operates directly on surface geometry. Crucially, the construction is applicable to unstructured point
clouds and other noisy real-world data. We show that tangent convolutions can be evaluated efficiently on large-scale
point clouds with millions of points. Using tangent convolutions, we design a deep fully-convolutional network for
semantic segmentation of 3D point clouds, and apply it to
challenging real-world datasets of indoor and outdoor 3D
environments. Experimental results show that the presented
approach outperforms other recent deep network constructions in detailed analysis of large 3D scenes.