  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.
