Tangent Convolutions for Dense Prediction in 3D


We present an approach to semantic scene analysis us- ing 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. Cru- cially, the construction is applicable to unstructured point clouds and other noisy real-world data. We show that tan- gent convolutions can be evaluated efficiently on large-scale point clouds with millions of points. Using tangent convo- lutions, 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 construc- tions in detailed analysis of large 3D scenes.

In IEEE Conference on Computer Vision and Pattern Recognition 2018