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Tangent Convolutions for Dense Prediction in 3D

Cited 322 time in Web of Science Cited 412 time in Scopus
Authors

Tatarchenko, Maxim; Park, Jaesik; Koltun, Vladlen; Zhou, Qian-Yi

Issue Date
2018
Publisher
IEEE
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3887-3896
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.
ISSN
1063-6919
URI
https://hdl.handle.net/10371/201314
DOI
https://doi.org/10.1109/CVPR.2018.00409
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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