1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
2. Adalsteinsson, D., Sethian, J.A.: A fast level set method for propagating interfaces. J. Comput. Phys. 118(2), 269–277 (1995)
3. Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1534–1543. IEEE Computer Society, Los Alamitos, CA, USA (2016).10.1109/CVPR.2016.170. https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.170
4. Bindu, V., Nair, K.R.: A fast narrow band level set formulation for shape extraction. In: The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), pp. 137–142. IEEE (2014)
5. Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.: Learning shape correspondence with anisotropic convolutional neural networks. In: D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, R. Garnett (eds.) Advances in Neural Information Processing Systems 29, pp. 3189–3197. Curran Associates, Inc. (2016). http://papers.nips.cc/paper/6045-learning-shape-correspondence-with-anisotropic-convolutional-neural-networks.pdf