A multi-scale fully convolutional network for semantic labeling of 3D point clouds

Author:

Yousefhussien Mohammed,Kelbe David J.,Ientilucci Emmett J.,Salvaggio Carl

Funder

U.S. Department of Energy

Office of Science

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference53 articles.

1. Audebert, N., Saux, B.L., Lefèvre, S., 21–23 November 2016. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks. In: Proceedings of the Asian Conference on Computer Vision (ACCV). Taipei, Taiwan.

2. Axelsson, P., 2000. DEM generation from laser scanner data using adaptive TIN models. In: ISPRS International Archives of Photogrammetry and Remote Sensing, vol. XXXIII-Part B4/1. pp. 111–118.

3. Segnet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017

4. Bai, S., Bai, X., Zhou, Z., Zhang, Z., Jan Latecki, L., 2016. GIFT: a real-time and scalable 3D shape search engine. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5023–5032.

5. Blomley, R., Jutzi, B., Weinmann, M., September 2016. 3D semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction. In: Proceedings of the International Conference on Geographic Object-Based Image Analysis (GEOBIA). Enschede, The Netherlands, pp. 1–8.

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