Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction-Reference-Cited by-同舟云学术

Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction

Published:2019-07 Issue:4 Volume:33 Page:04019027
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Chen Jingdao¹,Kira Zsolt²^ORCID,Cho Yong K.³^ORCID

Affiliation:

1. Ph.D. Student, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, 777 Atlanta Dr. N.W., Atlanta, GA 30332-0355.

2. Adjunct Professor, School of Interactive Computing, Georgia Institute of Technology, 790 Atlanta Dr. N.W., Atlanta, GA 30332-0355. ORCID: .

3. Associate Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlanta Dr. N.W., Atlanta, GA 30332-0355 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000842

Reference41 articles.

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3. Armeni I. O. Sener A. R. Zamir H. Jiang I. Brilakis M. Fischer and S. Savarese. 2016. “3D semantic parsing of large-scale indoor spaces.” In Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 1534–1543. Piscataway NJ: IEEE.

4. Brock A. T. Lim J. M. Ritchie and N. Weston. 2016. “Generative and discriminative voxel modeling with convolutional neural networks.” In Proc. Neural Information Processing Conf. 1–9. San Diego: Neural Information Processing Systems Foundation.

5. Chang A. X. et al. 2015. “ShapeNet: An information-rich 3D model repository.” Preprint submitted December 9 2015. http://arxiv.org/abs/1512.03012.

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