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GPU Accelerated Robust Scene Reconstruction

Cited 5 time in Web of Science Cited 9 time in Scopus
Authors

Dong, Wei; Park, Jaesik; Yang, Yi; Kaess, Michael

Issue Date
2019
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE International Conference on Intelligent Robots and Systems, pp.7863-7870
Abstract
We propose a fast and accurate 3D reconstruction system that takes a sequence of RGB-D frames and produces a globally consistent camera trajectory and a dense 3D geometry. We redesign core modules of a state-of-the-art offline reconstruction pipeline to maximally exploit the power of GPU. We introduce GPU accelerated core modules that include RGBD odometry, geometric feature extraction and matching, point cloud registration, volumetric integration, and mesh extraction. Therefore, while being able to reproduce the results of the high-fidelity offline reconstruction system, our system runs more than 10 times faster on average. Nearly 10Hz can be achieved in medium size indoor scenes, making our offline system even comparable to online Simultaneous Localization and Mapping (SLAM) systems in terms of the speed. Experimental results show that our system produces more accurate results than several state-of-the-art online systems. The system is open source at https://github.com/theNded/Open3D.
ISSN
2153-0858
URI
https://hdl.handle.net/10371/201312
DOI
https://doi.org/10.1109/IROS40897.2019.8967693
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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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