Robust and Efficient CPU-Based RGB-D Scene Reconstruction
1
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3652; https://doi.org/10.3390/s18113652
Received: 29 September 2018
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Revised: 25 October 2018
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Accepted: 25 October 2018
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Published: 28 October 2018
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
3D scene reconstruction is an important topic in computer vision. A complete scene is reconstructed from views acquired along the camera trajectory, each view containing a small part of the scene. Tracking in textureless scenes is well known to be a Gordian knot of camera tracking, and how to obtain accurate 3D models quickly is a major challenge for existing systems. For the application of robotics, we propose a robust CPU-based approach to reconstruct indoor scenes efficiently with a consumer RGB-D camera. The proposed approach bridges feature-based camera tracking and volumetric-based data integration together and has a good reconstruction performance in terms of both robustness and efficiency. The key points in our approach include: (i) a robust and fast camera tracking method combining points and edges, which improves tracking stability in textureless scenes; (ii) an efficient data fusion strategy to select camera views and integrate RGB-D images on multiple scales, which enhances the efficiency of volumetric integration; (iii) a novel RGB-D scene reconstruction system, which can be quickly implemented on a standard CPU. Experimental results demonstrate that our approach reconstructs scenes with higher robustness and efficiency compared to state-of-the-art reconstruction systems.
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Keywords:
3D reconstruction; camera tracking; volumetric integration; simultaneous localization and mapping (SLAM)
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MDPI and ACS Style
Li, J.; Gao, W.; Li, H.; Tang, F.; Wu, Y. Robust and Efficient CPU-Based RGB-D Scene Reconstruction. Sensors 2018, 18, 3652. https://doi.org/10.3390/s18113652
AMA Style
Li J, Gao W, Li H, Tang F, Wu Y. Robust and Efficient CPU-Based RGB-D Scene Reconstruction. Sensors. 2018; 18(11):3652. https://doi.org/10.3390/s18113652
Chicago/Turabian StyleLi, Jianwei, Wei Gao, Heping Li, Fulin Tang, and Yihong Wu. 2018. "Robust and Efficient CPU-Based RGB-D Scene Reconstruction" Sensors 18, no. 11: 3652. https://doi.org/10.3390/s18113652
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