27 July 2015 Dense surface reconstruction based on the fusion of monocular vision and three-dimensional flash light detection and ranging
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Abstract
A dense surface reconstruction approach based on the fusion of monocular vision and three-dimensional (3-D) flash light detection and ranging (LIDAR) is proposed. The texture and geometry information can be obtained simultaneously and quickly for stationary or moving targets with the proposed method. Primarily, our 2-D/3-D fusion imaging system including cameras calibration and an intensity-range image registration algorithm is designed. Subsequently, the adaptive block intensity-range Markov random field (MRF) with optimizing weights is presented to improve the sparse range data from 3-D flash LIDAR. Then the energy function is minimized quickly by conjugate gradient algorithm for each neighborhood system instead of the whole MRF. Finally, the experiments with standard depth datasets and real 2-D/3-D images demonstrate the validity and capability of the proposed scheme.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Gangtao Hao, Gangtao Hao, Xiaoping Du, Xiaoping Du, Ji-Guang Zhao, Ji-Guang Zhao, Hang Chen, Hang Chen, Jianjun Song, Jianjun Song, Yishuo Song, Yishuo Song, } "Dense surface reconstruction based on the fusion of monocular vision and three-dimensional flash light detection and ranging," Optical Engineering 54(7), 073113 (27 July 2015). https://doi.org/10.1117/1.OE.54.7.073113 . Submission:
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