Binocular Structured Light Stereo Matching Approach for Dense Facial Disparity Map

Ma, Shaoping; Shen, Yujie; Qian, Junfen; Chen, Hui; Hao, Zhonghua; Yang, Lei

doi:10.1007/978-3-642-25832-9_56

Cited by 2 publications

(1 citation statement)

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“…Different projected patterns and disparity estimation methods are employed. Ma et al combined a color-coded pattern based on vertical stripes and the semi-global stereo matching (SGM) algorithm to obtain a dense facial disparity map [ 21 ]. An et al provided the comparative analysis of various structured lighting techniques with a view for facial reconstruction [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light

Yao

Xue

et al. 2017

Sensors

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Depth information has been used in many fields because of its low cost and easy availability, since the Microsoft Kinect was released. However, the Kinect and Kinect-like RGB-D sensors show limited performance in certain applications and place high demands on accuracy and robustness of depth information. In this paper, we propose a depth sensing system that contains a laser projector similar to that used in the Kinect, and two infrared cameras located on both sides of the laser projector, to obtain higher spatial resolution depth information. We apply the block-matching algorithm to estimate the disparity. To improve the spatial resolution, we reduce the size of matching blocks, but smaller matching blocks generate lower matching precision. To address this problem, we combine two matching modes (binocular mode and monocular mode) in the disparity estimation process. Experimental results show that our method can obtain higher spatial resolution depth without loss of the quality of the range image, compared with the Kinect. Furthermore, our algorithm is implemented on a low-cost hardware platform, and the system can support the resolution of 1280 × 960, and up to a speed of 60 frames per second, for depth image sequences.

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Section: Introductionmentioning

confidence: 99%