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Article

Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach

1
Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland
2
Department of Information and Navigation Systems, ITMO University, 197101 St. Petersburg, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 1990; https://doi.org/10.3390/rs11171990
Received: 4 July 2019 / Revised: 15 August 2019 / Accepted: 20 August 2019 / Published: 23 August 2019
Binocular disparity and motion parallax are the most important cues for depth estimation in human and computer vision. Here, we present an experimental study to evaluate the accuracy of these two cues in depth estimation to stationary objects in a static environment. Depth estimation via binocular disparity is most commonly implemented using stereo vision, which uses images from two or more cameras to triangulate and estimate distances. We use a commercial stereo camera mounted on a wheeled robot to create a depth map of the environment. The sequence of images obtained by one of these two cameras as well as the camera motion parameters serve as the input to our motion parallax-based depth estimation algorithm. The measured camera motion parameters include translational and angular velocities. Reference distance to the tracked features is provided by a LiDAR. Overall, our results show that at short distances stereo vision is more accurate, but at large distances the combination of parallax and camera motion provide better depth estimation. Therefore, by combining the two cues, one obtains depth estimation with greater range than is possible using either cue individually. View Full-Text
Keywords: binocular disparity; motion parallax; depth perception; proprioceptive sensors; unscented Kalman filter binocular disparity; motion parallax; depth perception; proprioceptive sensors; unscented Kalman filter
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MDPI and ACS Style

Mansour, M.; Davidson, P.; Stepanov, O.; Piché, R. Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach. Remote Sens. 2019, 11, 1990. https://doi.org/10.3390/rs11171990

AMA Style

Mansour M, Davidson P, Stepanov O, Piché R. Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach. Remote Sensing. 2019; 11(17):1990. https://doi.org/10.3390/rs11171990

Chicago/Turabian Style

Mansour, Mostafa, Pavel Davidson, Oleg Stepanov, and Robert Piché. 2019. "Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach" Remote Sensing 11, no. 17: 1990. https://doi.org/10.3390/rs11171990

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