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Relative importance of binocular disparity and motion parallax for depth estimation: A computer vision approach

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Relative importance of binocular disparity and motion parallax for depth estimation : A computer vision approach. / Mansour, Mostafa; Davidson, Pavel; Stepanov, Oleg; Piché, Robert.

julkaisussa: Remote Sensing, Vuosikerta 11, Nro 17, 1990, 2019.

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@article{d3ea642fce73421cae8790b5896f1963,
title = "Relative importance of binocular disparity and motion parallax for depth estimation: A computer vision approach",
abstract = "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.",
keywords = "Binocular disparity, Depth perception, Motion parallax, Proprioceptive sensors, Unscented Kalman filter",
author = "Mostafa Mansour and Pavel Davidson and Oleg Stepanov and Robert Pich{\'e}",
year = "2019",
doi = "10.3390/rs11171990",
language = "English",
volume = "11",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI",
number = "17",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Relative importance of binocular disparity and motion parallax for depth estimation

T2 - A computer vision approach

AU - Mansour, Mostafa

AU - Davidson, Pavel

AU - Stepanov, Oleg

AU - Piché, Robert

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Binocular disparity

KW - Depth perception

KW - Motion parallax

KW - Proprioceptive sensors

KW - Unscented Kalman filter

U2 - 10.3390/rs11171990

DO - 10.3390/rs11171990

M3 - Article

VL - 11

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 17

M1 - 1990

ER -