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

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Details

Original languageEnglish
Article number1990
JournalRemote Sensing
Volume11
Issue number17
DOIs
Publication statusPublished - 2019
Publication typeA1 Journal article-refereed

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.

ASJC Scopus subject areas

Keywords

  • Binocular disparity, Depth perception, Motion parallax, Proprioceptive sensors, Unscented Kalman filter

Publication forum classification

Field of science, Statistics Finland