  Most of stereo vision works are focusing on computing the dense pixel
disparity of a given pair of left and right images. A camera pair usually
required lens undistortion and stereo calibration to provide an undistorted
epipolar line calibrated image pair for accurate dense pixel disparity
computation. Due to noise, object occlusion, repetitive or lack of texture and
limitation of matching algorithms, the pixel disparity accuracy usually suffers
the most at those object boundary areas. Although statistically the total
number of pixel disparity errors might be low (under 2% according to the Kitti
Vision Benchmark of current top ranking algorithms), the percentage of these
disparity errors at object boundaries are very high. This renders the
subsequence 3D object distance detection with much lower accuracy than desired.
This paper proposed a different approach for solving a 3D object distance
detection by detecting object disparity directly without going through a dense
pixel disparity computation. An example squeezenet Object Disparity-SSD
(OD-SSD) was constructed to demonstrate an efficient object disparity detection
with comparable accuracy compared with Kitti dataset pixel disparity ground
truth. Further training and testing results with mixed image dataset captured
by several different stereo systems may suggest that an OD-SSD might be
agnostic to stereo system parameters such as a baseline, FOV, lens distortion,
even left/right camera epipolar line misalignment.

Computer Vision and Pattern Recognition;Artificial Intelligence