Object Disparity

TL;DR

This paper introduces a novel approach for 3D object distance detection by directly estimating object disparity, bypassing dense pixel disparity computation, and demonstrates its efficiency and robustness across various stereo systems.

Contribution

The paper proposes a new method for direct object disparity detection that is less sensitive to stereo system parameters and improves accuracy at object boundaries.

Findings

Comparable accuracy to pixel disparity ground truth on KITTI dataset.

Potential robustness to different stereo system parameters.

Efficient object disparity detection with a lightweight neural network.

Abstract

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.