Stereo disparity optimization with depth change constraint based on a continuous video

Three-dimensional reconstruction based on stereo vision technology is an important research direction in the field of computer vision, and has a wide range of applications in industrial measurement, medical image reconstruction, cultural relic preservation, robot navigation, virtual reality and other fields. However, the threedimensional reconstruction of moving objects usually has poor accuracy, low efficiency and poor visualization effect due to the image noise, motion blur, complex and time-consuming calculation etc. In this article, a disparity optimization method based on depth change constraint is proposed, which utilizes the correlation of the adjacent frames in the continuous video sequence to eliminate mismatches and correct the wrong disparity values by introducing a depth change constraint threshold. The experiments on the video images which are taken by a binocular stereo vision system demonstrate that our method of removing incorrect matches bears satisfactory results and it can greatly improve the effect of the three-dimensional reconstruction of the moving objects.

Automated summary

Stereo vision technology is crucial for three-dimensional reconstruction, but challenges exist in reconstructing moving objects due to image noise and motion blur. This article proposes a disparity optimization method based on depth change constraint, which improves reconstruction accuracy by eliminating mismatches and correcting disparity values.