Review on Stereo Vision Based Depth Estimation

Abstract

Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The fundamental test of sound system vision is to create exact difference map. Sound system vision calculations for the most part perform four stages: first, coordinating cost calculation; second, cost collection; third, dissimilarity calculation or enhancement; and fourth, divergence refinement. Sound system coordinating issues are likewise examined. An enormous number of calculations have been produced for sound system vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.

Stereo Matching Based on Features of Image Patch

“…As a popular research topic for decades, traditional stereo matching methods comprise four steps: calculation of matching cost, aggregation of matching cost, optimization, and disparity refinement [3]. These methods usually calculate the similarity between pixels by designing an energy function.…”