Stereo Depth Estimation Algorithms: Difference between revisions
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== Methods == | == Methods == | ||
"The wide range of modern dense matching algorithms can be assigned to two big category: local methods, which evaluate the correspondences one point at a time, not considering neighboring points/measures, and global methods where some constraint on the regularity of results are enforced in the estimation"[2]. However after the advent of the MC-CNN paper[3] a whole host of learning network algorithms have dominated the KITT data set. | |||
== Results == | == Results == | ||
Revision as of 06:03, 12 December 2017
Introduction
The brain is a fantastic supercomputer with unmatched 3D object recognition and depth estimation. The ocular system has over 120 million rods and 6 million cones which send signals that are funneled into of our visual cortex resulting in our capability to see. The massive amounts of data and processing power of our brain, make it a fascinating phenomenon. Stereo depth is a major part of this machinery and in this paper, we delve into the topic by investigating existing computer algorithms. Depth extraction from stereo estimation boils down to Triangulation. The paper seeks to explain three primary approaches and details surrounding stereo depth extraction.
Background
Methods
"The wide range of modern dense matching algorithms can be assigned to two big category: local methods, which evaluate the correspondences one point at a time, not considering neighboring points/measures, and global methods where some constraint on the regularity of results are enforced in the estimation"[2]. However after the advent of the MC-CNN paper[3] a whole host of learning network algorithms have dominated the KITT data set.