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 categories: 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. | "The wide range of modern dense matching algorithms can be assigned to two big categories: 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. | ||
=== Local Method === | |||
==== Block Method ==== | |||
=== Global Method === | |||
==== Energy Minimization ==== | |||
Markov Random Fields have enable a statistical method of building out the disparity method | |||
==== Belief Propagation ==== | |||
==== Graph cuts Propagation ===== | |||
=== Other Methods === | |||
==== CNN ==== | |||
MC_CNN is an algorithm that started the movement | |||
==== Dynamic Programming ==== | |||
Scan line based traversal with a 2D dp matrix of pixel and disparity | |||
== Results == | == Results == | ||
Revision as of 06:08, 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
Computer vision has seen explosive growth during the turn of the century; stereo depth in particular has seen its fair share of research due to its application in panorama mosaic, improved face tracking, facial recognition, autonomous driving, high resolution geometric sampling and many others. The result of this high interest is a plethora of algorithms and papers describing various ways of estimating depth from two images.
A typical stereo algorithm consists of four steps: matching cost computation, cost aggregation, optimization and disparity refinement [1]. Stereo correspondence, the process of matching pixels between stereo images, is essentially an optimization problem. The local or global categories for stereo depth estimation are determined by the way an algorithm solves the stereo correspondence problem.
Methods
"The wide range of modern dense matching algorithms can be assigned to two big categories: 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.
Local Method
Block Method
Global Method
Energy Minimization
Markov Random Fields have enable a statistical method of building out the disparity method
Belief Propagation
Graph cuts Propagation =
Other Methods
CNN
MC_CNN is an algorithm that started the movement
Dynamic Programming
Scan line based traversal with a 2D dp matrix of pixel and disparity