Small test bed for some basic stereo algorithms
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README.md

census test

Testing of Census-like stereo algorithms. Mostly a space for experimenting with small concepts and ideas. As such, the code has basically no comments and poor interfaces for most of it's functionality.

Algorithms Implemented

  • SSD Block Matching (with an optional Sobel prefilter), for Left + Right frame perspective, with quadratic subpixel estimation
  • Census Matching (24 bit, 7x7 descriptor) + Block Matching, for Left + Right frame perspective, with linear subpixel estimation
  • Census (left frame only) with thresholds from the Intel RealSense R200. Implemented as documented in Intel's released documentation. Optionally includes a domain-transform on the cost volume. As published these thresholds can be effective in removing spurious matches.
  • Semiglobal Matching (5 paths), with SAD + Census cost metrics, subpixel matching, R200 thresholds, discontinuity scaling for SGM, naive hole filling and bilateral filter window weights. Only on the left-frame perspective.

Dependencies

  • C++14 compliant compiler
  • GLFW3 for visualization
  • librealsense for librealsense executable

Building

Windows

  • Pull down a version of this repository
  • A Visual Studio 2015 Solution is included in msvc/
  • CensusMatching is the primary project of interest
  • GLFW3 is installed via a NuGet Package, so Visual Studio should pull it down automatically

Linux

  • Pull down a version of this repository
  • Type make in the top-level-centest. This'll build everything and generate binaries in the Makefile directory.
  • Makefile can be modified to build a debug version of the executable

Running

  • Run via ./centest <json>
  • An example would be ./centest github_census/github_census.json
  • The example code expects to have a converted version of Middlebury's v3 dataset
  • The conversion routine is on github_census/generate_middlebury.py. This is a python script that requires Pillow and Imagemagick.
  • This program also supports passing valid json on the command line, instead of a file.
  • Output results are in the current working directory, to an 32-bit float disparity image PFM, and a confidence map in 32-bit floating point.
  • To visualize the pfm files, a vis_pfm script is provided ./vis_pfm depth.pfm [conf.pfm] with an optional conf.pfm argument, which generates an out.png file the execution directory with a histogram colored depthmap.

License

Mozilla Public License 2.0. More information is available on the Wikpedia article on MPL or MPL's official FAQ.