MuGSM (Multi-Directional Greedy Stereo Matching)

Fig.1 Flow of the MuGSM
> Objects
To obtain depth image from stereo images, we use stereo matching algorithm.
For a long time, many stereo matching algorithms have been developed.
And the performance are improving. But algorithms have weak points.
Those are long computation time, memory consuming, and scan line problem.
To solve these problems is the objects and motives.
We proposed an algorithm, whose accuracy is as good as SGM.
But, the computation time is shorter, and memory usage is smaller.
> Multi-directional Greedy

Make disparity maps using Greedy matching along 8 or 16 directions.
Fig 2. Matching directions Fig 3. Range of disparity
Number of matching direction is 8 or 16. The relation between current disparity and next disparity.
> Consistency Check
Find reliable area from greedy results using consisteny check.
Fig 4. Results of each greedy matching through 8 matching directions
Red point and blue point at each result mean same position of the original image.
Fig 5. Consistency check
Red points : The rate of inlier is higher than Consistency_ratio.
Blue points : The rate of inlier is smaller than Consistency_ratio.
> Iterative Expansion
1) After first ‘Consistency check’ get the reliable area
2) Restart Multi-directional Greedy Matching using reliable area.
3) Consistency check for the result of process 2.
4) Only in the holes(unreliable area), Iterate processes(2~3) are executed.
Fig 6. results of 1st iteration, 2nd iteration, 3th iteration, 4th iteration (from left-top, clockwise)
> Result
Computation time is 2~3 times faster than SGM’s. Memory usage is about 33% comparing to SGM’s. Accuracy almost equal.
Table 1. Compare with other stereo algorithms
Table 2. Memory usage
Table 3. Computation time