Stereo matching is the problem of finding correspondences between two images that are taken simultaneously from two cameras that are mounted so that they are parallel and separated along their x-axis. The output of stereo matching is a disparity image that, for every pixel in the left image (x), indicates how many pixels to the left its correspondence (x’) is in the right image, giving the disparity (x-x’).
The primary objective of this project is to address the stereo disparity problem by integrating the Normalized Cross-Correlation (NCC) algorithm with the Semi-Global Matching (SGM) algorithm. In addition to this integration, this project focuses on optimising the performance of these algorithms.
One aspect of optimisation involves re-formulating the NCC function for improved efficiency. Furthermore, I implement caching mechanisms to store reused data, thereby reducing computational overhead. For accelerating the SGM algorithm, I employ tensor operations in NumPy rather than conventional pixel-by-pixel iteration. This approach significantly enhances the computational efficiency of the algorithm.
| Left Image | Right Image | |
|---|---|---|
| Original |
|
|
| NCC Disparity |  |
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| SGM Disparity |  |
| Standard NCC | Accelerated NCC | |
|---|---|---|
| Runtime | 517.14 (s) | 255.15 (s) |
The main code is in semi_global_match.ipynb
The project uses python==3.12.2, and the dependencies can be installed by running:
pip install -r requirements.txt
- Yu Hsien Chiang, Thomas Chen