This is the implementation of Embedded real-time stereo estimation via Semi-Global Matching on the GPU, D. Hernandez-Juarez et al, ICCS 2016.
Performance obtained measured in Frames Per Second (FPS):
| 2 paths | 4 paths | 8 paths | |
|---|---|---|---|
| NVIDIA Tegra X1 | 81 | 42 | 19 |
| NVIDIA Titan X | 886 | 475 | 237 |
Results for example image (left and right Images):
Results for example image (Output):
Simply use CMake and target the output directory as "build". In command line this would be (from the project root folder):
mkdir build
cd build
cmake ..
make
Type: ./sgm dir p1 p2
The arguments p1 and p2 are semi-global matching parameters, for more information read the SGM paper.
dir is the name of the directory which needs this format:
dir
---- left (images taken from the left camera)
---- right (right camera)
---- disparities (results will be here)
Embedded real-time stereo estimation via Semi-Global Matching on the GPU D. Hernandez-Juarez, A. Chacón, A. Espinosa, D. Vázquez, J. C. Moure, and A. M. López ICCS2016 – International Conference on Computational Science 2016
- OpenCV
- CUDA
- CMake
- Maximum disparity has to be 128
- Image width and height must be a divisible by 4
If you use this code for your research, please kindly cite:
@inproceedings{sgm_gpu_iccs2016,
author = {Daniel Hernandez-Juarez and
Alejandro Chac{\'{o}}n and
Antonio Espinosa and
David V{\'{a}}zquez and
Juan Carlos Moure and
Antonio M. L{\'{o}}pez},
title = {Embedded Real-time Stereo Estimation via Semi-Global Matching on the
{GPU}},
booktitle = {International Conference on Computational Science 2016, {ICCS} 2016,
6-8 June 2016, San Diego, California, {USA}},
pages = {143--153},
year = {2016},
crossref = {DBLP:conf/iccS/2016},
url = {http://dx.doi.org/10.1016/j.procs.2016.05.305},
doi = {10.1016/j.procs.2016.05.305},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/iccS/JuarezCEVML16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}