Sparse disparity map estimation

Version 1.0.1 (3.95 MB) by Victor Gonzalez

MATLAB function to estimate disparity maps from stereo pairs images with GPU capability

https://github.com/alx3416/Sparse_Disparity

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Updated 11 Mar 2023

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Sparse disparity map estimation from stereo-pair images previously rectified, DEMO.m contains examples with some stereo pairs from Middlebury Stereo Evaluation, and KITTI 2015 disparity challenge.

Occlusions and missing disparities are labeled as NAN values, to add the possibility to generate a dense disparity map with other frameworks

For an RGB or grayscale stereo pair images, first, the magnitude gradient is obtained, then a Census transform is performed according to the selected method; for SAD and NCC, Census transform generates an image with a range of 2^24 possible values, for Hamming, Jaccard, and Mutual Information a binary Census Transform generates a volume with binary vectors for each pixel. Then a matching stage is performed with the selected method and finally, a match and disparity consistency check is performed.

The function uses GPU when is available.

See DEMO.m, for examples.

If this work is helpful to you, please cite this work.
doi.org/10.1007/s11760-017-1150-3

Cite As

Victor Gonzalez (2024). Sparse disparity map estimation (https://github.com/alx3416/Sparse_Disparity), GitHub. Retrieved November 26, 2024.

Gonzalez-Huitron, Victor, et al. “Parallel Framework for Dense Disparity Map Estimation Using Hamming Distance.” Signal, Image and Video Processing, vol. 12, no. 2, Springer Science and Business Media LLC, Aug. 2017, pp. 231–38, doi:10.1007/s11760-017-1150-3.

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MATLAB Release Compatibility

Created with R2019b

Compatible with any release

Platform Compatibility

Windows macOS Linux

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Versions that use the GitHub default branch cannot be downloaded

Version	Published	Release Notes
1.0.1	11 Mar 2023	improved description
1.0.0	21 May 2020		Download

To view or report issues in this GitHub add-on, visit the GitHub Repository.

MLA	Gonzalez-Huitron, Victor, et al. “Parallel Framework for Dense Disparity Map Estimation Using Hamming Distance.” Signal, Image and Video Processing, vol. 12, no. 2, Springer Science and Business Media LLC, Aug. 2017, pp. 231–38, doi:10.1007/s11760-017-1150-3.
APA	Gonzalez-Huitron, V., Ponomaryov, V., Ramos-Diaz, E., & Sadovnychiy, S. (2017). Parallel framework for dense disparity map estimation using Hamming distance. Signal, Image and Video Processing, 12(2), 231–238. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs11760-017-1150-3
BibTeX	@article{Gonzalez_Huitron_2017, doi = {10.1007/s11760-017-1150-3}, url = {https://doi.org/10.1007%2Fs11760-017-1150-3}, year = 2017, month = {aug}, publisher = {Springer Science and Business Media {LLC}}, volume = {12}, number = {2}, pages = {231--238}, author = {Victor Gonzalez-Huitron and Volodymyr Ponomaryov and Eduardo Ramos-Diaz and Sergiy Sadovnychiy}, title = {Parallel framework for dense disparity map estimation using Hamming distance}, journal = {Signal, Image and Video Processing} }