Sparse disparity map estimation

Version 1.0.1 (3,95 Mo) par Victor Gonzalez

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

https://github.com/alx3416/Sparse_Disparity

Suivre

0.0

(0)

129 téléchargements

Mise à jour 11 mars 2023

Afficher la licence sur GitHub

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

Citation pour cette source

Victor Gonzalez (2024). Sparse disparity map estimation (https://github.com/alx3416/Sparse_Disparity), GitHub. Récupéré le avril 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.

Afficher d’autres styles

Compatibilité avec les versions de MATLAB

Créé avec R2019b

Compatible avec toutes les versions

Plateformes compatibles

Windows macOS Linux

Tags Ajouter des tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Les versions qui utilisent la branche GitHub par défaut ne peuvent pas être téléchargées

Version	Publié le	Notes de version
1.0.1	11 mars 2023	improved description
1.0.0	21 mai 2020		Télécharger

Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.

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} }