Sparse disparity map estimation

버전 1.0.1 (3.95 MB) 작성자: Victor Gonzalez

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

https://github.com/alx3416/Sparse_Disparity

팔로우

0.0

(0)

다운로드 수: 130

업데이트 날짜: 2023/3/11

GitHub에서 라이선스 보기

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

인용 양식

Victor Gonzalez (2024). Sparse disparity map estimation (https://github.com/alx3416/Sparse_Disparity), GitHub. 검색됨 2024/7/24.

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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개발 환경: R2019b

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GitHub 디폴트 브랜치를 사용하는 버전은 다운로드할 수 없음

버전	게시됨	릴리스 정보
1.0.1	2023/3/11	improved description
1.0.0	2020/5/21		다운로드

이 GitHub 애드온의 문제를 보거나 보고하려면 GitHub 리포지토리로 가십시오.

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} }