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Filtering Disparity Map

asked 2019-03-10 21:54:56 -0600

WannabeEngr gravatar image

updated 2019-03-11 02:26:13 -0600

LBerger gravatar image

Any ideas on how to filter out these fluctuations? because it is messing up my generated disparity value, and I can't integrate my distance estimation algorithm.

My pre-processing was jus convert the input image into grayscale, and I only adjust WLS filter in its lambda & Sigma.

Any ideas on how to filter my depth map?? image description

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i might have no idea, but can you show, how you use the WLS filtering ? (maybe there's something more to tweak !)

berak gravatar imageberak ( 2019-03-11 02:22:38 -0600 )edit

WLS FILTER Parameters

lmbda = 70000
sigma = 2.0

wls_filter = cv2.ximgproc.createDisparityWLSFilter(matcher_left=stereo)


Using the WLS filter

filteredImg= wls_filter.filter(dispR,grayL,None,dispL)
filteredImg = cv2.normalize(src=filteredImg, dst=filteredImg, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX);
filteredImg = np.uint8(filteredImg)
cv2.imshow('filteredImg', filteredImg)
WannabeEngr gravatar imageWannabeEngr ( 2019-03-14 01:30:08 -0600 )edit

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answered 2019-03-11 10:17:38 -0600

HYPEREGO gravatar image

updated 2019-03-11 10:55:29 -0600

The first question is: it have to be real-time or not? The usual steps are left-right consistency check (it help you in refine occlusion) and apply a filter. You can try one of the proposed filters in OpenCV, generally a bilateral filter do the trick (convert the depth map to a disparity map!). Better things can be the use of a Guided filter (that is pretty easy to implement) and usually achieve good result even in real time application.

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Yes, it is in real time and I want to make it mobile, my project is to aid the blind through stereo vision & these fluctuations gives me a hard time. Okay i will try bilateral filter. Thanks!

WannabeEngr gravatar imageWannabeEngr ( 2019-03-14 01:27:25 -0600 )edit

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Asked: 2019-03-10 21:54:56 -0600

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Last updated: Mar 11 '19