OK, it seems lots of people are having problems with the ARROYO images. Therefore, feel free to optionally substitute the TREES images in place of the ARROYO images.

Note: this is optional, so if you've already completed everything using the ARROYO images, don't sweat it.

-Neil

C gets runtimes around a second if you optimize a bit.

Edited by author 03-05-2006 09:37 PM
Yeah, I think the blotchy occlusions are normal. If you work really hard at optimizing your code, rewriting some of the matlab built-in functions, and pre-processing, etc, you can get runtimes well under a minute within matlab. (~15 seconds)

Regarding Part C, when using an occlusion penalty of 0.1 and a window size of 9, I get really splotchy occlusions, 10s of pixels thick. I understand a larger window is supposed to blur my results, but is this normal? Are the occlusions supposed to thicken or disappear?


BTW, for anyone doing the C++ version of this project, it seems WAY faster. Times are under a minute for 9x9 windows.

.01, but I understand some tuning is necessary.

I get about 30 minutes for each graph in Part 3. What occlusion_penalty are you using to get results? The results that I get are filled with occlusions.

What sort of runtimes are people getting for part C? I'm trying my best to optimize my code but SSD runs are still taking around 30 minutes using reasonable disparity limits and a 5x5 window...

I don't know about these "arroyo" images.. I think the difference in lighting conditions really messes things up here in Part B. Maybe that's the point of the assignment, to see how lighting conditions can foul up disparity matching?

Anyway, regarding the equalization: if you crop the arroyo images to remove the black bars from rectification, then do a histeq() equalization, the lighting conditions match a little better between the images (not perfect, but better). This also seems to reduce excess occlusions (using penalty 0.01).

I tried stereo2d on the "trees" and "corridor" images, which have notably better lighting than "arroyo." The results also look better, suggesting that the difference in lighting is a major problem here.

Yeah, the left image is darker... should have noticed that when choosing the stereo pair. Anyway, it might work somewhat better to scale the image intensity of the left image like you say..

Hi there,

I find a problem in Part C, ARROYO. The left image is obviously darker than the right one. If we directly manipulate the intensities of pixels, I think we might get lots of false "matching" and/or false "occlusion". I guess a reasonable way to fix this problem is that we adjust one of them to be more like the other, in terms of "average" brightness. Then, apply our algorithms to the "normalized" image pair.

Is this idea OK?

Thanks~

Ryan: Enforcing the uniqueness constraint is relatively straightforward... if you're storing the best path correctly, this should just boil down to how you do the backtracking. As for part C, you will need to adjust the occlusion penalty (at least for the unnormalized versions). Try some different values and report the value that works best.

Bhavjit: Yes, it does depend on the occlusion penalty. I'm using an occlusion penalty of .01.

Ryan: It could be that you are calculating the Sum of the difference of the 5*5 matrixes and then squaring it. It is the Sum of the squared(.^2) differences.

Does any one know a good occlusion penalty for the other 2 cost fuctions(SAD and Normalized SSD)? should we, for example, choose a smaller penalty for the SAD becease we are not squaring it?

Bhavjit: It is specific; you can get Neil's answer by using an occlusion penalty of 0.01.

Neil, as for the 3rd point you made, isnt the answer very specific to the value of occlusion penalty chosen?

Is anyone getting their disparity maps to look correct when using the 5x5 or 9x9 windows? My part B looks great, but for the Part C I continue to get large areas of occlusions. I have only modified the stereo_2d and stereo_1d algorithm slightly.