Matching cost computation

Theoretical basics

The first step is to compute the cost volume containing the similarity coefficients. Different measures of similarity are available in Pandora :

SAD (Sum of Absolute Differences)
SSD (Sum of Squared Differences)
Census [Zabih1994]
ZNCC (Zero mean Normalized Cross Correlation)

It is possible to oversample the cost volume by a factor of 2 or 4 ( with the subpix parameter ) compared to to the input images. It can be useful for Disparity refinement

Note

The cost volume disparity dimension is sampled at the input images rate by default. Thus disparities tested are integers. However, to prevent from aliasing effects when refining the disparity map, one can use the subpix parameter. This will add subpixel disparities into the cost volume by oversampling the disparity dimension by an even factor.

Pandora can take into account a mask and a nodata value for each image. The masks and nodata are used during the matching cost computation :

Nodata pixel management: if the left window contains nodata, the center pixel of the window is invalidated. Therefore,the disparity range is invalidated : $cost(x, y, \forall d) = nan$ . If the right window contains nodata, the center pixel is invalidated. As a result, the pixels of the left image such as $I_{L}(x, y) = I_{R}(x + d, y)$ , are invalidated $cost(x, y, d) = nan$
Management of hidden pixels: if the center pixel of the left window is hidden, the disparity range is invalidated : $cost(x, y, \forall d) = nan$ . If the pixel in the center of the right window is hidden, the pixels of the left image such as $I_{L}(x, y) = I_{R}(x + d, y)$ are invalidated $cost(x, y, d) = nan$

[Zabih1994]

Zabih, R., & Woodfill, J. (1994, May). Non-parametric local transforms for computing visual correspondence. In European conference on computer vision (pp. 151-158). Springer, Berlin, Heidelberg.

Configuration and parameters

Name	Description	Type	Default value	Available value	Required
matching_cost_method	Similarity measure	string		ssd sad census zncc or mc_cnn if plugin_mccnn is installed	Yes
window_size	Window size for similarity measure	int	5	Must be >0 and For “census” : {3 5}	No
subpix	Cost volume upsampling factor	int	1	{1 2 4}	No
band	Band value on which to calculate the correlation	str	None	band in left and right image’s metadata	No
step	Computing step for pandora2d	int	1	1	No

For mc_cnn similarity measure see Configuration and parameters of Plugin mccnn for sub-parameters and configuration example.

Note

NB: Parameter band_im must be present in the left and right image’s metadata.
If the input images are multiband, the band’s names must be present on the image metadata. To see how to add band’s names on the image’s metadata, please see FAQ.

Example for mono band images

{
    "input" :
    {
        // ...
    },
    "pipeline" :
    {
        // ...
        "matching_cost":
        {
            "matching_cost_method": "ssd",
            "window_size": 7,
            "subpix": 4
        }
        // ...
    }
}

Example for multiband images

{
    "input" :
    {
        // ...
    },
    "pipeline" :
    {
        // ...
        "matching_cost":
        {
            "matching_cost_method": "ssd",
            "band":"r",
            "window_size": 7,
            "subpix": 4
        }
        // ...
    }
}