Matching cost computation

Theoretical basics

The first step compute from the pair of images a cost volumes containing the similarity coefficients. The cost volumes is a 4D tensor with dims [row, col, disp_col, disp_row].

For each disparity in the input vertical disparity range (disp_min_row, disp_max_row), Pandora2D will shift the right image by the corresponding vertical disparity and call Pandora to compute a cost volume with the input horizontal disparity range (disp_min_col, disp_max_col).

Different measures of similarity are available in Pandora2D :

• SAD (Sum of Absolute Differences)

• SSD (Sum of Squared Differences)

• ZNCC (Zero mean Normalized Cross Correlation)

Configuration and parameters

Table 4 Available parameters

Name	Description	Type	Default value	Available value	Required
matching_cost_method	Similarity measure	str		“ssd” , “sad”, “zncc”	Yes.
window_size	Window size for similarity measure	int	5	> 0	No
step	Step [row, col] for computing similarity coefficient	list[int, int]	[1, 1]	list[int >0, int >0]	No

Note

The order of steps should be [row, col].

Example

{
    "input" :
    {
        // input content
    },
    "pipeline" :
    {
        //...
        "matching_cost":
        {
            "matching_cost_method": "ssd",
            "window_size": 7,
            "step" : [5, 5]
        },
        //...
    }
}