The disparity computed by Pandora2D is such that:
I_{L}(x, y) = I_{R}(x + dx, y + dy)
with I_{L} , I_{R} the left image (left image) and the right image (right image), and dx the column disparity and dy the row disparity.
At this stage, a 4D (dims: row, col, disp_col, disp_row) cost_volumes is store. We use the Winner-Takes-All strategy to find the right disparity for each pixel. That's mean we are looking for the min (resp: max for zncc measure). For column's disparities (resp: row's disparities) we search the min or max in disp_row (res: disp_col) to obtain a 3D cost_volume (row, col, disp_col (res: disp_row)). To conclude, we extract the disparity of min (or max) from the 3D cost_volume and we obtain two disparity maps for row and col.
Name | Description | Type | Default value | Available value | Required |
---|---|---|---|---|---|
disparity _method | Disparity method | string | "wta" | Yes | |
invalid_disparity | Invalid disparity value | str, int, float | NaN | "NaN", "inf", int | No |
Example
{
"input" :
{
// input content
},
"pipeline" :
{
// ...
"disparity":
{
"disparity _method": "wta",
"invalid_disparity": "NaN"
},
// ...
}
}