Hi thanks for the amazing work on PDC-Net.
I'm just having a hard time understanding Figure3 in PDC-Net.. and there was no where else to ask so I'm asking it here!
This figure seems to be the motivation to use the mixture model (predicting M components of variances instead of one).
Can you give an easier explanation on figure3 ? especially on exactly what the inlier and outlier is in this context.
I think i would be able to better understand figure 3 with an example, so I provided you with an example below. Thanks in advance!!
(1) given two images, a query image Q and a reference image R, which have spatial size of (H,W).
(2) GLUnet predicted the flowfield of R to Q as Y_pred. where Y_pred has shape of (H,W,2)
(3) and we have the ground truth flow as Y_gt. where Y_gt has shape of (H,W,2)
(4) now from the current setting, how was figure3 obtained?
what exactly does the x dimension ("Absolute Error (pixels)), inliers and outliers mean in figure 3? Thanks again!
Hi thanks for the amazing work on PDC-Net.
I'm just having a hard time understanding Figure3 in PDC-Net.. and there was no where else to ask so I'm asking it here!
This figure seems to be the motivation to use the mixture model (predicting M components of variances instead of one).
Can you give an easier explanation on figure3 ? especially on exactly what the inlier and outlier is in this context.
I think i would be able to better understand figure 3 with an example, so I provided you with an example below. Thanks in advance!!
(1) given two images, a query image Q and a reference image R, which have spatial size of (H,W).
(2) GLUnet predicted the flowfield of R to Q as Y_pred. where Y_pred has shape of (H,W,2)
(3) and we have the ground truth flow as Y_gt. where Y_gt has shape of (H,W,2)
(4) now from the current setting, how was figure3 obtained?
what exactly does the x dimension ("Absolute Error (pixels)), inliers and outliers mean in figure 3? Thanks again!