Comparison with Inpainting Methods
Image rectangling aims to eliminate irregular boundaries while maintaining as much data
consistency and achieving good qualitative results as possible, therefore, previous
rectangling methods[1,2] choose to warp stitching images.
While inpainting methods are powerful at generating visually pleasing outcomes, they tend to
introduce extra content into the stitched ones, as demonstrated below, thus affecting the
data consistency negatively.
As shown in the table below, inpainted stitching images (row 2 and 3) result in the much
lower PSNR/SSIM metrics than by RecDiffusion.
Moreover, the quality of the images generated through inpainting techniques does not meet
the requirements of this task, as evident from their FID scores.
These scores are higher than the FID scores comparing the stitched input images to the
ground truth rectangling images, indicating a significant discrepancy in image quality.