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Image outpainting gains increasing attention since it can generate thecomplete scene from a partial view, providing a valuable solution to construct{360\textdegree} panoramic images. As image outpainting suffers from theintrinsic issue of unidirectional completion flow, previous methods convert theoriginal problem into inpainting, which allows a bidirectional flow. However,we find that inpainting has its own limitations and is inferior to outpaintingin certain situations. The question of how they may be combined for the best ofboth has as yet remained under-explored. In this paper, we provide a deepanalysis of the differences between inpainting and outpainting, whichessentially depends on how the source pixels contribute to the unknown regionsunder different spatial arrangements. Motivated by this analysis, we present aCylin-Painting framework that involves meaningful collaborations betweeninpainting and outpainting and efficiently fuses the different arrangements,with a view to leveraging their complementary benefits on a seamless cylinder.Nevertheless, straightforwardly applying the cylinder-style convolution oftengenerates visually unpleasing results as it discards important positionalinformation. To address this issue, we further present a learnable positionalembedding strategy to incorporate the missing component of positional encodinginto the cylinder convolution, which significantly improves the panoramicresults. It is noted that while developed for image outpainting, the proposedalgorithm can be effectively extended to other panoramic vision tasks, such asobject detection, depth estimation, and image super-resolution. Code will bemade available at \url{https://github.com/KangLiao929/Cylin-Painting}.

Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond