  Spatially varying warps are increasingly popular for image alignment. In
particular, as-projective-as-possible (APAP) warps have been proven effective
for accurate panoramic stitching, especially in cases with significant depth
parallax that defeat standard homographic warps. However, estimating spatially
varying warps requires a sufficient number of feature matches. In image regions
where feature detection or matching fail, the warp loses guidance and is unable
to accurately model the true underlying warp, thus resulting in poor
registration. In this paper, we propose a correspondence insertion method for
APAP warps, with a focus on panoramic stitching. Our method automatically
identifies misaligned regions, and inserts appropriate point correspondences to
increase the flexibility of the warp and improve alignment. Unlike other warp
varieties, the underlying projective regularization of APAP warps reduces
overfitting and geometric distortion, despite increases to the warp complexity.
Comparisons with recent techniques for parallax-tolerant image stitching
demonstrate the effectiveness and simplicity of our approach.
