Image stitching with perspective-preserving warping
This work addresses image stitching issues for applications like photography and computer vision, offering an incremental improvement over existing methods by better handling complex scenes.
The paper tackles the problem of image stitching under challenging conditions like casual camera motions and large depth changes, where global transformations cause misalignments and perspective distortions. The proposed perspective-preserving warping method combines local projective and similarity transformations, achieving satisfactory alignment accuracy and reduced distortions in experiments on various images.
Image stitching algorithms often adopt the global transformation, such as homography, and work well for planar scenes or parallax free camera motions. However, these conditions are easily violated in practice. With casual camera motions, variable taken views, large depth change, or complex structures, it is a challenging task for stitching these images. The global transformation model often provides dreadful stitching results, such as misalignments or projective distortions, especially perspective distortion. To this end, we suggest a perspective-preserving warping for image stitching, which spatially combines local projective transformations and similarity transformation. By weighted combination scheme, our approach gradually extrapolates the local projective transformations of the overlapping regions into the non-overlapping regions, and thus the final warping can smoothly change from projective to similarity. The proposed method can provide satisfactory alignment accuracy as well as reduce the projective distortions and maintain the multi-perspective view. Experiments on a variety of challenging images confirm the efficiency of the approach.