LAUSR

Image rectangling involves filling in the blanks created during image stitching through deformation techniques. However, existing methods still struggle with incomplete filling and distortion of content, ultimately affecting the overall visual impression and potentially hindering subsequent tasks such as recognition. In this work, we design a pixel-wise deformation framework that utilizes explicit edge guidance to maintain consistency of texture and structure, yielding rectangular images with natural structure. Specifically, we decouple motion into region-level and pixel-level components through uniform mesh warping and pixel-wise deformation to precisely rearrange the spatial distribution of all pixels. Uniform deformation preserves local structure within divided patches, while pixel-wise motion coordinates the consistency between patches. Their combination provides robust and accurate pixel-wise offsets for structure-preserved rectangling. To further bolster the consistency of structure and texture, we leverage edge information to establish structural constraints and design an edge-guided enhancement module to aid in restoring fine texture details. Additionally, stitched images encompass both meaningful content and blank spaces, we innovatively incorporate a mask predictor, which acts as a guiding beacon, directing the network's attention solely towards content-rich regions to facilitate precise pixel-wise motion estimation. Experimental results demonstrate that our approach achieves state-of-the-art performance in rectifying irregular boundaries while contributing to downstream visual perception tasks.