<p>Image stitching allows wide field-of-view images to be created. However, handheld shooting and alignment of overlapping regions in image stitching intrinsically result in irregular boundaries, compromising the wide-angle effect. To address this problem, we propose an unsupervised warping-based method for rectangling stitched images. We formulate irregular mesh prediction as a mesh motion regression task, constrained by three complementary objectives: shape-preserving, boundary-fitting, and content-preserving losses. This approach leverages geometric and semantic features of images to achieve rectangling without requiring labeled training data. Our primary contributions include (1) a label-free learning framework that improves rectification performance and generalization capability, and (2) a novel boundary-fitting scheme that reconstructs well-aligned meshes, producing visually natural rectangling results across diverse scenarios. Experiments demonstrate that our method achieves competitive or superior performance compared with state-of-the-art supervised methods.</p>