Crack detection for bridge bottom surfaces via remote sensing techniques is undergoing a revolution in the last few years. For such applications, a large amount of images, acquired with high-resolution industrial cameras close to the bottom surfaces with some mobile platform, are required to be stitched into a wide-view single composite image. The conventional idea of stitching a panorama with the affine model or the homographic model always suffers a series of serious problems due to poor texture and out-of-focus blurring introduced by depth of field. In this paper, we present a novel method to seamlessly stitch these images aided by 3D structure lines of bridge bottom surfaces, which are extracted from 3D camera data. First, we propose to initially align each image in geometry based on its rough position and orientation acquired with both a laser range finder (LRF) and a high-precision incremental encoder, and these images are divided into several groups with the rough position and orientation data. Secondly, the 3D structure lines of bridge bottom surfaces are extracted from the 3D cloud points acquired with 3D cameras, which impose additional strong constraints on geometrical alignment of structure lines in adjacent images to perform a position and orientation optimization in each group to increase the local consistency. Thirdly, a homographic refinement between groups is applied to increase the global consistency. Finally, we apply a multi-band blending algorithm to generate a large-view single composite image as seamlessly as possible, which greatly eliminates both the luminance differences and the color deviations between images and further conceals image parallax. Experimental results on a set of representative images acquired from real bridge bottom surfaces illustrate the superiority of our proposed approaches.