Image Stitching Based on Semantic Planar Region Consensus

Image stitching for two images without a global transformation between them is notoriously difficult. In this paper, noticing the importance of semantic planar structures under perspective geometry, we propose a new image stitching method which stitches images by allowing for the alignment of a set of matched dominant semantic planar regions. Clearly different from previous methods resorting to plane segmentation, the key to our approach is to utilize rich semantic information directly from RGB images to extract semantic planar image regions with a deep Convolutional Neural Network (CNN). We specifically design a module implementing our newly proposed clustering loss to make full use of existing semantic segmentation networks to accommodate region segmentation. To train the network, a dataset for semantic planar region segmentation is constructed. With the prior of semantic planar region, a set of local transformation models can be obtained by constraining matched regions, enabling more precise alignment in the overlapping area. We also use this prior to estimate a transformation field over the whole image. The final mosaic is obtained by mesh-based optimization which maintains high alignment accuracy and relaxes similarity transformation at the same time. Extensive experiments with both qualitative and quantitative comparisons show that our method can deal with different situations and outperforms the state-of-the-arts on challenging scenes.