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Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
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Sensors 2019, 19(23), 5310; https://doi.org/10.3390/s19235310
Received: 23 September 2019 / Revised: 25 November 2019 / Accepted: 27 November 2019 / Published: 2 December 2019
(This article belongs to the Section Physical Sensors)
Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitching methods based on hand-crafted features are effective for constructing a cylindrical panorama from a sequence of images in the case when there are sufficient reliable features in the scene. However, these methods are unable to handle low-texture environments where no reliable feature correspondence can be established. This paper proposes a novel two-step image alignment method based on deep learning and iterative optimization to address the above issue. In particular, a light-weight end-to-end trainable convolutional neural network (CNN) architecture called ShiftNet is proposed to estimate the initial shifts between images, which is further optimized in a sub-pixel refinement procedure based on a specified camera motion model. Extensive experiments on a synthetic dataset, rendered photo-realistic images, and real images were carried out to evaluate the performance of our proposed method. Both qualitative and quantitative experimental results demonstrate that cylindrical panorama stitching based on our proposed image alignment method leads to significant improvements over traditional feature based methods and recent deep learning based methods for challenging low-texture environments. View Full-Text
Keywords: cylindrical panorama; low-texture environments; convolutional neural network (CNN); robust image alignment; sub-pixel optimization cylindrical panorama; low-texture environments; convolutional neural network (CNN); robust image alignment; sub-pixel optimization
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Kang, L.; Wei, Y.; Jiang, J.; Xie, Y. Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization. Sensors 2019, 19, 5310.

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