This paper presents a GPU-based real-time approach for generating high-definition (HD) aerial image mosaics. The cumbersome process of registering HD images is addressed by a parallel scheme that rapidly matches binary features. The proposed feature matcher takes advantage of the fast ORB (oriented FAST and rotated BRIEF) descriptor and its attainable arrangement into hash tables. By exploiting the best functionalities of binary descriptors and hashing-based data structures, the process of creating HD mosaics is accelerated. On average, real-time performance of 14.5 ms is achieved in a frame-to-frame process, for input images of 2.7 K resolution (2704 × 1521). For evaluation purposes in terms of robustness and speed, we selected two image registration methods for comparison. The first method uses the feature extractor and matcher modules of the well-known ORB-SLAM. The second comparison is carried out against the standard KNN-based matcher of OpenCV. The experiments were conducted under different conditions and scenarios, and the proposed approach exhibits a speed-up of 10.5 times compared to ORB-SLAM-based approach and 36.5 times compared to the OpenCV matcher. Therefore, this research widens the range of applications for aerial mosaicing, since the proposed system is capable of creating high-detail panoramas of large sites while acquiring data.
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This work has been partially funded by the Royal Society through the Newton Advanced Fellowship with reference NA-140454 and by the CONACYT-INEGI research project 268528.
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de Lima, R., Cabrera-Ponce, A.A. & Martinez-Carranza, J. Parallel hashing-based matching for real-time aerial image mosaicing. J Real-Time Image Proc 18, 143–156 (2021). https://doi.org/10.1007/s11554-020-00959-y
- Image stitching
- Feature matching
- Binary descriptors