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Open Access A New GPU Bundle Adjustment Method for Large-Scale Data

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We developed a fast and effective bundle adjustment method for large-scale datasets. The preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology are simultaneously applied to deal with large-scale data and to accelerate the bundle adjustment process. The whole bundle adjustment process is modified to enable parallel computing. The critical optimization on parallel task assignment and GPU memory usage are specified. The proposed method was tested using 10 datasets. The traditional Levenberg Marquardt (LM) method, advanced PCG method, Wu's method and the proposed GPU parallel computing method are all compared and analyzed. Preliminary results have shown that the proposed method can process a large-scale dataset with about 13,000 images in less than three minutes on a common computer with GPU device. The efficiency of the proposed method is about the same with Wu's method while the accuracy is better.

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Document Type: Research Article

Publication date: September 1, 2017

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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