Abstract
For large-scale and highly redundant photo collections, eliminating statistical redundancy in multi-view geometry is of great importance to efficient 3D reconstruction. Our approach takes the full set of images with initial calibration and recovered sparse 3D points as inputs, and obtains a subset of views that preserve the final reconstruction accuracy and completeness well. We first construct an image quality graph, in which each vertex represents an input image, and the problem is then to determine a connected sub-graph guaranteeing a consistent reconstruction and maximizing the accuracy and completeness of the final reconstruction. Unlike previous works, which only address the problem of efficient structure from motion (SfM), our technique is highly applicable to the whole reconstruction pipeline, and solves the problems of efficient bundle adjustment, multi-view stereo (MVS), and subsequent variational refinement.
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Acknowledgement
We really appreciate the support of RGC-GRF 618711, RGC/NSFC N_HKUST607/11, ITC-PSKL12EG02, and National Basic Research Program of China (2012CB316300).
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Zhu, S., Fang, T., Zhang, R., Quan, L. (2015). Multi-view Geometry Compression. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_1
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DOI: https://doi.org/10.1007/978-3-319-16808-1_1
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