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Titlebook: Computer Vision -- ACCV 2014; 12th Asian Conferenc Daniel Cremers,Ian Reid,Ming-Hsuan Yang Conference proceedings 2015 Springer Internation

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Alexander E. Davies,Kenneth B. Kaplanure selection is used to reduce the computational complexity with high dimensional data, and an ensemble learning model is used to improve the efficiency with a large number of data. We demonstrate that the proposed method outperforms the-state of the art approaches in terms of computational complexity on various synthetic and real datasets.
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Multi-view Geometry Compressiond 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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Camera Calibration Based on the Common Self-polar Triangle of Sphere Imagese geometry and recovers five intrinsic parameters without calculating the projected circle center, which is more intuitive and simpler than the previous linear ones. Experiments with simulated data, as well as real images, show that our technique is robust and accurate.
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DEPT: Depth Estimation by Parameter Transfer for Single Still Imagesy little space.We evaluate our depth estimation method on the benchmark RGB-D (RGB + depth) datasets. The experimental results are comparable to the state-of-the-art, demonstrating the promising performance of our proposed method.
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Performance Evaluation of 3D Local Feature DescriptorsSI) has the best overall performance across all datasets. Unique Shape Context (USC), Rotational Projection Statistics (RoPS), 3D Shape Context (3DSC), and Signature of Histograms of OrienTations (SHOT) also achieved overall acceptable results.
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