Euclidean reconstruction and auto-calibration from continuous motion

Fredrik Kahl, A. Heyden
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引用次数: 21

Abstract

This paper deals with the problem of incorporating natural regularity conditions on the motion in an MAP estimator for structure and motion recovery from uncalibrated image sequences. The purpose of incorporating these constraints is to increase performance and robustness. Auto-calibration and structure and motion algorithms are known to have problems with (i) the frequently occurring critical camera motions, (ii) local minima in the non-linear optimization and (iii) the high correlation between different intrinsic and extrinsic parameters of the camera, e.g. the coupling between focal length and camera position. The camera motion (both intrinsic and extrinsic parameters) is modelled as a random walk process, where the inter-frame motions are assumed to be independently normally distributed. The proposed scheme is demonstrated on both simulated and real data showing the increased performance.
欧几里得重建和自校准连续运动
本文研究了在MAP估计器中加入运动的自然规则条件的问题,用于从未校准的图像序列中恢复结构和运动。合并这些约束的目的是提高性能和健壮性。已知自动校准和结构与运动算法存在以下问题:(i)频繁发生的关键相机运动,(ii)非线性优化中的局部最小值,以及(iii)相机的不同内在和外在参数之间的高度相关性,例如焦距和相机位置之间的耦合。摄像机运动(包括内部和外部参数)被建模为随机游走过程,其中帧间运动被假设为独立的正态分布。仿真和实际数据验证了该方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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