运动非线性最小二乘结构中创新信息的传播

Drew Steedly, Irfan Essa
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引用次数: 34

摘要

我们提出了一种改进现有运动结构(SFM)方法的新技术。我们提出了一种递归和最优的SFM算法。我们的方法将来自新帧的创新信息整合到现有解决方案中,而无需优化每个相机姿势和场景结构参数。为了做到这一点,我们逐步优化更大的参数子集,直到误差最小化。通过跟踪点和帧之间的连接,将这些附加参数包含在优化中。在许多情况下,添加一个帧的复杂性远远小于所有参数的全束调整。我们的算法最好描述为增量束调整,因为它允许将新信息添加到现有的非线性最小二乘解中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Propagation of innovative information in non-linear least-squares structure from motion
We present a new technique that improves upon existing structure from motion (SFM) methods. We propose a SFM algorithm that is both recursive and optimal. Our method incorporates innovative information from new frames into an existing solution without optimizing every camera pose and scene structure parameter. To do this, we incrementally optimize larger subsets of parameters until the error is minimized. These additional parameters are included in the optimization by tracing connections between points and frames. In many cases, the complexity of adding a frame is much smaller than full bundle adjustment of all the parameters. Our algorithm is best described us incremental bundle adjustment as it allows new information to be added to art existing non-linear least-squares solution.
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