The Sensitivity of Structure-From-Motion: A Comprehensive Theoretical and Experimental Study

IIS-9820224卡洛·托马西斯坦福大学$250,043-36摩尔运动结构的敏感性：全面的理论和实验研究这是一个为期三年的标准奖。PI将对运动结构(SFM)的灵敏度进行一般的理论分析和实验评估。这项任务需要发展技术，不仅扩展计算机视觉的技术水平，而且扩展统计参数估计的技术水平。具体地说，对于非方问题(测量值多于未知量)，将开发将全密度从测量传播到未知数的方法，并且标准协方差传播技术将被扩展到具有隐式公式的约束优化问题。将开发新的技术来将不适定问题分解为可解和不可解的分量。使用这些技术，将在不使用噪声发生器的情况下，系统地分析SFM标准配方的灵敏度。此外，与以前的研究相比，人们将更全面地了解非线性的影响。通过新提出的校准方法与实验结果进行比较，将验证理论结论，并将清楚地描述在给定相机质量水平和给定性能要求下，哪些SFM计算是可能的。将SFM问题分解为可解和不可解分量，以及将性能空间划分为可行和不可行解问题，将产生一种新的算法，它以原则性和通用性的方式结合了成功的特例算法。

IIS-9820224Carlo TomasiStanford University$250,043 - 36 mosThe Sensitivity of Structure-from-Motion: A Comprehensive Theoretical and Experimental StudyThis is a three year standard award. The PI will carry out a general theoretical analysis and experimental evaluation of the sensitivity of structure-from-motion (SFM). This task requires the development of techniques that extend the state of the art not only of computer vision but also of statistical parameter estimation. Specifically, methods for propagating full densities from measurements to unknowns will be developed for nonsquare problems (more measurements than unknowns), and standard covariance propagation techniques will be extended to constrained optimization problems with implicit formulations. New techniques will be developed for the decomposition of ill-posed problems into solvable and unsolvable components. Using these techniques, the sensitivity of standard formulations of SFM will be analyzed systematically, and without the use of noise generators. In addition, the effects of nonlinearities will be understood more completely than in previous studies. Comparison with experimental results, made possible by newly proposed calibration methods, will validate the theoretical conclusions and will lead to a clear delineation of which SFM computations are possible with a given level of camera quality and for a given performance requirement. The decomposition of SFM problems into solvable and unsolvable components, as well as the partition of performance space into feasible and infeasible problems, will lead to a new algorithm that combines successful special-case algorithms in a principled and general way.