Parashar Shaifali, Long Yuxuan, Salzmann Mathieu, Fua Pascal
IEEE Trans Pattern Anal Mach Intell. 2024 Nov;46(11):7027-7040. doi: 10.1109/TPAMI.2024.3383316. Epub 2024 Oct 3.
A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations. While this approach is more successful than its counterparts relying on global constraints, the resulting methods face two main problems: First, most of the equation systems they formulate are of high degree and must be solved using computationally expensive polynomial solvers. Some methods use polynomial reduction strategies to simplify the system, but this adds some phantom solutions. In any event, an additional mechanism is employed to pick the best solution, which adds to the computation without any guarantees on the reliability of the solution. Second, these methods formulate constraints between a pair of images. Even if there is enough motion between them, they may suffer from local degeneracies that make the resulting estimates unreliable without any warning mechanism. In this paper, we solve these problems for isometric/conformal NRSfM. We show that, under widely applicable assumptions, we can derive a new system of equations in terms of the surface normals, whose two solutions can be obtained in closed-form and can easily be disambiguated locally. Our formalism also allows us to assess how reliable the estimated local normals are and to discard them if they are not. Our experiments show that our reconstructions, obtained from two or more views, are significantly more accurate than those of state-of-the-art methods, while also being faster.
非刚性运动结构(NRSfM)的一个最新趋势是表达图像对之间的局部微分约束,通过求解多项式方程组可以得到任意点的表面法线。虽然这种方法比依赖全局约束的方法更成功,但由此产生的方法面临两个主要问题:首先,它们所构建的大多数方程组次数较高,必须使用计算成本高昂的多项式求解器来求解。一些方法使用多项式约简策略来简化系统,但这会增加一些虚假解。无论如何,都需要采用额外的机制来挑选最佳解,这增加了计算量,而且对解的可靠性没有任何保证。其次,这些方法在一对图像之间构建约束。即使它们之间有足够的运动,也可能会出现局部退化情况,导致所得估计不可靠,而且没有任何警告机制。在本文中,我们针对等距/共形NRSfM解决了这些问题。我们表明,在广泛适用的假设下,我们可以根据表面法线推导出一个新的方程组,其两个解可以以封闭形式获得,并且可以很容易地在局部进行区分。我们的形式体系还使我们能够评估估计的局部法线的可靠性,并在不可靠时将其舍弃。我们的实验表明,从两个或更多视图获得的重建结果比现有方法的结果要准确得多,同时速度也更快。