论文标题
可伸缩的组合求解器，用于弹性几何一致的3D形状匹配
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
论文作者
论文摘要
我们提出了一种可扩展的组合算法，用于在3D形状之间的几何一致映射空间上进行全球优化。我们使用Windheuser等人提出的数学优雅形式主义。 （ICCV 2011）其中3D形状匹配是在定向性扩散差异的空间上作为整数线性程序配制的。到目前为止，由于其复杂的约束结构及其尺寸较大，因此所产生的公式的实际适用性有限。我们提出了一种新颖的原始启发式，再加上一个拉格朗日双重问题，与以前的求解器相比，几个次数的数量更快。这使我们可以处理比以前可解决的三角形要多得多的形状。我们在不同的数据集上展示了引人注目的结果，甚至展示了我们可以解决匹配两个部分形状的挑战性设置，而无需使用完整的形状。我们的代码可在http://github.com/paul0noah/sm-comb上公开获取。
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant formalism proposed by Windheuser et al. (ICCV 2011) where 3D shape matching was formulated as an integer linear program over the space of orientation-preserving diffeomorphisms. Until now, the resulting formulation had limited practical applicability due to its complicated constraint structure and its large size. We propose a novel primal heuristic coupled with a Lagrange dual problem that is several orders of magnitudes faster compared to previous solvers. This allows us to handle shapes with substantially more triangles than previously solvable. We demonstrate compelling results on diverse datasets, and, even showcase that we can address the challenging setting of matching two partial shapes without availability of complete shapes. Our code is publicly available at http://github.com/paul0noah/sm-comb .
