We propose Progressive-X+, a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems. Also, we propose a sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively growing neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include using multiple homographies to estimate relative poses for global SfM; pose estimation from generalized homographies; and trajectory estimation of fast-moving objects.
翻译:我们提出“进步-X+”的新算法,用于寻找数量不详的几何模型,例如同质体。问题被正式确定为在不形成精确点到模型任务的情况下逐步找到主导模型实例。主要实例是通过类似于RANSAC的抽样和由模型质量功能驱动的合并过程,其中考虑到以前提出的事例。新实例是通过在共识空间中分组找到的。这一新配法导致一种具有最新精确度的简单迭代算法,同时实时运行一些视觉问题。此外,我们提议一个取样员,反映真实世界数据往往形成空间一致性结构的事实。取样员返回在逐渐扩大的邻里绘图中连接的组件。我们提出了多种几何模型的使用提高了准确性的一些应用。其中包括使用多种同系法来估计全球SfM的相对构成;根据通用同系法进行估计;以及快速移动物体的轨迹估计。