We present a new approach to rigid-body motion segmentation from two views. We use a previously developed nonlinear embedding of two-view point correspondences into a 9-dimensional space and identify the different motions by segmenting lower-dimensional subspaces. In order to overcome mixed and unknown dimensions of subspaces and nonuniform distributions along them we suggest the novel concept of global dimension and its minimization for clustering subspaces with some theoretical motivation. We propose a fast projected gradient algorithm for minimizing global dimension and thus segmenting motions from 2-views. We develop an outlier detection framework around the proposed method, and we present state-of-the-art results on outlier-free and outlier-corrupted two-view data for segmenting motion.

我们提出了一种新的刚体运动分割方法，它基于两个视图的非线性嵌入，通过对低维子空间进行分割来区分不同的运动，并采用全局维度和其最小化的概念来克服子空间沿非均匀分布的问题。我们提出了快速投影梯度算法来最小化全局维度，从而从两个视图中分割运动，同时开发了一个围绕所提出的方法的异常值检测框架，并在没有异常值和有异常值的两个视图数据上展示了最先进的结果。

使用全局尺寸最小化的新方法进行两视角运动分割