A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization
This addresses motion segmentation in computer vision, an incremental improvement with specific gains for two-view data.
The paper tackles rigid-body motion segmentation from two views by introducing global dimension minimization to cluster subspaces, achieving state-of-the-art results on both outlier-free and outlier-corrupted data.
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 nonuniform distributions along the subspaces, whose dimensions are unknown, 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.