Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling

We address the problem of robust two-view correspondence estimation within the context of dynamic scene modeling. To this end, we investigate the use of local spatio-temporal assumptions to both identify and refine dense low-level data associations in the absence of prior dynamic content models. By developing a strictly data-driven approach to correspondence search, based on bottom-up local 3D motion cues of local rigidity and non-local coherence, we are able to robustly address the higher-order problems of video synchronization and dynamic surface modeling. Our findings suggest an important relationship between these two tasks, in that maximizing spatial coherence of surface points serves as a direct metric for the temporal alignment of local image sequences. The obtained results for these two problems on multiple publicly available dynamic reconstruction datasets illustrate both the effectiveness and generality of our proposed approach.