Soft Cost Aggregation with Multi-resolution Fusion

This paper presents a simple and effective cost volume aggregation framework for addressing pixels labeling problem. Our idea is based on the observation that incorrect labelings are greatly reduced in cost volume aggregation results from low resolutions. However, image details may be lost in the low resolution results. To take advantage of the results from low resolution for reducing these incorrect labelings while preserving details, we propose a multi-resolution cost aggregation method (MultiAgg) by using a soft fusion scheme based on min-convolution. We implement our MultiAgg in applications on stereo matching and interactive image segmentation. Experimental results show that our method significantly outperforms conventional cost aggregation methods in labeling accuracy. Moreover, although MultiAgg is a simple and straight-forward method, it produces results which are close to or even better than those from iterative methods based on global optimization.