Object-Scale Adaptive Optical Flow Estimation Network

Mu Li, Baojiang Zhong, Kai-Kuang Ma

Abstract: Current deep learning methods for optical flow estimation often use spatial feature pyramids to extract image features. To get the correlation between images, they directly compute the cost volume of the obtained image features. In this process, fine object details tend to be ignored. To solve this fundamental problem, an object-scale adaptive optical flow estimation network is proposed, in which multi-scale features are selectively extracted and exploited using our developed feature selectable block (FSB). As a result, we can obtain the multi-scale receptive fields of objects at different scales in the image. To consolidate all image features generated from all scales, a new cost volume generation scheme called multi-scale cost volume generation block (MCVGB) is further proposed to aggregate information from different scales. Extensive experiments conducted on the Sintel and KITTI2015 datasets show that our proposed method can capture fine details of different scale objects with high accuracy and thus deliver superior performance over a number of state-of-the-art methods.