Dense depth map generation using sparse depth data from normal flow

Abstract

In this paper, we address the problem of dense depth map generation from two successive image frames in a video. We first recover the camera motion from the observable normal flow pattern using our previously proposed apparent flow constraints. Once the camera motion is estimated, sparse depth data can be directly recovered from the flow pattern. We utilize a hierarchical approach to generate an initial dense depth map from the sparse depth data. This depth map is further enhanced through the refinement of the associated optical flow field in a variational framework. Experimental results show that the proposed method can provide high-quality depth maps. We also have a faster computational time than the conventional optical flow approach.