Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps

Abstract

Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks such as depth/disparity estimation. Although disparity maps can be estimated for all LF views, most existing methods merely estimate depth/disparity for the central view and do not adequately deal with other LF views. However, having depth/disparity maps for all LF views can be useful for enhancing immersive multimedia applications, such as 3D reconstruction and LF editing. To overcome this limitation, in this paper, an efficient and occlusion-aware disparity propagation method is proposed. The proposed method generates disparity maps for all LF views given a single disparity map for one reference view (e.g., the central view). The disparity map for the reference view is propagated first into the four corner views to ensure angular consistency. Afterwards, an off-the-shelf existing disparity estimation model is used to fill any remaining holes in the corner views. Finally, disparity maps for the remaining views are recursively generated through a fast propagation step, which is followed by a final refinement step to regularize the generated disparity maps. The proposed method not only generates disparity maps for all LF views but also handles occlusions and ensures angular consistency. Experimental results on synthetic and real LF datasets with different disparity ranges, using several accuracy and angular consistency metrics, show outperforming or competitive results compared to the benchmark methods with a significant complexity reduction.

Keywords

• Light field disparity estimation
• angular consistency
• fast disparity propagation
• deep learning