Depth-map generation for multi-view autostereoscopic 3-D displays based on the SIFT algorithm constrained by boundary

A depth-map estimation method, which converts two-dimensional images into three-dimensional (3-D) images for multi-view autostereoscopic 3-D displays, is presented. The proposed method utilizes the Scale Invariant Feature Transform (SIFT) matching algorithm to create the sparse depth map. The image boundaries are labeled by using the Sobel operator. A dense depth map is obtained by using the Zero-Mean Normalized Cross-Correlation (ZNCC) propagation matching method, which is constrained by the labeled boundaries. Finally, by using depth rendering, the parallax images are generated and synthesized into a stereoscopic image for multi-view autostereoscopic 3-D displays. Experimental results show that this scheme achieves good performances on both parallax image generation and multi-view autostereoscopic 3-D displays.