A three-dimensional data measurement method combining stereo vision and Fourier transform profilometry(FTP) is proposed in this paper. Stereo vision is simple and fast but it is prone to mismatch in smooth areas. FTP has high accuracy over smooth areas but it can only measure a limited height gradient. Depth information of high quality is obtained by combining these two techniques. Firstly, the system was constructed with four CMOS sensor cameras and a projector. Four cameras’ position were adjusted to capture pictures of scene, and thus four depth maps were obtained from disparity. The rough depth map of the central camera field was obtained using image transform and image mosaic. Secondly, the rough depth map was segmented into parts with similar depth by Flood Fill algorithm. A mask was constructed based on each part to choose the proper areas of stripe image for FTP. Modulation analysis was used to get unwrapped phase from the distorted strip pattern caused by the object height. By merging the initial depth map with the depth map of FTP, the final depth map of high quality was obtained.
A novel method is proposed in this paper to accurately reconstruct the three-dimensional scenes by using a passive single-shot exposure with a lenslet light field camera. This method has better performance of 3D scenes reconstruction with both defocus and disparity depth cues captured by light field camera. First, the light field data is used to refocus and shift viewpoints to get a focal stack and multi-view images. In refocusing procedure, the phase shift theorem in the Fourier domain is first introduced to substitute shift in spatial domain, and sharper focal stacks can be obtained with less blurriness. Thus, 3D scenes can be reconstructed more accurately. Next, through multi-view images, disparity depth cues are obtained by performing correspondence measure. Then, the focal stack is used to compute defocus depth cues by focus measure based on gray variance. Finally, the focus cost is built to integrate both defocus and disparity depth cues, and the accurate depth map is estimated by using Graph Cuts based on the focus cost. Using this accurate depth map and all-in-focus image, the 3D structure in real world are accurately reconstructed. Our method is verified by a number of synthetic and real-world examples captured with a dense camera array and a Lytro light field camera.