Abstract
In this thesis, we propose a novel method to refine the depth map from stereo video. The proposed method enforces the spatial and temporal consistency of the depth maps computed from stereo video. Most previous works on estimating depth map from videos applied different ways to recover the depth by first deciding if a pixel belongs to dynamic region or static region. Because the static region has the same depth over time, but the dynamic region does not. Most methods employ a binary map to separate two regions in an image. Our method employs a probabilistic framework to describe the probability for a dynamic pixel or a static pixel. Compared to the binary map, the probability map is more flexible. In the proposed approach, we estimate the initial disparity map for each frame. The initial disparity map is temporally inconsistent because it is estimated with a stereo matching method from a single pair of stereo images. We apply the optical flow to estimate the corresponding pixels between neighboring frames. If the pixel is static, we smooth its depth both spatially and temporally. If the pixel is dynamic, we just smooth its depth spatially. In the final step, we optimize the depth map by applying iterative reweighted least squares (IRLS). Our experiments show the refined depth maps by using the proposed algorithm are more accurate and temporally consistent through experiments on a number of different stereo videos.