Abstract
Abstract State-of-the-art methods for disparity estimation achieve good results for single stereo frames, but temporal coherence in stereo videos is often neglected. In this paper, we present a method to compute temporally coherent disparity maps. We define an energy over whole stereo sequences and optimize their conditional random field (CRF) distributions using the mean-field approximation. In addition, we introduce novel terms for smoothness and consistency between the left and right views. We perform CRF optimization by fast, iterative spatio-temporal filtering with linear complexity in the total number of pixels. We propose two CRF optimization techniques, using parallel and sequential updates, and compare them in detail. While parallel updates are not guaranteed to converge, we show that, in practice with appropriate initialization, they provide the same quality as sequential updates and they also lead to faster implementations. Finally, we demonstrate that the results of our approach rank among the state of the art while having significantly less flickering artifacts in stereo sequences.
Highlights
While some disparity estimation methods leverage information over several frames of stereo video sequences, most do not attempt to produce temporally coherent disparity maps
We address these challenges by proposing a technique that produces temporally coherent disparity maps over stereo videos
Our contributions are (1) a new smoothness term that leverages both the left and right images to distinguish between image edges due to disparity discontinuities, and edges due to surface texture; (2) a novel consistency term to obtain a joint left-and-right disparity estimation problem; (3) a temporal smoothness term to achieve temporally coherent disparity maps over stereo video sequences; (4) a comparison of efficient conditional random field (CRF) optimization techniques based on parallel and sequential updates
Summary
While some disparity estimation methods leverage information over several frames of stereo video sequences, most do not attempt to produce temporally coherent disparity maps. Our contributions are (1) a new smoothness term that leverages both the left and right images to distinguish between image edges due to disparity discontinuities, and edges due to surface texture; (2) a novel consistency term to obtain a joint left-and-right disparity estimation problem; (3) a temporal smoothness term to achieve temporally coherent disparity maps over stereo video sequences; (4) a comparison of efficient CRF optimization techniques based on parallel and sequential updates. The method computes an initial disparity map using SGM and jointly optimizes for planar surfaces and local segments This approach is tailored for applications such as autonomous vehicles with an ego-motion assumption. Using a piecewise rigid model, their method includes consistencies in the temporal dimension that incorporates neighboring views Unlike these methods, we do not enforce segmentation nor local planarity on our disparity maps. In addition to end-to-end disparity error, we propose a quantitative measure to better evaluate the flicker artifacts in disparity sequences and compare with previous works
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