Fast Hierarchical Depth Map Computation from Stereo
This work addresses memory constraints in stereo depth estimation for applications with limited computational power, representing an incremental improvement over existing block-matching methods.
The paper tackles the problem of high memory usage in stereo depth map computation by introducing a multi-scale-hierarchical block-matching approach, which achieves results slightly worse than state-of-the-art SGM on the Middlebury benchmark while preserving low memory footprint and reducing complexity.
Disparity by Block Matching stereo is usually used in applications with limited computational power in order to get depth estimates. However, the research on simple stereo methods has been lesser than the energy based counterparts which promise a better quality depth map with more potential for future improvements. Semi-global-matching (SGM) methods offer good performance and easy implementation but suffer from the problem of very high memory footprint because it's working on the full disparity space image. On the other hand, Block matching stereo needs much less memory. In this paper, we introduce a novel multi-scale-hierarchical block-matching approach using a pyramidal variant of depth and cost functions which drastically improves the results of standard block matching stereo techniques while preserving the low memory footprint and further reducing the complexity of standard block matching. We tested our new multi block matching scheme on the Middlebury stereo benchmark. For the Middlebury benchmark we get results that are only slightly worse than state of the art SGM implementations.