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Stereo Matching Using Tree Filtering

Matching cost aggregation is one of the oldest and still popular methods for stereo correspondence. While effective and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously only locally-optimal, and the computational complexity of the full-kernel implementation usually depends on the region size. In this paper, the cost aggregation problem is re-examined and a non-local solution is proposed. The matching cost values are aggregated adaptively based on pixel similarity on a tree structure derived from the stereo image pair to preserve depth edges. The nodes of this tree are all the image pixels, and the edges are all the edges between the nearest neighboring pixels. The similarity between any two pixels is decided by their shortest distance on the tree. The proposed method is non-local as every node receives supports from all other nodes on the tree. The proposed method can be naturally extended to the time domain for enforcing temporal coherence. Unlike previous methods, the non-local property guarantees that the depth edges will be preserved when the temporal coherency between all the video frames are considered. A non-local weighted median filter is also proposed based on the non-local cost aggregation algorithm. It has been demonstrated to outperform all local weighted median filters on disparity/depth upsampling and refinement.