Abstract

We introduce a real-time stereo matching technique based on a reformulation of Yoon and Kweon’s adaptive support weights algo- rithm [1]. Our implementation uses the bilateral grid to achieve a speedup of 200× compared to a straightforward full-kernel GPU implementation, making it the fastest technique on the Middlebury website. We introduce a colour component into our greyscale approach to recover precision and depth superresolution 100×. We further present a spatiotemporal stereo increase discriminability. Using our implementation, we speed up spatial- matching approach based on our technique that incorporates temporal evidence in real time (>14 fps ). Our technique visibly reduces flickering and outperforms per-frame approaches in the presence of image noise. We have created five synthetic stereo videos, with ground truth dispar- ity maps, to quantitatively evaluate depth estimation from stereo video. Source code and datasets are available on our pro ject website1 .