Real-time stereo on GPGPU using progressive multi-resolution adaptive windows

We introduce a new GPGPU-based real-time dense stereo matching algorithm. The algorithm is based on a progressive multi-resolution pipeline which includes background modeling and dense matching with adaptive windows. For applications in which only moving objects are of interest, this approach effectively reduces the overall computation cost quite significantly, and preserves the high definition details. Running on an off-the-shelf commodity graphics card, our implementation achieves a 36 fps stereo matching on 1024 × 768 stereo video with a fine 256 pixel disparity range. This is effectively same as 7200 M disparity evaluations per second. For scenes where the static background assumption holds, our approach outperforms all published alternative algorithms in terms of the speed performance, by a large margin. We envision a number of potential applications such as real-time motion capture, as well as tracking, recognition and identification of moving objects in multi-camera networks.

Graphical AbstractFigure optionsDownload full-size imageDownload high-quality image (615 K)Download as PowerPoint slideHighlights► Background modeling is used to reduce computational cost of stereo matching. ► Dense matching is performed on multiple resolutions in a coarse-to-fine fashion. ► In-kernel integral image is used to accelerate the matching cost aggregation in GPGPU implementation.