Recently, stereo matching algorithms based on end-to-end convolutional neural networks achieve excellent
performance far exceeding traditional algorithms. Current state-of-the-art stereo matching networks mostly
rely on full cost volume and 3D convolutions to regress dense disparity maps. These modules are computationally
complex and high consumption of memory, and difficult to deploy in real-time applications. To overcome this
problem, we propose multilevel disparity reconstruction network, MDRNet, a lightweight stereo matching network
without any 3D convolutions. We use stacked residual pyramids to gradually reconstruct disparity maps from
low-level resolution to full-level resolution, replacing common 3D computation and optimization convolutions. Our
approach achieves a competitive performance compared with other algorithms on stereo benchmarks and real-time
inference at 30 frames per second with 4×104 resolutions.
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