  Stereo reconstruction models trained on small images do not generalize well
to high-resolution data. Training a model on high-resolution image size faces
difficulties of data availability and is often infeasible due to limited
computing resources. In this work, we present the Occlusion-aware Recurrent
binocular Stereo matching (ORStereo), which deals with these issues by only
training on available low disparity range stereo images. ORStereo generalizes
to unseen high-resolution images with large disparity ranges by formulating the
task as residual updates and refinements of an initial prediction. ORStereo is
trained on images with disparity ranges limited to 256 pixels, yet it can
operate 4K-resolution input with over 1000 disparities using limited GPU
memory. We test the model's capability on both synthetic and real-world
high-resolution images. Experimental results demonstrate that ORStereo achieves
comparable performance on 4K-resolution images compared to state-of-the-art
methods trained on large disparity ranges. Compared to other methods that are
only trained on low-resolution images, our method is 70% more accurate on
4K-resolution images.
