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Stereo reconstruction models trained on small images do not generalize wellto high-resolution data. Training a model on high-resolution image size facesdifficulties of data availability and is often infeasible due to limitedcomputing resources. In this work, we present the Occlusion-aware Recurrentbinocular Stereo matching (ORStereo), which deals with these issues by onlytraining on available low disparity range stereo images. ORStereo generalizesto unseen high-resolution images with large disparity ranges by formulating thetask as residual updates and refinements of an initial prediction. ORStereo istrained on images with disparity ranges limited to 256 pixels, yet it canoperate 4K-resolution input with over 1000 disparities using limited GPUmemory. We test the model's capability on both synthetic and real-worldhigh-resolution images. Experimental results demonstrate that ORStereo achievescomparable performance on 4K-resolution images compared to state-of-the-artmethods trained on large disparity ranges. Compared to other methods that areonly trained on low-resolution images, our method is 70% more accurate on4K-resolution images.

ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images