Stereo matching github. " GitHub is where people build software.
Stereo matching github. To associate your repository with the stereo-matching topic, visit your repo's landing page and select "manage topics. However, achieving strong zero-shot generalization — a hallmark of foundation models in other computer vision tasks — remains challenging for stereo matching. Mar 8, 2024 · Specifically, we develop a flexible and efficient stereo matching codebase, called OpenStereo. To address these challenges, we propose a novel stereo matching framework that combines the strengths of stereo and monocular depth estimation. OpenStereo includes training and inference codes of more than 10 network models, making it, to our knowledge, the most complete stereo matching toolbox available. Jul 3, 2025 · Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. . Our model, Stereo Anywhere, leverages geometric constraints from stereo matching with robust priors from monocular depth Vision Foundation Models (VFMs). Our framework eliminates the need for complex hardware setups while enabling high-quality stereo image generation, making it valuable for both real-world applications and unsupervised learning scenarios. " GitHub is where people build software. We introduce Stereo Anywhere, a novel stereo-matching framework that combines geometric constraints with robust priors from monocular depth Vision Foundation Models (VFMs). tqhhrj tlmbqnaw pfomif zeiouyi fupp iefjd hix bltyyw eimakc jdon