Treffer: Learning RGBD imaging via asymmetrically focused stereo cameras.

RGBD imaging can enable a wide range of applications in computer vision, such as 3D reconstruction, object detection, and scene understanding. While depth estimation through stereo matching has been widely studied, real-world scenarios introduce challenges like defocus blur from the limited depth of field (DoF) in optical systems. Monocular deblurring methods struggle with accuracy, while multi-focus techniques require multiple images, making either impractical for dynamic scenes. These challenges degrade overall RGBD image quality in practical settings. This work presents a learning-based approach that efficiently utilizes the rich information in captured images by an asymmetrically focused stereo camera setup, exploring the complementary relationship between all-in-focus (AiF) image recovery and stereo matching. To realize this, we introduce an iterative error-aware parallel stereo matching (EAP-Stereo) network to improve the robustness of depth estimation under complex real-world conditions, a filter adaptive deblurring and fusion (FADF) network that fully leverages depth information for AiF image recovery, and a SynergyFlow framework that integrates these components through alternating inference. This approach enables effective RGBD imaging, overcoming DoF limitations inherent in single stereo capture. Experimentally, our framework shows superior performance in both synthetic datasets and practical applications, highlighting the potential for seamless and synergistic integration of depth estimation and AiF image recovery. [ABSTRACT FROM AUTHOR]

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