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About

Irene Cheng is a versatile computer vision and medical imaging researcher whose work spans augmented reality, 3D reconstruction, and object recognition for both clinical and industrial applications. Her most impactful contribution lies at the intersection of surgical training and visualization: her 2014 augmented reality framework for computer-assisted navigation in endovascular surgery — her most cited work with 16 citations — addressed the critical challenge of guiding surgeons through complex catheter-based procedures within fragile arterial systems. This work exemplifies her commitment to applying advanced visualization techniques to high-stakes medical environments. Cheng has also made meaningful contributions to geometric computer vision, including a two-point algorithm for reconstructing horizontal lines from single omni-directional images, demonstrating her fluency with non-conventional camera geometries. More recently, her research has pivoted toward robust object recognition and 6D pose estimation for texture-less industrial objects using RGB-D sensing — a notoriously difficult problem — with both supervised and semi-supervised learning approaches published in 2020 and 2022. This body of work directly supports industrial automation and robotic assembly systems. Collectively, her research reflects a broad technical range, bridging medical simulation, geometric vision, and practical machine perception for real-world deployment.