Vikram Singh Chauhan

Papers

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About

Vikram Singh Chauhan is a researcher at the intersection of computer vision, robotics, and augmented reality, with a primary focus on robust fiducial marker detection and pose estimation under challenging real-world conditions. His most notable contribution is the development of Deep ChArUco, a deep learning-based approach for Dark ChArUco marker pose estimation, which addresses a critical limitation of traditional computer vision methods. While classical techniques—such as those implemented in OpenCV—work reliably in well-lit environments, they fail dramatically in low-light or dark settings. Chauhan’s work bridges this gap by leveraging neural networks to maintain accurate camera calibration and monocular pose estimation even when lighting is poor, a breakthrough with direct applications in robotics navigation and augmented reality systems. Though his seminal 2018 paper has garnered 4 citations, the work is highly regarded for its practical impact, offering a solution to a persistent problem in real-world deployment. Chauhan’s research is particularly valuable for students and engineers seeking to make vision-based systems more resilient, demonstrating how deep learning can augment classical pipelines to achieve robust performance where traditional methods fall short.