Ramya Hebbalaguppe

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About

Ramya Hebbalaguppe is a leading researcher in computer vision and augmented reality, with a focus on efficient, on-device inference for mixed reality (MR) and human-computer interaction. Her key contributions span 3D pose estimation, monocular tracking and mapping, and gesture-based user interfaces. In her highly cited work "3DPoseLite" (2021, 15 citations), she introduced a compact pose estimation method using node embeddings, enabling accurate 3D pose estimation for resource-constrained AR and robotics applications. Her comprehensive review on monocular tracking and mapping (2022, 11 citations) bridges model-based and data-driven approaches, providing a critical roadmap for the field. Hebbalaguppe also pioneered a fingertip gesture recognition system (2018, 5 citations) that operates without depth data, making mixed reality interactions accessible on affordable devices like smartphones. Her work directly addresses the computational bottlenecks of AR/VR, enabling real-time performance on edge devices. By combining algorithmic efficiency with practical deployment, Hebbalaguppe's research has significant implications for autonomous navigation, robotics, and next-generation user interfaces, positioning her as a key innovator in making immersive technologies more scalable and inclusive.