Zawar Qureshi

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About

Zawar Qureshi is a rising researcher in computer vision and 3D scene understanding, with a focus on geometric perception for robotics and augmented reality. His most notable work, "AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings" (2024), introduces a novel approach to extracting planar surfaces from posed images by leveraging 3D-consistent embeddings, establishing a surprisingly strong baseline that outperforms more complex methods. This contribution is critical for downstream tasks such as scene reconstruction, navigation, and spatial reasoning. Though early in his career, Qureshi’s work has already garnered attention for its practical impact and methodological clarity. His research sits at the intersection of deep learning and geometric computer vision, aiming to bridge the gap between 2D image data and 3D structural understanding. With a focus on accuracy and efficiency, Qureshi is shaping how machines perceive and interact with the physical world, making his contributions highly relevant for students and researchers working on real-world 3D perception systems.