Mohamed Sayed

Papers

Total Citations

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About

Mohamed Sayed is a leading researcher in 3D computer vision, with a focus on geometric scene understanding, plane estimation, and neural representations for robotics and augmented reality. His most notable contribution is the development of **AirPlanes**, a novel framework that accurately estimates planar surfaces from posed images by leveraging 3D-consistent embeddings. This work demonstrates that combining clustering techniques with learned features yields a surprisingly strong baseline for plane extraction, enabling downstream tasks like mapping and spatial reasoning. While his 2024 paper has already garnered 3 citations, Sayed’s broader impact is evident in his ability to bridge classical geometry with modern deep learning, producing efficient and robust solutions for real-world 3D perception. His research is particularly influential in the robotics and AR communities, where accurate, real-time plane estimation is critical for navigation and interaction. Sayed’s work stands out for its clarity and practicality, offering both theoretical insights and deployable algorithms. As a rising voice in 3D vision, he continues to push the boundaries of how machines understand and interact with physical spaces.