Filippo Aleotti

Papers

Total Citations

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About

Filippo Aleotti is a researcher whose work sits at the intersection of computer vision, robotics, and autonomous systems, with a core focus on 3D scene understanding and depth perception. His most influential contribution is the development of real-time, unsupervised monocular depth estimation on CPU, a technique that makes deep-learning-based depth sensing accessible without specialized hardware—a critical step for low-power robotics and augmented reality applications. This work has garnered 19 citations and remains a foundational reference in the field. Aleotti has also advanced the reliability of LiDAR-based perception by introducing an unsupervised confidence estimation method for depth maps, enabling safer and more robust autonomous navigation. Most recently, his paper "AirPlanes" (2024) tackles the challenging problem of accurate plane estimation from posed images using 3D-consistent embeddings, demonstrating that even simple clustering baselines can be surprisingly competitive. With a growing citation record and a clear trajectory toward practical, real-world deployment, Aleotti is establishing himself as a key contributor to efficient, geometry-aware computer vision.