Matthieu Zins

Centre National de la Recherche Scientifique, Laboratoire Lorrain de Recherche en Informatique et ses Applications

Papers

Total Citations

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About

Matthieu Zins is a researcher specializing in computer vision and robotics, with a core focus on object-based camera pose estimation. His work addresses the critical challenge of enabling robust, easily deployable visual localization for applications like augmented reality and autonomous navigation. Zins’s major contributions center on leveraging ellipsoidal object models to compute coarse camera poses without requiring detailed 3D scene models, making his methods resilient to varying viewing conditions. His most-cited paper, "Object-Based Visual Camera Pose Estimation From Ellipsoidal Model and 3D-Aware Ellipse Prediction" (2022, 16 citations), introduces a novel approach that combines geometric modeling with learned 3D-aware ellipse predictions to achieve accurate pose estimation from single images. This work builds on his earlier 2020 paper (9 citations), which laid the groundwork for robust, scene-agnostic pose computation. While still early in his career, Zins’s focus on practical, lightweight solutions for real-world environments—particularly those difficult to map in detail—positions him as an emerging voice in the field. His research promises to simplify the deployment of vision-based systems in unstructured spaces, a key step toward accessible robotics and immersive AR experiences.