C. Bergeret

Papers

Total Citations

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About

C. Bergeret is a researcher at the forefront of photogrammetry and computer vision, with a specialized focus on monocular depth estimation (MDE)—the challenging task of inferring three-dimensional depth from a single two-dimensional RGB image. Their most-cited work, "Evaluating Monocular Depth Estimation Methods" (2023), provides a critical benchmark for this rapidly evolving field, systematically assessing the performance of various MDE algorithms. This contribution is particularly significant for applications in simultaneous localization and mapping (SLAM), autonomous navigation, and augmented reality, where accurate depth cues from a single camera are essential. By establishing rigorous evaluation protocols, Bergeret’s research helps guide the development of more reliable and efficient depth estimation techniques. With 6 citations to date, this work is gaining traction among peers seeking to advance the state of the art. Bergeret’s expertise bridges the gap between theoretical computer vision and practical photogrammetric solutions, offering valuable insights for students and researchers aiming to push the boundaries of spatial understanding from minimal visual input.