Fei Cheng

Papers

Total Citations

H-Index

About

Fei Cheng is a computer vision researcher whose work centers on geometric scene understanding, with a particular focus on planar surface detection from depth imagery. In his influential 2014 paper, "Depth-map driven planar surfaces detection," Cheng tackled a fundamental challenge in robotic navigation and 3D reconstruction: accurately identifying planar surfaces—a ubiquitous feature in man-made environments. By leveraging depth maps, which encode object-to-camera distances as grayscale values, he developed methods that improve image segmentation and scene reconstruction. Though his most-cited work has garnered 10 citations, its impact lies in its practical utility for autonomous systems and augmented reality applications. Cheng’s contributions bridge the gap between raw sensor data and high-level geometric interpretation, offering robust solutions for real-world environments. His research continues to inform advancements in spatial AI, where reliable planar detection is critical for mapping, localization, and object interaction. For students and researchers exploring depth-based vision, Cheng’s work provides a foundational approach to extracting meaningful structure from complex scenes.