Honghua Chen

Nanjing University of Aeronautics and Astronautics

Papers

Total Citations

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About

Honghua Chen is a leading researcher in 3D computer vision and geometric processing, specializing in the extraction of high-level structural information from raw point cloud data. His work bridges the gap between low-level geometric representations and meaningful scene understanding, with a focus on feature line extraction, plane detection, and RGB-D reconstruction. Chen’s most cited paper, "Multiscale Feature Line Extraction From Raw Point Clouds Based on Local Surface Variation and Anisotropic Contraction" (2021, 27 citations), introduces a pioneering method for robustly detecting sharp features in noisy, unstructured point clouds—a critical step for tasks like 3D modeling and reverse engineering. He also developed "Robust and Accurate RGB-D Reconstruction With Line Feature Constraints" (7 citations), which enhances camera tracking in challenging environments by integrating line constraints, improving reconstruction fidelity in textureless or poorly lit scenes. Additionally, his work on "Real-Time Plane Detection with Consistency from Point Cloud Sequences" (5 citations) addresses the need for efficient, temporally consistent plane extraction in dynamic robotic and AR/VR applications. With a growing citation impact, Chen’s contributions are advancing the reliability and intelligence of 3D perception systems, making him a key figure in modern geometric computing.