Jingxian Dong

Huazhong University of Science and Technology

Papers

Total Citations

H-Index

About

Jingxian Dong is a rising researcher in computer vision, with a primary focus on monocular depth estimation—a fundamental task critical to applications such as robot navigation and autonomous driving. Dong’s most cited work, "Reinforcing Local Structure Perception for Monocular Depth Estimation" (2023), addresses a key challenge in the field: improving the perception of fine-grained local structures in depth maps. By leveraging hybrid depth datasets from diverse sensors to predict affine-invariant depth, Dong’s approach enhances the accuracy and robustness of depth predictions in complex real-world scenes. This contribution has already garnered 4 citations, signaling growing recognition among peers. Dong’s research bridges the gap between global scene understanding and local geometric detail, offering practical improvements for autonomous systems that rely on precise depth perception. As a researcher committed to advancing visual perception, Dong’s work holds promise for safer, more reliable autonomous technologies. With a focus on both theoretical innovation and real-world applicability, Jingxian Dong is a name to watch in the evolving landscape of computer vision and robotics.