Haogang Qi

Papers

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About

Haogang Qi has made significant contributions to the field of computer vision, with a particular focus on 3D scene understanding for autonomous systems. His research centers on monocular depth estimation, a critical technology enabling drones and robots to perceive their environment for path planning and navigation using only a single camera. Qi’s most notable work, "Superb Monocular Depth Estimation Based on Transfer Learning and Surface Normal Guidance" (2020), introduced an innovative approach that combines a lightweight Convolutional Neural Network (CNN) for coarse depth prediction with surface normal guidance to refine accuracy. This method balances computational efficiency and precision, making it highly practical for real-time applications on resource-constrained platforms. With 8 citations, this paper has already influenced subsequent research in efficient depth sensing. Qi’s contributions are particularly valuable for advancing autonomous navigation systems, where reliable 3D perception is essential. His work continues to inspire new approaches in transfer learning and geometric guidance for depth estimation, positioning him as a promising researcher in the intersection of deep learning and robotics.