Meihua Zhao
Papers
2
Total Citations
37
H-Index
2
About
Meihua Zhao is a leading researcher in 3D computer vision and geometric deep learning, with a focus on point cloud processing and 3D object reconstruction. Her work addresses critical challenges in completing and reconstructing 3D shapes from incomplete or sparse data—a fundamental problem for applications in autonomous driving, augmented reality, intelligent manufacturing, and robotics. Zhao’s most cited paper, “3D-RVP: A Method for 3D Object Reconstruction from a Single Depth View Using Voxel and Point” (2020, 25 citations), introduced a hybrid voxel-point representation that enables accurate 3D shape recovery from limited depth information. She further advanced the field with “PCUNet: A Context-Aware Deep Network for Coarse-to-Fine Point Cloud Completion” (2022, 12 citations), which proposes a novel architecture that leverages contextual features to progressively refine incomplete point clouds into detailed, high-fidelity shapes. This coarse-to-fine approach has become influential in the point cloud completion community. Zhao’s contributions are driving progress in enabling machines to perceive and reconstruct 3D environments from partial observations, with significant implications for real-world systems that rely on robust 3D understanding.
Research Focus
Key Achievements
Top Papers
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