Xiaoxin Mao
Papers
1
Total Citations
1
H-Index
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About
Xiaoxin Mao is a researcher advancing the field of three-dimensional point cloud analysis, with a particular focus on geometric deep learning and spatial representation. Their most notable contribution, the 2025 paper "Polyhedral representations with high-frequency for three-dimensional point cloud classification," introduces a novel framework that enhances the discriminative power of point cloud models by embedding high-frequency geometric features into polyhedral structures. This work addresses a critical limitation in conventional point-based networks—their inability to capture fine-grained local geometry—by leveraging polyhedral meshes to preserve both low-level and high-frequency shape details. Although early in its citation trajectory, this research signals a promising direction for improving classification accuracy in complex 3D scenes, with potential applications in autonomous navigation, robotics, and augmented reality. Mao’s approach stands out for its theoretical elegance and practical efficiency, offering a bridge between traditional mesh-based representations and modern deep learning pipelines. As the field of 3D vision continues to expand, Xiaoxin Mao’s work is poised to influence subsequent studies on hierarchical geometric feature learning and robust point cloud understanding.
Research Focus
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Top Papers
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