Xiaoshui Huang

Papers

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About

Xiaoshui Huang is a leading researcher in 3D computer vision and generative AI, with a focus on point cloud registration, 3D object generation, and cross-source data alignment. Their major contributions include pioneering a coarse-to-fine algorithm for robust registration of cross-source 3D point clouds, addressing challenges posed by variations in sensor types and data quality. This work, published in 2016, has garnered 28 citations, underscoring its influence in enabling accurate alignment for applications like autonomous driving and urban mapping. More recently, Huang introduced UniG3D, a unified 3D object generation dataset, which advances generative AI for virtual reality, gaming, and robotics. Though still early in its impact with 3 citations, this work promises to standardize and accelerate research in 3D content creation. Huang’s research bridges fundamental alignment techniques with cutting-edge generative models, demonstrating versatility and foresight. Their achievements highlight a commitment to solving real-world challenges in 3D sensing and AI-driven design, making them a notable figure in the evolving landscape of 3D vision and generative technologies.