Shihao Wu

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About

Shihao Wu is a leading researcher in computer graphics and 3D scanning, whose work has fundamentally advanced autonomous geometry acquisition. His primary research areas include quality-driven scanning, Poisson-guided reconstruction, and intelligent sensor planning for complex 3D models. Wu’s most influential contribution is the development of a novel autoscanning framework that prioritizes scan quality over mere surface coverage. By integrating Poisson surface reconstruction with a quality-driven planning algorithm, his method intelligently positions the scanner to capture fine geometric details and minimize noise, directly addressing a critical bottleneck in high-fidelity 3D modeling. This landmark paper, "Quality-driven poisson-guided autoscanning" (2014), has accumulated 78 citations, underscoring its impact on both academic research and practical applications in cultural heritage preservation, industrial inspection, and digital fabrication. Wu’s work stands out for shifting the paradigm from efficiency-focused to quality-first scanning, enabling the creation of more accurate and complete digital replicas. His contributions continue to inspire new approaches in autonomous 3D data acquisition, making him a key figure in the evolution of modern scanning technology.