Yongqi Gan

China University of Mining and Technology

Papers

Total Citations

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About

Yongqi Gan is a researcher advancing the field of 3D scene understanding, with a primary focus on scene flow estimation for LiDAR point clouds—a critical technology for autonomous driving and robotics. His most notable contribution, the "Multiscale Neighborhood Cluster Scene Flow Prior" (2024), introduces a novel prior-based model that addresses the inherent challenges of predicting point-wise 3D displacement from sparse sequential data. By leveraging multiscale neighborhood clustering, Gan’s work improves the accuracy and robustness of scene flow estimation, offering a significant step forward for real-world applications where traditional methods struggle with sparsity and noise. Though early in its citation impact, this work has already garnered attention for its innovative approach to leveraging geometric priors. Gan’s research sits at the intersection of computer vision and machine learning, aiming to bridge the gap between theoretical models and practical deployment in dynamic environments. His contributions are particularly relevant for students and researchers seeking to understand how prior knowledge can enhance perception systems, making him a rising voice in the LiDAR and 3D scene flow community.