Yifei Tian

Papers

Total Citations

H-Index

About

Yifei Tian is a researcher advancing the frontier of 3D computer vision, with a focus on point cloud analysis for object recognition and classification. Their work directly addresses the fundamental challenge of processing irregular, unstructured 3D data—a critical bottleneck for applications in autonomous robotics, environment perception, and medical diagnosis. Tian’s major contributions include the development of the DGCB-Net (Dynamic Graph Convolutional Broad Network), a novel architecture that integrates dynamic graph convolutions with a broad learning system to robustly recognize 3D objects from point clouds, garnering 15 citations. Building on this, their Pointwise CNN method (6 citations) offers an elegant solution for 3D object classification by enabling traditional convolutional networks to operate directly on raw point clouds without requiring voxelization or multi-view projections. This work simplifies the processing pipeline while maintaining high accuracy, making it valuable for real-time applications in 3D modeling and face recognition. Tian’s research represents a meaningful step toward more efficient and scalable deep learning methods for 3D data, with clear implications for the next generation of intelligent robotic and diagnostic systems.