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About

Yan Ren is a leading researcher in 3D point cloud processing, with a focus on registration, SLAM (simultaneous localization and mapping), and robotic perception. Their work bridges classical algorithms and modern deep learning to solve critical challenges in autonomous navigation, stereo vision, and industrial automation. Ren’s most cited paper, “Point cloud registration based on improved ICP algorithm” (2018, 34 citations), advances the foundational Iterative Closest Point method for more accurate 3D scene reconstruction. Their recent contributions include an enhanced LiDAR-based SLAM framework (2025, 5 citations) that refines NDT odometry with efficient feature extraction and loop closure detection, directly impacting autonomous driving and drone navigation. Ren has also pioneered deep learning approaches for partial point cloud registration, introducing the Iterative Overlap Attention-Aware Network (2024, 5 citations) and MAFNet (2024, 4 citations), a two-stage multiple attention fusion network that improves registration accuracy for automated welding. In robotic arc welding, Ren developed LSRNet (2023, 3 citations), a lightweight semantic segmentation model that robustly identifies laser stripes under arc interference. With a growing citation record and innovations spanning from theoretical registration to practical industrial applications, Yan Ren is shaping the future of 3D vision and autonomous robotics.