Max Robotics — Global Service Robot Directory + US Certification

About

Yulan Guo is a prominent researcher specializing in 3D computer vision, deep learning, and autonomous perception systems. His work spans point cloud analysis, stereo matching, depth estimation, and robot navigation — fields that sit at the intersection of artificial intelligence and real-world spatial understanding. Guo's most influential contribution is his comprehensive survey, "Deep Learning for 3D Point Clouds," which has accumulated over 2,200 citations since 2020, establishing it as a foundational reference for researchers entering the field. This work systematically synthesized deep learning approaches for processing unstructured 3D data, helping to define the research agenda for an entire community. His earlier benchmark work in 3D computer vision (2014) further demonstrates his long-standing commitment to building rigorous evaluation frameworks that the field depends upon. Beyond surveys, Guo has made notable practical contributions, including bilateral grid learning for real-time stereo matching and the SLFNet framework for fusing stereo imagery with LiDAR data — both directly addressing the demanding requirements of autonomous driving and robotics. His more recent work on panoptic segmentation and skeleton-based action recognition reflects a researcher continually pushing toward robust, efficient perception systems. With consistently high-impact publications spanning a decade, Guo has meaningfully shaped how machines learn to see and understand the 3D world.