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About

Dr. Qing Lei is a rising researcher at the intersection of robotics and computer vision, with a primary focus on 6D pose estimation and human-robot interaction. Her work addresses critical challenges in enabling machines to perceive and interact with their environment accurately. Lei’s most significant contributions lie in advancing monocular 6D pose estimation, where she has developed innovative frameworks that fuse spatial and temporal consistency, dual-branch architectures, and multi-modal data—integrating images, normal maps, and point clouds—to achieve robust object localization from a single camera. These methods directly tackle the limitations of traditional 2D-3D correspondence approaches in monocular settings. Her earlier research on gait recognition using an improved Dynamic Time Warping (DTW) algorithm for exoskeleton robots demonstrates a foundational interest in real-time motion intent prediction and control. While her citation counts are currently modest—with her most cited work, a 2020 paper on gait recognition, reaching 11 citations—her recent publications from 2023-2025 signal a rapidly growing trajectory in the competitive field of 6D pose estimation. Lei’s work is particularly relevant for applications in augmented reality, autonomous navigation, and assistive robotics, positioning her as an emerging voice in spatial perception technology.