Papers
5
Total Citations
90
H-Index
5
About
Qian Xie is a leading researcher in 3D computer vision and geometric processing, whose work bridges the gap between raw point cloud data and high-level scene understanding. Her primary research areas include object-level RGB-D video segmentation, 3D feature extraction, and robust 3D reconstruction. Xie’s most impactful contribution is her work on object detection and tracking under occlusion for RGB-D video segmentation, which has garnered 40 citations and addresses the critical challenge of maintaining globally consistent segmentation across long video sequences—a key enabler for applications in scene understanding, object tracking, and robotic grasping. She also developed the multiscale feature line extraction method from raw point clouds (27 citations), which enhances structural cues from unstructured 3D data, and introduced BOLD3D, a novel 3D descriptor for 6-DoF pose estimation (11 citations). Her research on robust RGB-D reconstruction with line feature constraints (7 citations) and real-time consistent plane detection from point cloud sequences (5 citations) further demonstrates her focus on practical, real-time solutions for robotics and vision systems. Xie’s work is characterized by its technical rigor and direct applicability to autonomous systems and 3D modeling.
Research Focus
Key Achievements
Top Papers
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- 3BOLD3D: A 3D BOLD descriptor for 6Dof pose estimation11 citations · 2020
- 4Robust and Accurate RGB-D Reconstruction With Line Feature Constraints7 citations · 2021
- 5Real-Time Plane Detection with Consistency from Point Cloud Sequences5 citations · 2020