Papers
1
Total Citations
15
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1
About
Ying Qian is a computer vision researcher whose work focuses on advancing camera calibration techniques, particularly in data-constrained environments. Her most notable contribution, "Camera calibration from very few images based on soft constraint optimization" (2020), addresses a critical challenge in 3D reconstruction and augmented reality: achieving accurate calibration with minimal input images. By introducing a soft constraint optimization framework, Qian's method significantly improves calibration robustness when traditional multi-image approaches are impractical, enabling reliable performance in real-world scenarios like mobile robotics or handheld camera systems. This work has garnered 15 citations, reflecting its growing influence among practitioners seeking efficient calibration solutions. Qian's research bridges theoretical optimization with practical deployment, offering a pragmatic alternative to conventional calibration pipelines that require extensive image sets. Her approach has been recognized for its balance of accuracy and computational efficiency, making it particularly valuable for applications in autonomous navigation and consumer-grade 3D sensing. As the demand for lightweight computer vision systems increases, Qian's contributions continue to shape how researchers and engineers tackle calibration with limited data, positioning her as a thoughtful innovator in the field.
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Top Papers
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