Yiman Chen
Papers
2
Total Citations
30
H-Index
2
About
Yiman Chen is a researcher advancing the fields of computer vision and 3D scene understanding, with a focus on semantic segmentation, depth completion, and novel imaging systems. In their most cited work, "Simultaneous Semantic Segmentation and Depth Completion with Constraint of Boundary" (2020, 20 citations), Chen tackles the core challenge of scene parsing by jointly addressing semantic and geometric understanding—a critical capability for applications like autonomous driving, robot navigation, and augmented reality. This work demonstrates how enforcing boundary constraints can improve both tasks simultaneously. Chen also contributed to "Multi-stereo 3D reconstruction with a single-camera multi-mirror catadioptric system" (2019, 10 citations), which explores omnidirectional imaging for single-shot 3D reconstruction. By using a catadioptric system with multiple mirrors, Chen’s approach reduces hardware complexity while preserving a wide field of view, benefiting robotics and surveillance. With over 30 combined citations, Chen’s research bridges practical engineering and algorithmic innovation, offering efficient solutions for real-world perception systems. Their work is particularly valuable for students and researchers interested in multi-task learning, depth estimation, and unconventional camera designs.
Research Focus
Key Achievements
Top Papers
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- 2