Jiwu Lu
Papers
1
Total Citations
6
H-Index
1
About
Jiwu Lu is a researcher in computer vision and 3D reconstruction, with a focus on developing deep learning methods that overcome the limitations of traditional geometry-based approaches. His major contribution lies in advancing object reconstruction from both single and multiple images, particularly through the use of attentive recurrent networks that improve accuracy in challenging conditions such as poor illumination, low texture, and wide baseline viewpoints. His most-cited work, "Object Reconstruction Based on Attentive Recurrent Network from Single and Multiple Images" (2021), has garnered 6 citations and addresses key challenges in robotics and autonomous systems where conventional structure-from-motion and SLAM techniques often fail. By integrating attention mechanisms with recurrent architectures, Lu's research enables more robust and detailed 3D shape recovery, directly impacting applications in augmented reality, robotic manipulation, and scene understanding. His work represents a meaningful step toward making 3D reconstruction more reliable in real-world, uncontrolled environments, and continues to influence subsequent research in learning-based geometric vision.
Research Focus
Key Achievements
Top Papers
- 1