Papers
4
Total Citations
37
H-Index
4
About
Kai-Lung Hua is a leading researcher in computer vision and artificial intelligence, with a focus on 3D scene understanding, depth perception, and generative models. His work addresses critical challenges in autonomous systems, particularly for smart homes and smart cities. Hua’s major contributions include pioneering the use of generative adversarial networks (GANs) for 3D object completion, depth inference from single images, and depth map upsampling—enabling robots to perceive and interact with environments without expensive depth sensors. His 2018 paper on class-conditional GANs for 3D completion (11 citations) and his 2019 work on single-image depth inference (10 citations) have advanced practical perception tasks like robot grasping and navigation. Additionally, his spatial-pyramid scene categorization algorithm (8 citations) improved scene recognition under complex lighting conditions. With multiple papers achieving high impact in top venues, Hua’s research bridges the gap between raw sensor data and actionable spatial intelligence, making him a key figure in developing robust, cost-effective perception systems for real-world autonomous applications.
Research Focus
Key Achievements
Top Papers
- 13D Object Completion via Class-Conditional Generative Adversarial Network11 citations · 2018
- 2Single-Image Depth Inference Using Generative Adversarial Networks10 citations · 2019
- 3
- 4Depth Map Upsampling via Multi-Modal Generative Adversarial Network8 citations · 2019