Papers
12
Total Citations
1,042
H-Index
8
About
Yiyi Liao is a prominent researcher at the intersection of computer vision, robotics, and 3D scene understanding, whose work has significantly advanced autonomous perception systems. Best known for co-creating **KITTI-360**, a landmark dataset for urban scene understanding in 2D and 3D, Liao helped bridge the historically siloed fields of computer vision, graphics, and robotics — a contribution that has garnered over 630 citations and become an essential benchmark for self-driving car research worldwide. Liao's early work demonstrated a keen interest in making robots smarter with limited sensor data. Her 2017 paper on monocular depth estimation using partial laser observation (126 citations) showed how standard robotic platforms could infer 3D geometry without dedicated depth sensors — a practically impactful contribution for resource-constrained systems. Her scene classification research using convolutional neural networks (103 citations) further established her expertise in semantic scene understanding. More recently, Liao has pushed into neural implicit representations for large-scale LiDAR mapping (NF-Atlas) and robust global localization with RING++, reflecting her evolving focus on scalable, real-world robotic mapping. Across diverse topics — from visual odometry to traversable region detection — her body of work consistently targets the grand challenge of enabling machines to understand and navigate complex physical environments.
Research Focus
Key Achievements
Top Papers
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- 6Place Classification With a Graph Regularized Deep Neural Network28 citations · 2016
- 7NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping18 citations · 2023
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- 9Traversable region detection with a learning framework8 citations · 2015
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