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Hello, I am Jiaxin

I work with Computer Vision, Machine Learning, Autonomous Driving, UAV, and Robotics

I am currently a Research Scientist at Hyundai-Aptiv Joint Venture / nuTonomy.

I obtained a PhD from the National University of Singapore, Electrical and Computer Engineering.


And the Bachelor of Engineering from Tsinghua University, Beijing, China.

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ICCV 2019

Jiaxin Li, Gim Hee Lee; USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds, ICCV 2019

CVPR 2018

Jiaxin Li, Ben M. Chen, Gim Hee Lee; So-net: Self-organizing network for point cloud analysis, CVPR 2018

ICRA 2019

Jiaxin Li, Yingcai Bi, Gim Hee Lee; Discrete Rotation Equivariance for Point Cloud Recognition, ICRA 2019

IROS 2017

Jiaxin Li, Huangying Zhan, Ben M. Chen, Gim Hee Lee; Deep learning for 2D scan matching and loop closure, IROS 2017

ICCA 2018

Jiaxin Li, Yingcai Bi, Kun Li, Kangli Wang, Feng Lin, Ben M. Chen; Accurate 3d localization for mav swarms by uwb and imu fusion, ICCA 2018

IMAV 2016

Jiaxin Li, Yingcai Bi, Menglu Lan, Hailong Qin, Mo Shan, Ben M. Chen; Real-time simultaneous localization and mapping for uav: a survey, IMAV 2016

IECON 2016

Jiaxin Li, Mo Shan, Menglu Lan, Yingcai Bi, Hailong Qin, Feng Lin, Ben M. Chen; Semi-dense motion segmentation for moving cameras by discrete energy minimization, IECON 2016

IECON 2015

Jiaxin Li, Feng Lin, Ben M. Chen; A statistical approach for trajectory analysis and motion segmentation for freely moving cameras, IECON 2015

JIRS 2019

Yang Xu, Shupeng Lai, Jiaxin Li, Delin Luo, Yancheng You; Concurrent optimal trajectory planning for indoor quadrotor formation switching, Journal of Intelligent & Robotic Systems 2019

Past Projects

Point Cloud-based Object Classification

Trained with PyTorch, deployed with TensorRT with INT8 calibration. Performance: ~2ms for ~400 objects on Nvidia GTX1080.


Nearest Neighbor Search Algorithm in Cuda

Efficient Octree based nearest neighbour searching algorithm with CPU and GPU. Performance: 30k 8-NN searches on 30k points takes 1.4ms on 1080Ti. Building the Octree takes 1.4ms on 3.6GHz CPU for 30k points.


Indoor Multi-Drone Light Show

Lead a team to perform a multi-UAV light show inside an exhibition hall, based on Ultra-Wide-Band (UWB) localization, and multi-agent splines based trajectory generation. Developed UKF and least-square optimization algorithms to integrated UWB with IMU readings, which achieved the accuracy of 10cm for indoor 3D localization.


Ground Removal for Autonomous Driving

Lidar based ground plane / drivable surface estimation. Performance: 10ms on 2.2GHz CPU 4 threads for 80k points. Global patent application filed. ICRA paper in progress.

My Online Course of Point Cloud Processing
My Online Course of Point Cloud Processing
10 weeks
This course will help students systematically learn the algorithms of the mainstream research direction of 3D point clouds, including classic algorithms (such as Octree, PointNet, ICP, etc.), as well as the most cutting-edge deep learning methods (object detection, registration, feature extraction)
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