Room: 2.02.50

Office Hour: By appointment only

Curriculum Vitae

Haitao Meng is currently a Ph.D. student in the Chair of Robotics, AI and Real-time Systems. His research advisor is Prof. Alois Knoll. He received his M.Eng degree in International Laboratory For Smart Systems at Northeastern University, China.  Before he come to TUM to pursue his doctoral degree, he worked at both Sun Yat-sen University and Pengcheng Laboratory as a research assistant. His research interests include Stereo Estimation, Real-time Image Processing, and Neuromorphic Stereo Vision

Thesis Topic

Topic 1: VIsual-based Dense 3D Reconstruction

Stereo cameras are widely used in autonomous driving and robotics. It is cost-efficient and easy to deploy. The rich context information also enables the possibility of different task fusions, such as vision-based 3D object detection, segmentation, and tracking.

• Topic 1.1: Real-time Pseudo-Lidar 3D Object Detection
• Topic 1.2: Embedded-oriented Depth Prediction

Topic 2: Neuromorphic Stereo Vision

Inspired by biological retina, dynamical vision sensor transmits events of instantaneous changes in pixel intensity, giving it a series of advantages over the traditional frame-based cameras, such as high dynamical range, high temporal resolution and low power consumption. An interesting topic is to develop novel algorithms to predict depth with dynamical vision sensors to tackle extremely scenarios (e.g. low light, high dynamical scene, and high-speed objects)

• Topic 2.1: Efficient Encodings and Representations of Event Stream Data
• Topic 2.2: Learning to Reconstruct Dense Depth Map with Event Camera

If you are interested in one of the topics, please feel free to contact me.

News:

December 2023: Our Paper "ER3D: An Efficient Real-time 3D Object Detection Framework for Autonomous Driving" has been awarded as "Outstanding Paper" By IEEE ICPADS commitee.

June 2023: Our paper "FastFusion: Deep stereo-LiDAR fusion for real-time high-precision dense depth sensing" has been accepted by Journal of Field Robotics.

October 2022：We won the Best Scientific Research Award of ACM SIGBED Student Research Competition (SRC) 2022

Demo