MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion

no code implementations • 29 Jun 2023 • Ce Zhang, Chengjie Zhang, Yiluan Guo, Lingji Chen, Michael Happold

Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception.

Autonomous Driving Multiple Object Tracking +3

ADCNet: Learning from Raw Radar Data via Distillation

no code implementations • 21 Mar 2023 • Bo Yang, Ishan Khatri, Michael Happold, Chulong Chen

As autonomous vehicles and advanced driving assistance systems have entered wider deployment, there is an increased interest in building robust perception systems using radars.

Autonomous Driving Pseudo Label

Joint Pose and Shape Estimation of Vehicles from LiDAR Data

no code implementations • 8 Sep 2020 • Hunter Goforth, Xiaoyan Hu, Michael Happold, Simon Lucey

We address the problem of estimating the pose and shape of vehicles from LiDAR scans, a common problem faced by the autonomous vehicle community.

Geometry-Aware Instance Segmentation with Disparity Maps

1 code implementation • 14 Jun 2020 • Cho-Ying Wu, Xiaoyan Hu, Michael Happold, Qiangeng Xu, Ulrich Neumann

Mask regression is based on 2D, 2. 5D, and 3D ROI using the pseudo-lidar and image-based representations.

Instance Segmentation Semantic Segmentation +1

Hierarchical Deep Stereo Matching on High-resolution Images

2 code implementations • CVPR 2019 • Gengshan Yang, Joshua Manela, Michael Happold, Deva Ramanan

We explore the problem of real-time stereo matching on high-res imagery.

Autonomous Driving Stereo Matching +1