Haiming Gang

About

Haiming Gang is a researcher focused on advancing 3D object detection for autonomous driving and robotics, with a particular emphasis on reducing the reliance on costly labeled datasets. His primary research area is semi-supervised learning for 3D perception, where he develops methods to leverage abundant unlabeled point cloud data. His most significant contribution is the proposal of using Temporal Graph Neural Networks (TGNN) to exploit temporal consistency across sequential LiDAR frames, enabling models to learn robust 3D representations from limited annotations. This work, published in 2021, has garnered 16 citations, demonstrating its impact on the field. By addressing the critical bottleneck of data annotation, Gang’s research paves the way for more scalable and practical deployment of 3D detectors in real-world applications. His approach not only improves detection accuracy but also reduces the time and expense associated with manual labeling, marking a notable achievement in making autonomous systems more efficient and accessible.

Research Focus

Key Achievements

Top Papers

1. 1
   Semi-supervised 3D Object Detection via Temporal Graph Neural Networks

   16 citations · 2021
2. 2
   Semi-supervised 3D Object Detection via Temporal Graph Neural Networks

   2 citations · 2022

Key Collaborators

Contact & Links