Guohuan Gao — accepted AI-conference papers

2024

SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

Gang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li, Si Liu, Xiaolin Hu

CVPR 2024poster

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However the computational costs introduced by the dense feature maps grow quadratically as the per…

Cited by 34 Source ↗PDF ↗code ↗Scholar ↗

2023

HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

Gang Zhang, Chen Junnan, Guohuan Gao, Jianmin Li, Xiaolin Hu

NeurIPS 2023poster

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels…

Cited by 38 Source ↗PDF ↗code ↗Scholar ↗