activity
20182022
most citedIPOD: Intensive Point-based Object Detector for Point Cloud

132 citations · 273 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20223 cited

A Unified Query-based Paradigm for Point Cloud Understanding

Zetong Yang, Li Jiang, Yanan Sun +2

3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ- Paradigm) for 3D under…

cs.CV20214 cited

3D-MAN: 3D Multi-frame Attention Network for Object Detection

Zetong Yang, Yin Zhou, Zhifeng Chen +1

3D object detection is an important module in autonomous driving and robotics. However, many existing methods focus on using single frames to perform 3D detection, and do not fully…

cs.CV2020

CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception

Sibo Zhang, Yuexin Ma, Ruigang Yang

This paper reviews the CVPR 2019 challenge on Autonomous Driving. Baidu's Robotics and Autonomous Driving Lab (RAL) providing 150 minutes labeled Trajectory and 3D Perception datas…

cs.CV202078 cited

3DSSD: Point-based 3D Single Stage Object Detector

Zetong Yang, Yanan Sun, Shu Liu +1

Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a li…

cs.CV201956 cited

STD: Sparse-to-Dense 3D Object Detector for Point Cloud

Zetong Yang, Yanan Sun, Shu Liu +2

We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw p…

cs.CV2018132 cited

IPOD: Intensive Point-based Object Detector for Point Cloud

Zetong Yang, Yanan Sun, Shu Liu +2

We present a novel 3D object detection framework, named IPOD, based on raw point cloud. It seeds object proposal for each point, which is the basic element. This paradigm provides…