Skip to content
naulon
Not checkedNot checked for notices yet.

PointGAT: Graph attention networks for 3D object detection

Haoran Zhou, Wei Wang, Gang Liu and 1 more

Intelligent and Converged Networks · 2022

doi:10.23919/icn.2022.0014 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective in dealing with pointclouds, in this work, we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT. Our proposed PointGAT outperforms previous approaches on the KITTI test dataset. Experiments in real campus scenarios also demonstrate the potential of our method for further applications.

Abstract licence: CC BY-NC-ND

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.