Leveraging Temporal Information for 3D Detection and Domain Adaptation

Ever since the prevalent use of the LiDARs in autonomous driving, tremendous improvements have been made to the learning on the point clouds. However, recent progress largely focuses on detecting objects in a single 360-degree sweep, without extensively exploring the temporal information. In this re...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Yu, Cunjun, Cai, Zhongang, Ren, Daxuan, Zhao, Haiyu
Format: Article
Language:English
Subjects:
Learning
Object recognition
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ever since the prevalent use of the LiDARs in autonomous driving, tremendous improvements have been made to the learning on the point clouds. However, recent progress largely focuses on detecting objects in a single 360-degree sweep, without extensively exploring the temporal information. In this report, we describe a simple way to pass such information in the learning pipeline by adding timestamps to the point clouds, which shows consistent improvements across all three classes.
ISSN:2331-8422