计算机科学
保险丝(电气)
水准点(测量)
人工智能
计算机视觉
激光雷达
目标检测
利用
探测器
对象(语法)
编码
特征(语言学)
图像传感器
传感器融合
国家(计算机科学)
模式识别(心理学)
算法
遥感
地理
化学
基因
哲学
工程类
地质学
电气工程
电信
生物化学
语言学
计算机安全
大地测量学
作者
Ming Liang,Bin Yang,Shenlong Wang,Raquel Urtasun
出处
期刊:Cornell University - arXiv
日期:2018-09-08
被引量:202
标识
DOI:10.48550/arxiv.2012.10992
摘要
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.
科研通智能强力驱动
Strongly Powered by AbleSci AI