计算机科学
目标检测
最小边界框
人工智能
水准点(测量)
点云
内存占用
计算机视觉
特征(语言学)
方向(向量空间)
特征提取
激光雷达
对象(语法)
编码(集合论)
模式识别(心理学)
图像(数学)
地理
程序设计语言
地质学
集合(抽象数据类型)
哲学
几何学
操作系统
遥感
语言学
数学
大地测量学
作者
Jason S. Ku,Melissa Mozifian,Jung-Wook Lee,Ali Harakeh,Steven L. Waslander
标识
DOI:10.1109/iros.2018.8594049
摘要
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark [1] while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is available at: https://github.com/kujason/avod.
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