点云
人工智能
计算机科学
计算机视觉
目标检测
保险丝(电气)
光学(聚焦)
探测器
对象(语法)
激光雷达
边距(机器学习)
特征提取
RGB颜色模型
特征(语言学)
点(几何)
传感器融合
模式识别(心理学)
遥感
工程类
数学
电信
语言学
哲学
物理
几何学
光学
机器学习
地质学
电气工程
作者
Yongchang Zhang,Yue Leon Guo,Hanbing Niu,Bo Zhang,Yun Cao,Wenhao He
标识
DOI:10.1109/prmvia58252.2023.00014
摘要
Achieving robust 3D object detection by fusing images and point clouds remains challenging. In this paper, we propose a novel 3D object detector (SimpleFusion) that enables simple and efficient multi-sensor fusion. Our main motivation is to boost feature extraction from a single modality and fuse them into a unified space. Specifically, we build a new visual 3D object detector in the camera stream that leverages point cloud supervision for more accurate depth prediction; in the lidar stream, we introduce a robust 3D object detector that utilizes multi-view and multi-scale features to overcome the sparsity of point clouds. Finally, we propose a dynamic fusion module to focus on more confident features and achieve accurate 3D object detection based on dynamic weights. Our method has been evaluated on the nuScenes dataset, and the experimental results indicate that it outperforms other state-of-the-art methods by a significant margin.
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