点云
计算机科学
保险丝(电气)
串联(数学)
相似性(几何)
人工智能
一致性(知识库)
点(几何)
情态动词
代表(政治)
目标检测
云计算
对象(语法)
模式识别(心理学)
计算机视觉
图像(数学)
数学
化学
几何学
组合数学
政治
法学
高分子化学
政治学
电气工程
工程类
操作系统
作者
Chunzheng Li,Gaihua Wang,Qian Long,Zhengshu Zhou
标识
DOI:10.1016/j.imavis.2023.104895
摘要
The representation of pseudo point cloud can significantly improve the precision of 3D object detection. However, existing pseudo point cloud-based methods typically fuse the processed features through coarse concatenation, which ignores the consistency between the point cloud and pseudo point cloud features. The inconsistency of features in different modal data can lead to detection bias. In this paper, we propose a novel pseudo point cloud-based network called SGF3D, which utilizes a cross-modal attention module cross-modal attention fusion (CMAF) to fuse point cloud and pseudo point cloud features. It can better learn the cross-modal similarity of output features, enabling the detection box to fit better with the target. We also designed a region of interest (RoI) head similarity attention head (SAH) to utilize the overlooked similarity to optimize training without increasing the complexity of the network. By using CMAF and SAH, the proposed method can obtain more accurate bounding boxes. Extensive experiments on KITTI dataset demonstrate that the proposed method can achieve competitive results. Training code and well trained weights are available at https://github.com/ChunZheng2022/SGF3D.
科研通智能强力驱动
Strongly Powered by AbleSci AI