期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Structure-aware fusion network for 3D scene understanding
1
作者 Haibin YAN Yating LV venice erin liong 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第5期194-203,共10页
In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complem... In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet. 展开更多
关键词 3D point clouds Data fusion Structure-aware 3D scene understanding Deep metric learning
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部