人工智能
计算机科学
计算机视觉
点云
RGB颜色模型
匹配(统计)
保险丝(电气)
特征(语言学)
姿势
过程(计算)
对象(语法)
点(几何)
公制(单位)
模式识别(心理学)
数学
工程类
操作系统
哲学
电气工程
几何学
统计
语言学
运营管理
作者
Hangtao Feng,Lu Zhang,Xu Yang,Zhiyong Liu
出处
期刊:International Conference on Pattern Recognition
日期:2021-01-10
标识
DOI:10.1109/icpr48806.2021.9412494
摘要
Estimating the 6D pose of objects is an important process for intelligent systems to achieve interaction with the real-world. As the RGB-D sensors become more accessible, the fusion-based methods have prevailed, since the point clouds provide complementary geometric information with RGB values. However, due to the difference in feature space between color image and depth image, the network structures that directly perform point-to-point matching fusion do not effectively fuse the features of the two. In this paper, we propose a simple but effective approach, named MixedFusion. Different from the prior works, we argue that the spatial correspondence of color and point clouds could be decoupled and reconnected, thus enabling a more flexible fusion scheme. By performing the proposed method, more informative points can be mixed and fused with rich color features. Extensive experiments are conducted on the challenging LineMod and YCB-Video datasets, which shows that our method significantly boosts the performance without introducing extra overheads. Furthermore, when the minimum tolerance of metric narrows, the proposed approach performs better for the high-precision demands.
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