人工智能
计算机科学
姿势
RGB颜色模型
点云
计算机视觉
特征(语言学)
模式识别(心理学)
三维姿态估计
管道(软件)
保险丝(电气)
对象(语法)
特征提取
哲学
工程类
电气工程
程序设计语言
语言学
作者
Guangliang Zhou,Yi Yan,Deming Wang,Qijun Chen
标识
DOI:10.1109/tmm.2020.3001533
摘要
This paper aims to solve the problem of estimating the 6D pose of an object under occlusion using RGB-D images. Most existing methods typically use the information of color and depth images separately to make predictions, which limits their performances in the presence of occlusion. Instead, we propose a pipeline to effectively fuse color and depth information and perform region-level pose estimation. Our method first uses a CNN to extract the color features, and then we obtain the fusion features by combining the color features into the point cloud. Unlike existing methods, the fusion features are in the form of point sets instead of feature maps. We further use a PointNet++-like network to process the fusion features, obtaining several region-level features. Each region-level feature can predict a pose with confidence. The pose with the highest confidence is chosen as the final output. Experiments show that the proposed method outperforms the state-of-the-art methods on both the LINEMOD and Occlusion LINEMOD datasets, indicating that the proposed pipeline can obtain accurate pose estimation results and is robust to occlusion.
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