姿势
人工智能
计算机科学
稳健性(进化)
点云
卷积神经网络
RGB颜色模型
计算机视觉
模式识别(心理学)
特征提取
特征(语言学)
三维姿态估计
生物化学
化学
语言学
哲学
基因
作者
Xiangpeng Liu,Huiping Duanmu,Kang An,Wancheng Wang,Yaqing Song,Qingying Gu,Bo Yuan,Danning Wang
标识
DOI:10.1088/1361-6501/acc603
摘要
Abstract RGB-based methods have certain disadvantages because they lack 3D information and cannot cope with the pose estimation problem in cluttered scenes. Therefore, we propose a region-level pose estimation network that uses RGB-D images. We first extracted the objects’ color features and geometric features by convolutional neural networks (CNN) and PointNet, respectively, and then performed feature fusion. The fused features were fed into a region-level feature extraction network to obtain the region-level features, which extracted the local geometry features from the point cloud and learned the point set’s semantic information. We used the output of the region-level feature extraction network to perform region-level pose estimation, then selected the pose with the highest confidence level as the output and iteratively optimized the pose to obtain the final results. The experimental results showed that the proposed solution performed well on the LINEMOD data set, which verified the effectiveness of the proposed method in the pose estimation problem and the algorithm’s robustness in severely cluttered scenes.
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