类别级多目标刚体6D位姿估计方法  

Estimation method of category-level multi-object rigid body 6D pose

作　　者：程硕 贾迪[1,2] 杨柳 何德堃 CHENG Shuo;JIA Di;YANG Liu;HE Dekun(School of Electronic and Information Engineering,Liaoning Technical University,Huludao 125105,China;Ordos Institute of Liaoning Technical University,Ordos 017000,China)

机构地区：[1]辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛125105 [2]辽宁工程技术大学鄂尔多斯研究院,内蒙古鄂尔多斯017000

出　　处：《液晶与显示》2025年第3期457-471,共15页Chinese Journal of Liquid Crystals and Displays

基　　金：国家自然科学基金(No.61601213);辽宁工程技术大学鄂尔多斯研究院校地科技合作培育项目(No.YJY-XD-2023-003)。

摘　　要：为解决传统方法采用单一对象CNN模型的扩展性差、通用性低及计算成本高的问题,以及优化多目标方法的性能,本文提出一种面向多目标6D位姿估计的单阶段网络架构,设计一种多分支特征提取解码器,有效地捕捉并聚合细节特征。本文提出特征优化与筛选模块,该模块对输入特征进行筛选以提取多尺度特征。以上两者结合,设计一种新的特征金字塔结构,提升网络的整体性能,提升对遮挡情况的位姿估计效果。实验在合成数据集LINEMOD及Occluded LINEMOD上进行。结果显示,本文方法在处理遮挡物体场景时取得了较显著的提升,与PyraPose、SD-Pose和CASAPose等现有最先进方法相比,本文方法在ADD/S-Recall指标上分别提高了43.1%、16.1%和12%。在目标数量较少时表现更佳,目标数量为4个时,性能提升17%。消融实验进一步验证了各模块的有效性。本文提出的单阶段多目标网络架构通过引入多分支特征提取解码器、特征优化与筛选模块以及特征金字塔结构,仅需训练一个网络即可处理任意数量的目标,在合成数据条件下,可以更好地完成6D位姿估计。实验结果验证了本文方法的有效性。In order to solve the problems of poor scalability,low generality and high computational cost of the traditional method using single object CNN model,and optimize the performance of multi-objective method.In this paper,a single-stage network architecture for multi-objective 6D attitude estimation is proposed,and a multi-branch feature extraction decoder is designed to capture and aggregate detailed features effectively.This paper proposes a feature optimization and screening module,which filters input features to extract multi-scale features.Combining the above two,a new feature pyramid structure is designed to improve the overall performance of the network and improve the pose estimation effect of occlusion.The experiments are carried out on synthetic data set LINEMOD and Occluded LINEMOD.The results show that the proposed method has achieved significant improvement in the processing of blocked object scenes.Compared with the most advanced methods such as PyraPose,SD-Pose and CASAPose,the proposed method has increased the ADD/S-Recall index by 43.1%,16.1%and 12%,respectively.It performed better when the number of targets is small,increasing performance by 17%when the number of targets is 4.The ablation experiment further verifies the effectiveness of each module.By introducing multi-branch feature extraction decoder,feature optimization and screening module,and feature pyramid structure,the proposed single-stage multi-objective network architecture can process any number of targets by training only one network,and can perform 6D pose estimation better under the condition of synthetic data.Experimental results verify the effectiveness of the proposed method.

关 键 词：6D位姿估计 多目标单阶段网络 多分支特征提取解码器 特征选择 合成数据 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]