为提高目标6D姿态追踪网络的收敛能力和追踪精度,提出一种基于少量数据驱动的目标6D姿态追踪复用预测网络。以当前时刻的彩色及深度(red green blue and depth,RGB-D)图像和上一时刻的目标渲染值作为输入,通过2个独立的特征编码器提取...为提高目标6D姿态追踪网络的收敛能力和追踪精度,提出一种基于少量数据驱动的目标6D姿态追踪复用预测网络。以当前时刻的彩色及深度(red green blue and depth,RGB-D)图像和上一时刻的目标渲染值作为输入,通过2个独立的特征编码器提取特征矩阵,在特征编码器中引入通道注意力机制模块,保证有选择性地调整通道信息的权重;构建复用预测网络模块,将特征矩阵解耦得到旋转矩阵,通过旋转矩阵前向传播与特征矩阵融合,将融合的结果再次解耦得到物体6D姿态的旋转矩阵与平移矩阵,并采用李代数方法通过2个矩阵计算出目标的6D姿态。实验结果表明:在使用少量数据训练网络模型的情况下,与MaskFusion、“TEASER++”和se(3)-Tracknet等方法相比,所提方法能够提高目标6D姿态追踪的准确率。展开更多
In this article we study the estimation method of nonparametric regression measurement error model based on a validation data. The estimation procedures are based on orthogonal series estimation and truncated series a...In this article we study the estimation method of nonparametric regression measurement error model based on a validation data. The estimation procedures are based on orthogonal series estimation and truncated series approximation methods without specifying any structure equation and the distribution assumption. The convergence rates of the proposed estimator are derived. By example and through simulation, the method is robust against the misspecification of a measurement error model.展开更多
In this article, we develop estimation approaches for nonparametric multiple regression measurement error models when both independent validation data on covariables and primary data on the response variable and surro...In this article, we develop estimation approaches for nonparametric multiple regression measurement error models when both independent validation data on covariables and primary data on the response variable and surrogate covariables are available. An estimator which integrates Fourier series estimation and truncated series approximation methods is derived without any error model structure assumption between the true covariables and surrogate variables. Most importantly, our proposed methodology can be readily extended to the case that only some of covariates are measured with errors with the assistance of validation data. Under mild conditions, we derive the convergence rates of the proposed estimators. The finite-sample properties of the estimators are investigated through simulation studies.展开更多
文摘为提高目标6D姿态追踪网络的收敛能力和追踪精度,提出一种基于少量数据驱动的目标6D姿态追踪复用预测网络。以当前时刻的彩色及深度(red green blue and depth,RGB-D)图像和上一时刻的目标渲染值作为输入,通过2个独立的特征编码器提取特征矩阵,在特征编码器中引入通道注意力机制模块,保证有选择性地调整通道信息的权重;构建复用预测网络模块,将特征矩阵解耦得到旋转矩阵,通过旋转矩阵前向传播与特征矩阵融合,将融合的结果再次解耦得到物体6D姿态的旋转矩阵与平移矩阵,并采用李代数方法通过2个矩阵计算出目标的6D姿态。实验结果表明:在使用少量数据训练网络模型的情况下,与MaskFusion、“TEASER++”和se(3)-Tracknet等方法相比,所提方法能够提高目标6D姿态追踪的准确率。
文摘In this article we study the estimation method of nonparametric regression measurement error model based on a validation data. The estimation procedures are based on orthogonal series estimation and truncated series approximation methods without specifying any structure equation and the distribution assumption. The convergence rates of the proposed estimator are derived. By example and through simulation, the method is robust against the misspecification of a measurement error model.
文摘In this article, we develop estimation approaches for nonparametric multiple regression measurement error models when both independent validation data on covariables and primary data on the response variable and surrogate covariables are available. An estimator which integrates Fourier series estimation and truncated series approximation methods is derived without any error model structure assumption between the true covariables and surrogate variables. Most importantly, our proposed methodology can be readily extended to the case that only some of covariates are measured with errors with the assistance of validation data. Under mild conditions, we derive the convergence rates of the proposed estimators. The finite-sample properties of the estimators are investigated through simulation studies.
