融合残差注意力和标准偏差的6D姿态细化网络

在6D物体姿态估计领域中, 现有算法往往难以实现对目标物体精准且鲁棒的姿态估计. 为解决该问题, 提出了一种结合残差注意力、混合空洞卷积和标准差信息的物体6D姿态细化网络. 首先, 在Gen6D图片特征提取网络中, 采用混合空洞卷积模块替换传统卷积模块, 以此扩大感受野、加强全局特征捕获能力. 接着, 在3D卷积神经网络中, 加入残差注意力模块, 这有助于区分特征通道的重要程度, 进而在提取关键特征的同时, 减少浅层特征的丢失. 最后, 在平均距离损失函数中, 引入了标准差信息, 从而使模型能够区分物体的更多姿态信息. 实验结果显示, 所提出的网络在LINEMOD数据集和GenMOP数据集上的ADD指标分别达到了68.79%和56.03%. 与Gen6D网络相比, ADD指标分别提升了1.78个百分点和5.64个百分点, 这一结果验证了所提出的网络能够显著提升6D姿态估计的准确性.

6D Pose Refiner Network Combining Residual Attention and Standard Deviation

In the domain of 6D object pose estimation, existing algorithms often struggle to achieve precise and robust pose estimation of the target objects. To address this challenge, this study introduces an object 6D pose refinement network that incorporates residual attention, hybrid dilated convolution, and standard deviation information. Firstly, in the Gen6D image feature extraction network, traditional convolutional modules are replaced with hybrid dilated convolution modules to expand the receptive field and enhance the capability to capture global features. Subsequently, within the 3D convolutional neural network, a residual attention module is integrated. This assists in distinguishing the importance of feature channels, hence extracting key features while minimizing the loss of shallow-layer features. Finally, the study introduces standard deviation information into the average distance loss function, enabling the model to discern more pose information of the object. Experimental results demonstrate that the proposed network achieves ADD scores of 68.79% and 56.03% on the LINEMOD dataset and GenMOP dataset, respectively. Compared to the Gen6D network, there is an improvement of 1.78% and 5.64% in the ADD scores, validating the significant enhancement in the accuracy of 6D pose estimation brought about by the proposed network.