面向目标6DoF姿态与尺寸估计的全卷积神经网络模型

Full convolution neural network model for 6DoF attitude and size estimation

针对6DoF姿态估计需要收集与标注大量数据训练神经网络提出一种小数据集下面向目标6DoF姿态与尺寸估计的全卷积神经网络模型以降低人工操作成本。首先采用注意力机制与特征金字塔相结合的方式通过区域建议网络提取感兴趣区域,将该区域输入并行融合全卷积网络获得掩膜图;其次通过增加跳跃连接丰富每个卷积后的特征信息,将其融合并通过分类获得预测标准化坐标空间图;最后将得到的掩膜图与标准化坐标空间图通过三维点云配准获得目标的6DoF姿态与尺寸。实验表明,该方法在小数据集下较PVN3D方法精度提升约2.6%,较GPVPose方法精度提升约1%。

In order to reduce the cost of manual operation, this paper proposed a fully convolutional neural network model for 6DoF pose and size estimation of targets with small data sets for 6DoF pose and size estimation that required collecting and labeling a large amount of data to train neural networks.Firstly, it combined the attention mechanism with the feature pyramid to extract the region of interest through the region suggestion network, and the region was input into the parallel fusion full convolution network to obtain the mask map.Secondly, it enriched the feature information after each convolution by adding jump connections, which were fused and classified to obtain the predicted normalization coordinate space map.Finally, it obtained the 6DoF pose and size of the target by 3D point cloud registration between the obtained mask image and the normalization coordinate space image.Experiments show that compared with PVN3D method, this method improves the accuracy by about 2.6% and GPVPose method by about 1% in small data sets.