【摘要】 随着深度相机的应用,三维场景的重建越来越简单、快速。从单视角的深度场景图像中检索出物体还是比较困难,特别是物体的姿态估计。提出了一种基于卷积神经网络的深度图像姿态估计算法。该算法采用了回归估计来实现姿态的估计。通过3D模型合成大量不同姿态的深度图像样本,从而解决回归估计需要稠密采样的训练数据问题。对于不同类别的物体,分别用线性回归估计来拟合姿态函数。在基于Le Net-5模型上修改了卷积神经网络的结构,使得该网络适用于回归估计。实验结果表明:我们的方法取得了平均误差约4.3°的估计结果,优于其他文献的方法。更多还原

【Abstract】 3D scenes can be reconstructed more easily and rapidly with depth camera. However, it is difficult to retrieve items in 3D scenes from a single view depth image, especially for the pose estimation. In this paper, we present a method of pose estimation using convolutional neural network with synthesis depth data, which predicts the items’ pose in 3D scenes by regression. This is achieved by(i) synthesizing large amount of depth images with different pose for linear regression using 3D model,(ii) designing a class-dependent linear regression framework, which estimates the object’s pose from different classes separately,(iii) reforming Le Net-5 model by representing the loss layer as a linear regression form. The proposed algorithm is demonstrated on different data sets and achieves higher accuracy(average error 4.3°) than other algorithms.更多还原