A real-time method for estimating object 3D pose based on a multi-branch architecture is proposed,aiming to solve the issues of low precision in real-time pose estimation and slow convergence of regression solving models caused by the large scale and range of target dimensions and positions in the vehicle-road cooperative application scenario.On the basis of the model architecture of the target 2D detection algorithm,a branch structure for position estimation is designed for outputting the target pose quaternion,the 3D spatial position of the target relative to the camera and the target size.In the training stage,corresponding loss functions are designed for the pose estimation branch,in which the vector unitisation operator is used to regress the pose quaternion,and the logarithmic remapping algorithm is used to regress the target dimensions and target-to-camera distances.In the inference stage,the 3D pose of the target is solved based on the quaternion and target-to-camera distances output from the model,so as to achieve the accurate pose estimation.In the pose accuracy verification of the OVRC dataset,the maximum mean square error of the position coordinate is 1.94 m,and the maximum mean square error of the attitude angle estimation result is 3.98°.In the relative accuracy test experiment of the Kitti dataset,the detection accuracy is improved by 3.22%compared with the PVNet method,and the inference efficiency is improved by 1 times compared with the 3DBB method.
NETL
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维普
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