A Robust CoS-PVNet Pose Estimation Network in Complex Scenarios

Abstract

Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors such as occlusion, noise, weak texture, and lighting changes may affect the accuracy and robustness of object 6D pose estimation. We propose a robust CoS-PVNet (complex scenarios pixel-wise voting network) pose estimation network for complex scenes. By adding a pixel-weight layer based on the PVNet network, more accurate pixel point vectors are selected, and dilated convolution and adaptive weighting strategies are used to capture local and global contextual information of the input feature map. At the same time, the perspective-n-point localization algorithm is used to accurately locate 2D key points to solve the pose of 6D objects, and then, the transformation relationship matrix of 6D pose projection is solved. The research results indicate that on the LineMod and Occlusion LineMod datasets, CoS-PVNet has high accuracy and can achieve stable and robust 6D pose estimation even in complex scenes.