Holistic and local patch framework for 6D object pose estimation in RGB-D images

Abstract

6D object pose estimation is a challenging problem of great importance arising in computer vision and many practical applications. In this paper, we present a novel framework for 6D object pose estimation in RGB-D images. By contrast with recent holistic or local patch-based method, we combine holistic and local patches together to fulfill this task. The proposed method has three stages, including holistic patch extraction, local patch regression and 6D pose refinement. In the first stage, we employ an existing convolutional neural network to roughly predict the location of target object and extract holistic patches, which is trained with synthetic rendering data. In the second stage, an improved Convolutional Auto-Encoder (CAE) is employed to learn the condensed feature representation of local patch, and coarse 6D object pose can be estimated by the regression of feature voting. Finally, we utilize Particle Swarm Optimization (PSO) to refine 6D object pose. The proposed method is evaluated on three challenging public datasets which can test the performance under background clutter, foreground occlusion as well as multiple-instance conditions. Moreover, we provide extensive experiments on the various parameters of the framework such as the dimension of local patch feature and some parameters in PSO. Several experimental results demonstrate that the proposed method outperforms some other state-of-the-art methods.