Object 6D Pose Estimation with Non-local Attention

2020/02/20 by Jianhan Mei, Henghui Ding, Mei, Jianhan +3
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Robot Manipulation and Learning #Robotics and Sensor-Based Localization #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2002.08749

openalex publication_date 2020/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we address the challenging task of estimating 6D object pose from a single RGB image. Motivated by the deep learning based object detection methods, we propose a concise and efficient network that integrate 6D object pose parameter estimation into the object detection framework. Furthermore, for more robust estimation to occlusion, a non-local self-attention module is introduced. The experimental results show that the proposed method reaches the state-of-the-art performance on the YCB-video and the Linemod datasets.

Citations

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal\n Networks

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