# Object 6D Pose Estimation with Non-local Attention

arXiv: https://arxiv.org/abs/2002.08749 · HTML: https://mlsanity.com/paper/2002.08749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition](https://mlsanity.com/paper/2106.03455/) (2021)
- [Spatial Feature Mapping for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2206.01831/) (2022)

## Abstract (arXiv)

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.