Learning 6D pose of textureless objects via multi-scale dense relation

Junwen Huang; Zhenwei Bian; Yifei Shi; Xin Xu; Hongjia Zhang; Chenggang Xie

doi:10.1117/12.2580859

10 November 2020 Learning 6D pose of textureless objects via multi-scale dense relation

Junwen Huang, Zhenwei Bian, Yifei Shi, Xin Xu, Hongjia Zhang, Chenggang Xie

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841B (2020) https://doi.org/10.1117/12.2580859
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

6D object pose estimation is a fundamental problem for many computer vision and robotics applications. Recent work has shown that data-driven approaches could enable accurate 6D pose estimation for objects with sufficient texture on the surface. However, few works have focused on estimation 6D pose for texture-less objects. In this paper, we present a network that estimating 6D pose for texture-less objects by using the multi-scale relational features. The proposed network, which leverages both the appearance and geometry features from multi-scale point groups, is able to extract distinctive features for texture-less region. In particular, the multi-scale features encode relational information of the point groups, are more informative compared to the feature comes from vanilla convolutional neural networks and PointNet. The proposed network is end-to-end trainable. Experiments on T-LESS dataset demonstrate our method achieves competitive results on 6D pose estimation task of texture-less objects.

Citation Download Citation

Junwen Huang, Zhenwei Bian, Yifei Shi, Xin Xu, Hongjia Zhang, and Chenggang Xie "Learning 6D pose of textureless objects via multi-scale dense relation", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841B (10 November 2020); https://doi.org/10.1117/12.2580859

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KEYWORDS

RGB color model

Computer vision technology

Machine vision

Autoregressive models

Clouds

Neural networks

Image fusion

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