Cross-domain fusion and embedded refinement-based 6D object pose tracking on textureless objects

Jichun Wang (), Guifang Duan (), Yang Wang (), Guodong Yi (), Liangyu Dong (), Zili Wang (), Xuewei Zhang () and Shuyou Zhang ()
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Jichun Wang: Zhejiang University
Guifang Duan: Zhejiang University
Yang Wang: Zhejiang University
Guodong Yi: Zhejiang University
Liangyu Dong: Zhejiang University
Zili Wang: Zhejiang University
Xuewei Zhang: Zhejiang University
Shuyou Zhang: Zhejiang University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 3, No 3, 1563-1577

Abstract: Abstract In industrial production, the ability to accurately perceive the location and orientation information of target objects enables the generalization of certain production processes to unstructured scenarios, thereby facilitating intelligent manufacturing. 6D object pose tracking aims to achieve real-time, accurate and long-term pose estimation given a video sequence. In this paper, we introduce a novel RGB-based 6D object pose tracking method that leverages temporal information. Our approach mainly involves building a network to predict the pose residual between two consecutive image frames. Given industrial objects with weak textures and complex shapes, we incorporate a cross-domain attention fusion module during the feature fusion phase, enabling the capture of pixel-level correspondences between different feature representations. This module enhances robustness to illumination variations and occlusion challenges. Additionally, we propose a simple yet effective pose regression module, referred to as the embedded refinement module, which considers the deviation of previous pose estimations. This module mitigates the cumulative pose estimation deviation due to large movements to some extent. We conduct comparative experiments on the YCB dataset, Fast-YCB dataset and a customized dataset specifically designed for the manipulation of industrial parts by a robotic arm. The results demonstrate that our proposed method surpasses state-of-the-art techniques, achieving robust and long-term tracking capabilities.

Keywords: Pose tracking; Cross-domain fusion; Embedded refinement; Neural network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02316-9

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