Work

Enhancing object pose estimation for RGB images in cluttered scenes

Year: 2025

Type: article

Abstract: Estimating the 6D pose of objects is crucial for robots to interact with the environment. 6D Object pose estimation from RGB images in a cluttered scene and heavy occlusions is a critical issue. Most existing methods use two stages to estimate object pose: First, extract the object features, and then use the PnP/RANSAC method to estimate object pose. However, most of these techniques merely localize a group of key-points by regressing their coordinates, which are vulnerable to occlusion and have poor performance for multi-object pose estimation. These methods cannot directly regress the 6D pose estimation from a loss during training. In this paper, we propose a framework based on convolutional neural network (CNN) and self-attention mechanism as an end-to-end method for single and multi-object 6D pose estimation using RGB images with low computational cost. Our method utilizes feature fusion to extract local features and combines multi-head self-attention (MHSA) with iterative refinement to improve pose estimation performance. Furthermore, our method can be scaled according to computational resources. Our experiments illustrate that our method performs in benchmark datasets the Linemod and Occlusion Linemod and achieves 97.45% and 84.84% in terms of the ADD(-S) metric in both datasets, respectively.

Source: Scientific Reports

DOI: 10.1038/s41598-025-90482-6

Authors: Metwalli Al-Selwi, Huang Ning, Yin Gao, Yan Chao, Qiming Li, Jun Li

Institutions: Chinese Academy of Sciences, Fujian Institute of Research on the Structure of Matter, Quanzhou Institute of Equipment Manufacturing Haixi Institute, University of Chinese Academy of Sciences

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Natural Science Foundation of China, Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China