Work

EBFA-6D: End-to-End Transparent Object 6D Pose Estimation Based on a Boundary Feature Augmented Mechanism

Year: 2024

Type: article

Abstract: Transparent objects, commonly encountered in everyday environments, present significant challenges for 6D pose estimation due to their unique optical properties. The lack of inherent texture and color complicates traditional vision methods, while the transparency prevents depth sensors from accurately capturing geometric details. We propose EBFA-6D, a novel end-to-end 6D pose estimation framework that directly predicts the 6D poses of transparent objects from a single RGB image. To overcome the challenges introduced by transparency, we leverage the high contrast at object boundaries inherent to transparent objects by proposing a boundary feature augmented mechanism. We further conduct a bottom-up feature fusion to enhance the location capability of EBFA-6D. EBFA-6D is evaluated on the ClearPose dataset, outperforming the existing methods in accuracy while achieving an inference speed near real-time. The results demonstrate that EBFA-6D provides an efficient and effective solution for accurate 6D pose estimation of transparent objects.

Source: Sensors

DOI: 10.3390/s24237584

Authors: Xinbei Jiang, Zichen Zhu, Tianhan Gao, Nan Guo

Institution: Northeastern University

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funder: National Natural Science Foundation of China