Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation

TL;DR

This paper introduces a novel hard sample synthesis method based on estimated pose and occlusion errors to enhance 6D object pose estimation, especially in challenging bin-picking scenarios, leading to significant accuracy improvements.

Contribution

It presents a model-agnostic hard example synthesis approach utilizing simulators and pose error modeling in both view and occlusion spaces, targeting high-error regions for training.

Findings

Up to 20% increase in correct detection rate.

Effective targeting of high-error pose and occlusion regions.

Improved robustness of pose estimation models.

Abstract

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where objects may be textureless and in difficult poses, and occlusion between objects of the same type may cause confusion even in well-trained models. We propose a novel method of hard example synthesis that is model-agnostic, using existing simulators and the modeling of pose error in both the camera-to-object viewsphere and occlusion space. Through evaluation of the model performance with respect to the distribution of object poses and occlusions, we discover regions of high error and generate realistic training samples to specifically target these regions. With our training approach, we demonstrate an improvement in correct detection rate of up to 20% across several ROBI-dataset objects using state-of-the-art pose estimation models.