针对物体姿态估计中的难例，合成真实训练样本提升精度。
Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation
- 基于姿态与遮挡误差建模，生成针对性难例。
- 在ROBI数据集上使检测准确率最高提升20%。
- 适用于纹理缺失、遮挡严重的机器人抓取场景。
6D物体姿态估计是机器人高效交互环境的基础，尤其在料箱拣选应用中极具挑战性：物体可能无纹理、姿态复杂，同类物体间的遮挡会导致即使训练良好的模型也产生混淆。本文提出一种无需依赖特定模型的难例合成方法，利用现有模拟器，并对相机-物体视域空间及遮挡空间中的姿态误差进行建模。通过分析姿态与遮挡分布下的模型性能，定位高误差区域并生成真实感强的训练样本加以针对性优化。实验表明，采用该训练策略后，使用先进姿态估计算法，在多个ROBI数据集物体上检测正确率最高提升20%。
原文摘要 · Abstract (English)
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.
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