In industrial applications requiring real-time feedback, such as quality control and robotic manipulation, the demand for high-speed and accurate pose estimation remains critical. Despite advances improving speed and accuracy in pose estimation, finding a balance between computational efficiency and accuracy poses significant challenges in dynamic environments. Most current algorithms lack scalability in estimation time, especially for diverse datasets, and the state-of-the-art (SOTA) methods are often too slow. This study focuses on developing a fast and scalable set of pose estimators based on GDRNPP to meet or exceed current benchmarks in accuracy and robustness, particularly addressing the efficiency-accuracy trade-off essential in real-time scenarios. We propose the AMIS algorithm to tailor the utilized model according to an application-specific trade-off between inference time and accuracy. We further show the effectiveness of the AMIS-based model choice on four prominent benchmark datasets (LM-O, YCB-V, T-LESS, and ITODD).

本研究解决了工业应用中实时反馈所需的高速度和准确性的姿态估计问题，特别是在动态环境中的效率与准确性之间的权衡。提出了基于AMIS算法的快速可扩展姿态估计器，该算法可以根据特定应用调整模型，以优化推理时间和准确度之间的平衡。研究表明，该模型在四个重要的基准数据集上达到了或超过了当前的精度与稳健性基准。

高效姿态估计6D: 可扩展和高效的6D物体姿态估计