提出高效可扩展的6D位姿估计算法，兼顾速度与精度。
EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation
- 基于GDRNPP设计自适应模型选择算法AMIS
- 在4个数据集上实现高速且高精度的位姿估计
- 适合工业实时场景中的机器人抓取与质检
工业应用如质量检测和机器人操作需要实时反馈，对高精度、高速度的6D物体位姿估计需求迫切。尽管近年来方法在速度与精度上不断进步，但在动态环境中平衡计算效率与准确率仍具挑战。现有算法普遍缺乏估算时间的可扩展性，尤其是面对多样化数据集时，当前最先进方法往往速度过慢。本文针对此问题，基于GDRNPP开发了一套快速且可扩展的位姿估计算法，旨在达到或超越现有基准的准确率与鲁棒性，特别解决实时场景下的效率-精度权衡。提出AMIS算法，可根据特定应用场景在推理时间和准确率间进行自适应模型选择。实验验证了该方法在四个主流基准数据集（LM-O、YCB-V、T-LESS、ITODD）上的有效性。
原文摘要 · Abstract (English)
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).
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