标题： 基于姿态自编码器的相对位姿回归：提升零售应用中的准确性与数据效率 显示英文标题

标题： Relative Pose Regression with Pose Auto-Encoders: Enhancing Accuracy and Data Efficiency for Retail Applications

摘要： 精确的相机定位对于现代零售环境至关重要，它能够提升客户体验、简化库存管理并实现自主操作。 虽然从单张图像进行绝对姿态回归（APR）提供了一个有前景的解决方案，但结合视觉和空间场景先验的方法通常能实现更高的准确性。 相机姿态自动编码器（PAEs）最近被引入以将这些先验嵌入到APR中。 在本工作中，我们将PAEs扩展到相对姿态回归（RPR）任务，并提出一种新的重新定位方案，该方案使用基于PAE的RPR来优化APR预测，而无需额外存储图像或姿态数据。 我们首先介绍基于PAE的RPR，并通过与具有相同架构的基于图像的RPR模型进行比较来证明其有效性。 然后我们展示了由基于PAE的RPR驱动的精炼策略，在室内基准测试中提升了APR的定位精度。 值得注意的是，即使仅使用30%的数据进行训练，我们的方法也表现出具有竞争力的性能，显著降低了零售部署中的数据收集负担。 我们的代码和预训练模型可在以下链接获取：https://github.com/yolish/camera-pose-auto-encoders 显示英文摘要

摘要： Accurate camera localization is crucial for modern retail environments, enabling enhanced customer experiences, streamlined inventory management, and autonomous operations. While Absolute Pose Regression (APR) from a single image offers a promising solution, approaches that incorporate visual and spatial scene priors tend to achieve higher accuracy. Camera Pose Auto-Encoders (PAEs) have recently been introduced to embed such priors into APR. In this work, we extend PAEs to the task of Relative Pose Regression (RPR) and propose a novel re-localization scheme that refines APR predictions using PAE-based RPR, without requiring additional storage of images or pose data. We first introduce PAE-based RPR and establish its effectiveness by comparing it with image-based RPR models of equivalent architectures. We then demonstrate that our refinement strategy, driven by a PAE-based RPR, enhances APR localization accuracy on indoor benchmarks. Notably, our method is shown to achieve competitive performance even when trained with only 30% of the data, substantially reducing the data collection burden for retail deployment. Our code and pre-trained models are available at: https://github.com/yolish/camera-pose-auto-encoders