用自编码器提升零售场景相机定位精度，数据量少30%仍有效。
Relative Pose Regression with Pose Auto-Encoders: Enhancing Accuracy and Data Efficiency for Retail Applications
- 用姿态自编码器建模场景先验，实现相对位姿回归
- 在室内基准上提升绝对位姿回归精度，仅需30%数据训练
- 无需存储图像或姿态数据，适合低资源零售部署
精准的相机定位对现代零售环境至关重要，可支持客户体验优化、库存管理自动化和自主运营。尽管单图绝对位姿回归（APR）具有潜力，但结合视觉与空间场景先验的方法通常表现更优。相机姿态自编码器（PAE）近期被引入以将此类先验嵌入APR。本文将PAE拓展至相对位姿回归（RPR）任务，提出一种新颖的重定位策略，利用PAE驱动的RPR对APR预测进行精修，且无需额外存储图像或姿态数据。我们首先构建基于PAE的RPR模型，并与同架构的图像基RPR模型对比，验证其有效性。进一步实验表明，该精修策略显著提升了室内基准上的APR定位精度。值得注意的是，本方法在仅使用30%训练数据时即达到竞争性性能，大幅降低零售场景部署的数据采集负担。代码与预训练模型已开源：https://github.com/yolish/camera-pose-auto-encoders。
原文摘要 · Abstract (English)
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
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