AI 中文总结
本文针对空地行人重识别的视角偏差问题，提出VR3D框架，通过视角鲁棒3D表示交互与可靠性感知融合技术，在三个基准数据集上实现了优于现有方法的性能，CARGO数据集Rank-1提升5.63%。
AI 中文摘要
空地行人重识别是一项极具挑战性的任务，跨平台视角变化会导致严重的遮挡和几何变形。现有方法仅在2D图像空间内学习视角不变表示，而剧烈的视角变化会使学习到的特征仍与视角偏差耦合。为解决该问题，本文提出VR3D——一种视角鲁棒3D表示学习框架，该框架将图像映射到统一的3D坐标空间以实现视角无关的特征交互。具体而言，本文引入视角鲁棒3D表示交互（View-Robust 3D Representation Interaction），利用从单张2D观测中提取的3D先验，将2D外观特征提升到规范3D空间；VR3I采用3D几何-语义注意力（3D Geometry-Semantic Attention），基于对应身体部位的3D空间位置建立2D图像块与3D体素之间的交互，有效将2D语义锚定在3D框架内。此外，由于视角变化和3D重建误差会导致不同样本的表示可靠性存在差异，本文引入可靠性感知融合（Reliability-Aware Fusion），该模块会估计样本特定的可靠性并自适应聚合多源表示。在三个基准数据集（CARGO、AG-ReID.v1和AG-ReID.v2）上进行的大量实验表明，VR3D的性能优于现有最新方法，例如其在CARGO数据集上的Rank-1指标提升了5.63%，本文的代码将公开。
英文摘要
Aerial-ground person re-identification is a challenging task due to cross-platform viewpoint variations, which cause severe occlusion and geometric deformation. Existing methods attempt to learn view-invariant representations exclusively within the 2D image space, where drastic viewpoint variations cause the learned features to remain coupled with viewpoint bias. To address this, we propose VR3D, a View-Robust 3D Representation Learning framework that maps images into a unified 3D coordinate space to achieve view-independent feature interaction. Specifically, we introduce View-Robust 3D Representation Interaction, which leverages 3D priors extracted from single 2D observations to lift 2D appearance features into a canonical 3D space. VR3I employs 3D Geometry-Semantic Attention to establish interactions between 2D patches and 3D voxels from corresponding body parts based on their 3D spatial locations, effectively grounding 2D semantics within a 3D framework. In addition, as the reliability of these representations varies across samples due to viewpoint changes and 3D reconstruction errors, we introduce Reliability-Aware Fusion, which estimates sample-specific reliability and adaptively aggregates the multi-source representations. Extensive experiments on three benchmark datasets (CARGO, AG-ReID.v1, and AG-ReID.v2) demonstrate that VR3D outperforms recent methods. For example, it achieves a 5.63% improvement in Rank-1 on CARGO. Our code will be released.
Comments12 pages, 10 figures
