用于通用行人重识别的可靠性感知3D几何注入
Reliability-Aware 3D Geometric Injection for Universal Person Re-identification
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中文总结 AI 辅助
针对通用行人重识别问题，提出UniGeo框架，通过一致性感知可靠性门和双流残差融合，将3D信息处理解耦，投影单目3D参数补偿结构，以残差形式引入3D先验并过滤噪声，提升了挑战性场景性能。
中文摘要 AI 辅助
通用行人重识别旨在在统一模型中跨多种现实场景检索行人身份，包括严重遮挡、服装变化和跨模态转换。现有2D表示因缺乏深度和拓扑感知而难以处理空间模糊性，引入单目3D先验会因极端视觉退化下的几何估计噪声导致严重负迁移。为此提出UniGeo框架，由一致性感知可靠性门和双流残差融合驱动，将3D信息处理解耦为几何提取和动态利用，通过投影单目3D参数到运动关节表示来提供纯结构补偿，将3D先验作为后期结构残差，由一致性感知门调制以过滤几何噪声。实验表明该方法在具有挑战性、对结构敏感的场景中表现更好，在干净领域也保持了有竞争力的性能。
英文摘要
Universal person re-identification (ReID) aims to retrieve pedestrian identities across diverse real-world scenarios, including severe occlusions, clothing changes, and cross-modality shifts, within a unified model. However, existing 2D representations fundamentally struggle with spatial ambiguities due to a lack of depth and topological awareness, while naively introducing monocular 3D priors often causes severe negative transfer due to geometric estimation noise under extreme visual degradation. To safely harness the clothing-invariant and canonical structural properties of 3D geometry, we propose UniGeo, a Universal Monocular 3D-Enhanced ReID framework driven by a Consistency-Aware Reliability Gate and Dual-Stream Residual Fusion. Specifically, the processing of 3D information is strategically decoupled into geometric extraction and dynamic utilization. To provide pure structural compensation, we project monocular 3D parameters into kinematic joint representations, explicitly capturing instance-level geometric topology to resolve appearance-based ambiguities. To robustly incorporate these cues without perturbing the reliable 2D feature space, we isolate the 3D prior as a late-stage structural residual; modulated by the consistency-aware gate, this mechanism adaptively filters geometric noise and enables controlled fallback to the pure 2D baseline. Extensive experiments show that our method improves challenging, structure-sensitive scenarios while preserving competitive performance on clean domains. Code is available at https://github.com/BohanSu/UniGeo.
发表机构
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（武汉大学计算机学院多媒体软件国家工程研究中心）
机构由 AI 辅助整理，请以论文原文为准。
