Three-dimensional (3D) fingerprints preserve global finger geometry and local ridge structure while avoiding contact-induced deformation, but they remain difficult to integrate with legacy two-dimensional (2D) fingerprint systems. This paper addresses the intermediate stage between 3D acquisition and cross-modal matching, and presents a unified framework for 3D fingerprint preprocessing and registration across contactless and contact-based 2D modalities. The framework combines four components: 1) a nonparametric visualization and unwrapping method that converts a 3D fingerprint point cloud into a rolled-equivalent 2D representation without relying on a global finger-shape model; 2) a point-cloud fusion pipeline that registers and mosaics multiple partial 3D captures into a more complete fingerprint model; 3) an ellipse-based pose normalization method for canonical finger alignment; and 4) a pose-aware cross-modal registration strategy that improves compatibility between 3D fingerprints and both contactless and contact-based 2D fingerprints. Experiments on a self-collected multimodal fingerprint database containing 150 fingers show that the proposed framework achieves ridge-level 3D registration accuracy, robust pose estimation, and consistent gains in 2D compatibility. In particular, the 3D fusion error is concentrated around 0.09 mm, contactless 2D--3D registration reaches ridge-scale projection accuracy, and pose-aware unwrapping improves genuine matching scores relative to generic 3D unwrapping. These results support the use of 3D fingerprints as an effective geometric bridge across heterogeneous fingerprint modalities. The baseline implementation has been publicly released at https://github.com/XiongjunGuan/3DFpVisual.

翻译：三维指纹在保留手指全局几何结构与局部脊线纹理的同时，避免了接触形变的影响，但其与传统的二维指纹系统仍难以融合。本文聚焦于三维采集与跨模态匹配之间的中间环节，提出了一套统一框架，用于三维指纹的预处理及与非接触式/接触式二维指纹的跨模态配准。该框架包含四个组件：1）非参数化可视化与展开方法，无需依赖全局手指形状模型，即可将三维指纹点云转换为等效滚动展开的二维表征；2）点云融合管线，通过配准与拼接多个局部三维采集数据生成更完整的手指模型；3）基于椭圆的姿态归一化方法，实现手指的规范对齐；4）姿态感知的跨模态配准策略，提升三维指纹与非接触式/接触式二维指纹的兼容性。在自建包含150根手指的多模态指纹数据库上进行的实验表明，所提框架实现了脊线级三维配准精度、鲁棒的姿态估计，并持续提升二维兼容性。具体而言，三维融合误差集中在0.09毫米左右，非接触式二维-三维配准达到脊线尺度投影精度，且相比通用三维展开，姿态感知展开提升了真实匹配分数。这些结果支持将三维指纹作为异构指纹模态间的有效几何桥梁。基线实现已公开于https://github.com/XiongjunGuan/3DFpVisual。