面向结构光三维成像的传感器感知逐点协方差
Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging
- Yonsei University（延世大学）
- Yonsei Institute for Embodied Intelligence（延世具身智能研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对结构光三维成像中逐点协方差未反映测量过程的问题，提出传感器感知一阶方法构建逐点3×3协方差场，实验验证其在G-ICP配准中性能优于各向同性模型。
中文摘要 AI 辅助
逐点不确定性模型在结构光三维重建中对概率配准、融合及质量评估至关重要。但实际应用中，点云协方差常被建模为各向同性常数，或从局部表面几何推断，无法明确反映测量过程。这在条纹投影轮廓术（FPP）中是局限：相位噪声经校准重建传播后，会产生强各向异性三维不确定性。本文提出一种传感器感知的一阶方法，基于实验测得的相位精度及校准后的相位-深度、相位-三维映射，构建逐点3×3协方差场。该方法将秩1的相位诱导协方差，与结合拟合的横向图像空间扰动尺度得到的有效满秩补充分离。固定成像条件下的重复平面实验表明，协方差主导方向与观测射线高度一致，且主导相位诱导不确定性尺度与标量深度不确定性相符。在G-ICP配准中，所提协方差显著优于常数各向同性模型，同时提供了与传统几何基协方差互补的传感器衍生不确定性表示。
英文摘要
Per-point uncertainty models are important in structured-light 3D reconstruction for probabilistic registration, fusion, and quality assessment. In practice, however, point-cloud covariances are often modeled as isotropic constants or inferred from local surface geometry and therefore do not explicitly reflect the measurement process. This is a limitation in fringe projection profilometry (FPP), where phase noise propagates through calibrated reconstruction and produces strongly anisotropic 3D uncertainty. This paper presents a sensor-informed first-order method for constructing a per-point 3 x 3 covariance field from experimentally measured phase precision and calibrated phase-to-depth and phase-to-3D mappings. The formulation separates a rank-1 phase-induced covariance from an effective full-rank completion obtained by incorporating fitted lateral image-space perturbation scales. Repeated-plane experiments under fixed imaging conditions show close alignment of the dominant covariance direction with the viewing ray, and consistency between the dominant phase-induced uncertainty scale and scalar depth uncertainty. In G-ICP registration, the proposed covariance substantially improves over a constant isotropic model while providing a sensor-derived uncertainty representation complementary to conventional geometry-based covariances.
