发表机构
The University of Texas at Austin; Texas Materials Institute（德克萨斯大学奥斯汀分校; 德克萨斯材料研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出分辨率感知的RAFT-DVC密集DVC框架，明确其精度与工作模式，在不同纹理、位移条件下性能优异，可实现跨纹理迁移与大体积估计。
AI 中文摘要
数字体积相关（DVC）可从体积图像提供三维全场位移测量，但基于机器学习的DVC模型的内部分辨率如何影响精度和工作范围仍知之甚少。本文提出RAFT-DVC，这是一种基于循环全对场变换（RAFT）的分辨率感知DVC求解器系列，编码器下采样因子s分别为2、4和8。采用匹配设计，我们发现三种求解器能将位移定位至约0.017特征网格体素，经验原始体积误差缩放约为0.017s体素。这些求解器呈现由位移范围和体积纹理兼容性共同决定的互补工作模式。合成基准测试显示，在细纹理、小至中等位移条件下，RAFT-DVC的误差与调优后的经典DVC处于同一量级；在粗纹理、大位移条件下，其表现相当或更具优势。频率扫描测试量化了变形空间分辨率，而分块推理可实现大体积上的密集估计。对压痕过程中采集的共焦体积图像的评估表明，使求解器工作模式匹配变形幅度和图像纹理至关重要。对弹性泡沫的微CT图像的测试，尽管仅在粒子标记的合成数据上训练，仍提供了跨纹理迁移的证据。我们还发现了三维RAFT相关采样中的坐标顺序不一致问题，并引入了非立方体脉冲测试以独立于网络训练验证采样器几何结构。校正采样器可提升原生输入精度和对未见过的体积维度的泛化能力。综上，这些结果确立了RAFT-DVC为一种快速、分辨率感知的密集DVC框架，具有明确的精度和工作模式。
英文摘要
Digital volume correlation (DVC) provides three-dimensional full-field displacement measurements from volumetric images, but how the internal resolution of a machine-learning-based DVC model affects accuracy and operating range remains poorly understood. Here, we present RAFT-DVC, a resolution-aware family of recurrent all-pairs field transforms (RAFT)-based DVC solvers with encoder downsampling factors s = 2, 4, and 8. Using a matched design, we find that the three solvers localize displacement to approximately 0.017 feature-grid voxel, giving an empirical raw-volume error scaling of approximately 0.017s voxel. The solvers exhibit complementary operating regimes governed jointly by displacement reach and volumetric-texture compatibility. Synthetic benchmarks show that RAFT-DVC achieves errors of the same order as tuned classical DVC under fine-texture, small-to-moderate-displacement conditions and becomes competitive or advantageous under coarse-texture, large-displacement conditions. Frequency-swept tests quantify deformation spatial resolution, while tiled inference enables dense estimation on large volumes. Evaluation on confocal volumetric images acquired during indentation illustrates the importance of matching solver operating regime to deformation magnitude and image texture. Tests on micro-CT images of elastomeric foam, despite training only on particle-labeled synthetic data, provide evidence of cross-texture transfer. We also identify coordinate-order inconsistencies in three-dimensional RAFT correlation sampling and introduce a non-cubic impulse test to verify sampler geometry independently of network training. Correcting the sampler improves native-input accuracy and generalization to unseen volume dimensions. Together, these results establish RAFT-DVC as a fast, resolution-aware framework for dense DVC with characterized accuracy and operating regimes.
