NeDF：用于稀疏视角层析背景纹影的神经偏转场
NeDF: neural deflection fields for sparse-view tomographic background oriented Schlieren
AI总结：
本文提出神经偏转场NeDF，利用深度神经网络结合位置编码与分层采样策略，在稀疏视角条件下实现高质量TBOS三维密度场重建，并通过预混湍流火焰LES合成数据定量验证其优于经典方法。
摘要：
三维密度变化的湍流广泛存在于高速空气动力学、燃烧和非均相混合过程中。基于多相机的层析背景纹影（TBOS）已成为揭示三维流动密度结构的有力技术。然而，通常需要数十台相机才能获得高质量的重建密度场。受限于恶劣实验环境中可用光学窗口数量和有限空间，仅具有稀疏视角和有限观察角度的TBOS往往成为实际中的必要选择，这使得TBOS重建的反问题严重病态，并导致层析成像质量下降。在本研究中，我们提出了一种新的TBOS重建方法，即神经偏转场（NeDF），利用深度神经网络（DNN）表示密度梯度场，而不使用任何预训练的神经网络模型。特别地，该方法结合了最先进的位置编码技术和分层采样策略，以捕获高空间频率的密度结构。TBOS重建所需的背景图像基于高保真非线性光线追踪方法合成，并使用来自预混湍流火焰大涡模拟（LES）的真实流场作为真值。借助这些合成的BOS图像，与经典TBOS重建方法相比，所提方法的优越性得到了定量验证，同时位置编码和分层采样策略的具体贡献也得到了阐明。
英文摘要：
Three-dimensional (3D) density-varying turbulent flows are widely encountered in high-speed aerodynamics, combustion, and heterogeneous mixing processes. Multi-camera-based tomographic background-oriented Schlieren (TBOS) has emerged as a powerful technique for revealing 3D flow density structures. However, dozens of cameras are typically required to obtain high-quality reconstructed density fields. Limited by the number of available optical windows and confined space in the harsh experimental environments, TBOS with only sparse views and limited viewing angles often becomes the necessary choice practically, rendering the inverse problem for TBOS reconstruction severely ill-posed and resulting in degraded tomography quality. In this study, we propose a novel TBOS reconstruction method, neural deflection field (NeDF), utilizing deep neural networks (DNNs) to represent the density gradient fields without using any pretrained neural network models. Particularly, state-of-the-art positional encoding techniques and hierarchical sampling strategies are incorporated to capture the density structures of high spatial frequencies. Required background images for TBOS reconstructions are synthesized based on a high-fidelity nonlinear ray-tracing method with the ground truth flows from conducting LES simulations on premixed turbulent flames. Owing to these synthesized BOS images, the superiority of the proposed method is quantitatively verified compared to the classical TBOS reconstruction methods, and the specific contributions from the position encoding and the hierarchical sampling strategy are also elucidated.
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