用可学习的物理先验实现跨场景的非视域成像，低信噪比下仍有效。
Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors
- 设计可学习路径补偿与自适应相位场，动态调整光路建模。
- 仅在合成数据训练，即可在多种真实低信噪比场景泛化。
- 适合做非视域成像、鲁棒重建或跨设备应用的研究者。
非视域（NLOS）成像通过间接反射恢复隐藏物体，具有广泛应用潜力。现有方法依赖固定经验物理先验，如单一路径补偿，且在低信噪比（SNR）场景下泛化能力有限。为此，本文提出基于学习的新方案，包含两个核心设计：可学习路径补偿（LPC）与自适应相位场（APF）。LPC为场景中不同物体分配定制化路径补偿系数，有效缓解远距离区域的光波衰减；APF则学习光照函数的精确高斯窗口，动态选择瞬态测量中的相关频段。实验表明，该方法仅在合成数据上训练，即可无缝泛化至多种真实世界数据集，涵盖不同成像系统与低信噪比条件。
原文摘要 · Abstract (English)
Non-line-of-sight (NLOS) imaging, recovering the hidden volume from indirect reflections, has attracted increasing attention due to its potential applications. Despite promising results, existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors, e.g., single fixed path compensation. Moreover, these approaches still possess limited generalization ability, particularly when dealing with scenes at a low signal-to-noise ratio (SNR). To overcome the above problems, we introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF). The LPC applies tailored path compensation coefficients to adapt to different objects in the scene, effectively reducing light wave attenuation, especially in distant regions. Meanwhile, the APF learns the precise Gaussian window of the illumination function for the phasor field, dynamically selecting the relevant spectrum band of the transient measurement. Experimental validations demonstrate that our proposed approach, only trained on synthetic data, exhibits the capability to seamlessly generalize across various real-world datasets captured by different imaging systems and characterized by low SNRs.
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