热成像总是难以驯服：刻画并应对纯热成像新视角合成的挑战
Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View Synthesis
针对低动态范围与逐帧光度漂移，提出轻量预处理加泼溅管线来扩展可用动态范围并稳定每帧光度，可直接迁移到其他低质量传感器（如事件相机、低光相机）的 NVS 优化中，缓解无 RGB 引导时的 floater 伪影。
原文依据 we introduce a lightweight preprocessing and splatting pipeline that expands usable dynamic range and stabilizes per-frame photometry
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论热成像相机在黑暗与恶劣条件下能提供可靠的可见性，但相比可见光图像，热成像图像用于新视角合成（NVS）仍然困难得多。这一困难主要源于廉价热传感器具有的两个特性。第一，热图像动态范围极低，这削弱了外观线索并限制了可用于优化的梯度。第二，热数据表现出快速的逐帧光度波动以及缓慢的辐射漂移，二者都会破坏对应关系估计，并在视角合成过程中产生高频漂浮物伪影，在除相机位姿外没有 RGB 引导时尤为严重。基于这些观察，我们引入一种轻量的预处理与泼溅管线，用以扩展可用动态范围并稳定逐帧光度。我们的方法在纯热成像 NVS 基准上取得最优性能，且无需任何针对特定数据集的调参。
英文原文摘要 1020 字符
Thermal cameras provide reliable visibility in darkness and adverse conditions, but thermal imagery remains significantly harder to use for novel view synthesis (NVS) than visible-light images. This difficulty stems primarily from two characteristics of affordable thermal sensors. First, thermal images have extremely low dynamic range, which weakens appearance cues and limits the gradients available for optimization. Second, thermal data exhibit rapid frame-to-frame photometric fluctuations together with slow radiometric drift, both of which destabilize correspondence estimation and create high-frequency floater artifacts during view synthesis, particularly when no RGB guidance (beyond camera pose) is available. Guided by these observations, we introduce a lightweight preprocessing and splatting pipeline that expands usable dynamic range and stabilizes per-frame photometry. Our approach achieves state-of-the-art performance across thermal-only NVS benchmarks, without requiring any dataset-specific tuning.