一种通过逆渲染从航空摄影测量图像中恢复反照率的通用方法
A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering
AI总结：
本文提出一种基于逆渲染的通用航空摄影测量图像反照率恢复方法，利用可估计的太阳光照和场景几何解决不适定问题，无需额外输入即优于现有方法，并能改善特征匹配等图像处理任务。
摘要：
为合成3D环境进行户外场景建模需要从原始图像中恢复反射率/反照率信息，由于该过程中复杂的未建模物理现象（如间接照明、体积散射、镜面反射），这是一个不适定问题。该问题在实际背景下仍未解决。恢复的反照率可促进模型重照明和着色，从而进一步提升渲染模型的真实感及数字孪生的应用。通常，摄影测量3D模型仅将源图像作为纹理材质，这不可避免地将（拍摄时的）不需要的光照伪影嵌入到纹理中。因此，这些受污染的纹理对于需要实现真实感渲染的合成环境而言是次优的。此外，这些嵌入的环境光照进一步给不同图像间的光度一致性带来挑战，导致图像匹配的不确定性。本文提出了一种通用的图像形成模型，用于在自然光照下从典型航空摄影测量图像中恢复反照率，并推导出逆模型，通过逆渲染本征图像分解来解析反照率信息。我们的方法基于这样一个事实：在航空摄影测量中，太阳光照和场景几何都是可估计的，因此它们可以为这一不适定问题提供直接输入。这种基于物理的方法不需要典型基于无人机的摄影测量采集数据之外的任何额外输入，并且被证明优于现有方法。我们还证明，恢复的反照率图像反过来可以改善摄影测量中的典型图像处理任务，如特征和密集匹配、边缘和线条提取。
英文摘要：
Modeling outdoor scenes for the synthetic 3D environment requires the recovery of reflectance/albedo information from raw images, which is an ill-posed problem due to the complicated unmodeled physics in this process (e.g., indirect lighting, volume scattering, specular reflection). The problem remains unsolved in a practical context. The recovered albedo can facilitate model relighting and shading, which can further enhance the realism of rendered models and the applications of digital twins. Typically, photogrammetric 3D models simply take the source images as texture materials, which inherently embed unwanted lighting artifacts (at the time of capture) into the texture. Therefore, these polluted textures are suboptimal for a synthetic environment to enable realistic rendering. In addition, these embedded environmental lightings further bring challenges to photo-consistencies across different images that cause image-matching uncertainties. This paper presents a general image formation model for albedo recovery from typical aerial photogrammetric images under natural illuminations and derives the inverse model to resolve the albedo information through inverse rendering intrinsic image decomposition. Our approach builds on the fact that both the sun illumination and scene geometry are estimable in aerial photogrammetry, thus they can provide direct inputs for this ill-posed problem. This physics-based approach does not require additional input other than data acquired through the typical drone-based photogrammetric collection and was shown to favorably outperform existing approaches. We also demonstrate that the recovered albedo image can in turn improve typical image processing tasks in photogrammetry such as feature and dense matching, edge, and line extraction.
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