通过逆渲染恢复航拍图像反照率,提升三维建模真实感。
A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering
- 基于物理模型,利用太阳光照与场景几何信息解算反照率。
- 在真实航拍数据上优于现有方法,无需额外输入。
- 可提升特征匹配、边缘提取等摄影测量任务效果。
为构建合成三维环境,需从原始图像中恢复反射率(即反照率),但受间接光照、体积散射、镜面反射等未建模物理因素影响,该问题在实际中仍难以解决。现有航拍三维模型通常直接使用原始图像作为贴图,导致纹理中嵌入拍摄时的光照伪影,影响渲染真实性和图像匹配一致性。本文提出一种适用于自然光照下典型航拍摄影测量图像的通用图像形成模型,并通过逆渲染进行内在图像分解以恢复反照率。该方法利用航拍摄影测量中可估计的太阳光照与场景几何作为输入,构建基于物理的逆模型,仅依赖常规无人机采集数据,无需额外输入,实验表明其性能显著优于现有方法。同时,恢复的反照率图像还能有效提升摄影测量中的特征匹配、密集匹配、边缘和线段提取等典型任务效果。
原文摘要 · Abstract (English)
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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