A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering

• URL: http://arxiv.org/abs/2409.03032v1
• Date: Wed, 4 Sep 2024 18:58:32 GMT
• Title: A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering
• Authors: Shuang Song, Rongjun Qin,
• Abstract summary: This paper presents a general image formation model for albedo recovery from typical aerial photogrammetric images under natural illuminations. Our approach builds on the fact that both the sun illumination and scene geometry are estimable in aerial photogrammetry.
• Score: 7.874736360019618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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