A Novel Intrinsic Image Decomposition Method to Recover Albedo for Aerial Images in Photogrammetry Processing

TL;DR

This paper introduces a new method for recovering surface albedo from aerial photogrammetric images, improving rendering realism and processing accuracy by reducing lighting effects.

Contribution

The paper proposes an innovative inverse model for albedo estimation from aerial images, enhancing photogrammetric workflows and rendering quality.

Findings

Outperforms existing albedo recovery methods.

Improves feature matching and dense point cloud generation.

Enhances photogrammetric processing accuracy.

Abstract

Recovering surface albedos from photogrammetric images for realistic rendering and synthetic environments can greatly facilitate its downstream applications in VR/AR/MR and digital twins. The textured 3D models from standard photogrammetric pipelines are suboptimal to these applications because these textures are directly derived from images, which intrinsically embedded the spatially and temporally variant environmental lighting information, such as the sun illumination, direction, causing different looks of the surface, making such models less realistic when used in 3D rendering under synthetic lightings. On the other hand, since albedo images are less variable by environmental lighting, it can, in turn, benefit basic photogrammetric processing. In this paper, we attack the problem of albedo recovery for aerial images for the photogrammetric process and demonstrate the benefit of albedo recovery for photogrammetry data processing through enhanced feature matching and dense matching. To this end, we proposed an image formation model with respect to outdoor aerial imagery under natural illumination conditions; we then, derived the inverse model to estimate the albedo by utilizing the typical photogrammetric products as an initial approximation of the geometry. The estimated albedo images are tested in intrinsic image decomposition, relighting, feature matching, and dense matching/point cloud generation results. Both synthetic and real-world experiments have demonstrated that our method outperforms existing methods and can enhance photogrammetric processing.