OPTIMIZING MESH RECONSTRUCTION AND TEXTURE MAPPING GENERATED FROM A COMBINED SIDE-VIEW AND OVER-VIEW IMAGERY
Image-based 3D modelling are rather mature nowadays with well-acquired images through standard photogrammetric processing pipeline, while fusing 3D dataset generated from images with different views for surface reconstruction remains to be a challenge. Meshing algorithms for image-based 3D dataset requires visibility information for surfaces and such information can be difficult to obtain for 3D point clouds generated from images with different views, sources, resolutions and uncertainties. In this paper, we propose a novel multi-source mesh reconstruction and texture mapping pipeline optimized to address such a challenge. Our key contributions are 1) we extended state-of-the-art image-based surface reconstruction method by incorporating geometric information produced by satellite images to create wide-area surface model. 2) We extended a texture mapping method to accommodate images acquired from different sensors, i.e. side-view perspective images and satellite images. Experiments show that our method creates conforming surface model from these two sources, as well as consistent and well-balanced textures from images with drastically different radiometry (satellite images vs. street-view level images). We compared our proposed pipeline with a typical fusion pipeline - Poisson reconstruction and the results show that our pipeline shows distinctive advantages.