Geometric Processing for Image-based 3D Object Modeling
It addresses the need for cost-effective and flexible 3D modeling in fields like surveying or cultural heritage, but it is incremental as it reviews existing methods rather than introducing new ones.
This article summarizes the geometric processing workflow for image-based 3D object modeling, focusing on state-of-the-art methods for geo-referencing, dense image matching, and texture mapping to create dimensionally accurate and photorealistic 3D representations from images.
Image-based 3D object modeling refers to the process of converting raw optical images to 3D digital representations of the objects. Very often, such models are desired to be dimensionally true, semantically labeled with photorealistic appearance (reality-based modeling). Laser scanning was deemed as the standard (and direct) way to obtaining highly accurate 3D measurements of objects, while one would have to abide the high acquisition cost and its unavailability on some of the platforms. Nowadays the image-based methods backboned by the recently developed advanced dense image matching algorithms and geo-referencing paradigms, are becoming the dominant approaches, due to its high flexibility, availability and low cost. The largely automated geometric processing of images in a 3D object reconstruction workflow, from ordered/unordered raw imagery to textured meshes, is becoming a critical part of the reality-based 3D modeling. This article summarizes the overall geometric processing workflow, with focuses on introducing the state-of-the-art methods of three major components of geometric processing: 1) geo-referencing; 2) Image dense matching 3) texture mapping. Finally, we will draw conclusions and share our outlooks of the topics discussed in this article.