Geometric Processing for Image-based 3D Object Modeling

TL;DR

This paper reviews the geometric processing workflow for image-based 3D object modeling, emphasizing recent advances in geo-referencing, dense image matching, and texture mapping to enable accurate, realistic 3D reconstructions.

Contribution

It provides a comprehensive overview of state-of-the-art methods in geometric processing for image-based 3D modeling, highlighting recent technological developments.

Findings

Advanced dense image matching algorithms improve 3D reconstruction accuracy.

Geo-referencing techniques enable precise spatial positioning of models.

Texture mapping methods enhance photorealistic appearance of 3D objects.

Abstract

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.