# Geometric Processing for Image-based 3D Object Modeling

arXiv: https://arxiv.org/abs/2106.14307 · HTML: https://mlsanity.com/paper/2106.14307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 166 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [Computing the Stereo Matching Cost with a Convolutional Neural Network](https://mlsanity.com/paper/1409.4326/) (2014)
- [Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling](https://mlsanity.com/paper/1708.07987/) (2017)

## Abstract (arXiv)

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.