# A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking

arXiv: https://arxiv.org/abs/2012.01044 · HTML: https://mlsanity.com/paper/2012.01044/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Blendify -- Python rendering framework for Blender](https://mlsanity.com/paper/2410.17858/) (2024)
- [HuSc3D: Human Sculpture dataset for 3D object reconstruction](https://mlsanity.com/paper/2506.07628/) (2025)
- [Reconstructing Small 3D Objects in front of a Textured Background](https://mlsanity.com/paper/2105.11352/) (2021)
- [Physically-Based Simulation of Automotive LiDAR](https://mlsanity.com/paper/2512.05932/) (2025)

## Abstract (arXiv)

We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking. Applying SfM allows us to determine camera motions without manually defining feature tracks or calibrating the cameras used to capture the image data. With MVS we are able to automatically compute dense scene models, which is not feasible with the built-in tools of Blender. Currently, our framework supports several state-of-the-art SfM and MVS pipelines. The modular system design enables us to integrate further approaches without additional effort. The framework is publicly available as an open source software package.