Structure from motion is an algorithm that generates a 3D reconstruction (pointcloud) from a sequence of 2D images. Instructions on running the repo can be found below. The rough steps of my pipeline are:
i) Match keypoints between images
ii) Find a good image pair to initialize the reconstruction (many matches and significant rotation between images)
iii) Extend the reconstruction by resecting adjacent images with PnP and triangulating new points
iv) Refine camera parameters and 3D point coordinates with bundle adjustment regularly
Here are examples of the output I was able to generate:
If desired, clone this repository and setup the conda environment:
git clone https://github.com/rshilliday/sfm.git
cd sfm
conda env create -n sfm -f environment.yml
conda activate sfm
Now, run jupyter:
jupyter notebook
And open main.ipynb and hit "run all" to generate a 3D reconstruction of the "templeRing" dataset (http://vision.middlebury.edu/mview/data/).
I also created my own dataset from pictures of a Viking figurine. To generate a reconstruction of that, in the second cell of main.ipynb change n_imgs to 49 and change the imgset parameter of find_features() to 'Viking', and then hit "run all".