Merge branch 'master' of https://github.com/rshilliday/sfm

README.md

@@ -1,2 +1,28 @@
-# sfm
-An implementation of incremental structure from motion
+# An implementation of incremental Structure from Motion
+
+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:
+
+![](results/side-by-side.png)
+
+
+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".