diff --git a/README.md b/README.md
index 4ec4579..13ebd88 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ We follows the steps in the paper. First, to construct a simple system, we use o
### Resampling the light vector
The ```icosahedron_cosntruction.m``` and ```subdivide.m``` handle the generation of the creation of a subdivided half icosahedron to uniformly resample the light vector. And ```resampling_light_vector.m``` find the one light vectors closest to the resample base. To create a icosahedron, we follow the instruction in the ```/reference/uniform_sampling.jpg``` to get each vertices and midpoints from the gloden ratio. To subdivide the icosahedron, a simple method to divide the edge evenly and create a triangle is used. To find the closest light vector, we sorted the light vectors by its distance to the resulted vertices and select the top.
-
+
### Load the images with only the unique light vector
After the light vectors are selected, since the light vector and the data are indexed in the same order, we only read the image indexes the same as the light vectors.
@@ -33,7 +33,14 @@ The local normal estimation from the ratio images to remove the unknown light am
Select the scale for each image. Then create the slant and tilt, the toolbox handles for us. Notice that the final image will rotate by 90
# results
-The images could be found in ```/results```
+## teapot
+normal image
+
+
+simple system
+
+
+More images could be found in ```/results```
```matlab