diff --git a/README.md b/README.md
index f86de3b..83319e1 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,45 @@
 # OpenDepthMap
+
+OpenDepthMap is an experiment in [Binocular Depth Mapping](https://en.wikipedia.org/wiki/Binocular_disparity), based around the [ultraleap](https://www.ultraleap.com/)'s first generation [LeapMotion](https://www.ultraleap.com/product/leap-motion-controller/) hand tracking sensors.
+
+While the LeapMotion devices are designed only to provide real-time skeletal modeling and pose estimation of the human hand, they also expose their raw camera data as part of a debugging tool. OpenDepthMap makes use of this data to feed a *computer vision pipeline* and a *block matching stereo correspondence algorithm* in order to estimate depth data for an entire 3D space.
+
+## The Tech
+
+OpenDepthMap is split into three parts, across four distinct programming languages. From the highest level, the flow of data is as follows:
+
+```
+LeapMotion Driver <---> LeapMotion Library <---> FFI Wrapper <---> Rust Interface Library <---> CV Pipeline
+```
+
+The LeapMotion devices can only interact with Linux hosts via the [`LeapMotion V2`]() API. This API is written in C++ and has no C bindings, making it hard to expose via Foreign Function Interface to another language. I solved this problem by writing a [small wrapper]() that exposes the C++ functions I need via a C interface. A Rust library ([`libodm`]()) then uses this wrapper to expose a type-safe, memory-safe, and error-handled interface to the LeapMotion device in a format that is easy to generate further FFI bindings from. 
+
+Originally, this project ended at Rust, and made use of the [`opencv`]() crate to handle the computer vision pipeline, but I was limited by a few critical functions missing from that binding, so I decided to add yet another layer of FFI into the mix. Using [PYO3](), `libodm` is exposed as a Python3 library, where the end-application uses it to control and read from the LeapMotion device, while also using Python's OpenCV bindings and [`numpy`]() for the final processing step.
+
+## Vision Pipeline
+
+The LeapMotion devices are dual-sensor infra-red cameras. Through the Image API, OpenDepthMap pulls both sensor outputs at the same time, and applies the following:
+
+ 1. A 3x3 blur
+ 2. A block matching stereo correspondence algorithm ([StereoBM](https://docs.opencv.org/3.4/d9/dba/classcv_1_1StereoBM.html))
+ 3. Contrast boost
+ 4. GrayScale pixel threshold
+ 5. Contour detection
+ 6. Contour filtering
+ 7. A mask based on only the largest contour
+ 8. Colour mapping
+ 9. Exposure boost
+ 10. Weighted image combination
+
+![Image pipeline](assets/process-flow.png)
+
+The result is a raw camera view with depth data overlayed on top of it.
+
+ <!-- ---
 Point cloud streams from Leap Motion cameras
 ```
 clang libclang-dev libopencv-dev python3-dev python-dev python3-opencv
 
 ```
 
-https://developer-archive.leapmotion.com/documentation/v2/cpp/index.html
\ No newline at end of file
+https://developer-archive.leapmotion.com/documentation/v2/cpp/index.html -->
\ No newline at end of file
diff --git a/assets/process-flow.png b/assets/process-flow.png
new file mode 100644
index 0000000..0815f3e
Binary files /dev/null and b/assets/process-flow.png differ
diff --git a/libodm/cpp/wrapper.cc b/libodm/cpp/wrapper.cc
index d087577..e209a52 100644
--- a/libodm/cpp/wrapper.cc
+++ b/libodm/cpp/wrapper.cc
@@ -31,7 +31,8 @@ void beginEventLoop()
         controller = new Controller();
 
         // Set device policy
-        controller->setPolicyFlags(Controller::POLICY_IMAGES);
+        controller->setPolicy(Controller::POLICY_IMAGES);
+        controller->setPolicy(Controller::POLICY_OPTIMIZE_HMD);
 
     }
 }