Kudan's Augmented Reality SDK is an engine for iOS and Android devices that allows mobile applications to recognise and track their physical surroundings using nothing more than hardware found in almost every smart device. It also offers its own 3D rendering to really bring your ideas to life. This engine can be included in any project, meaning existing apps can be enhanced with AR just as easily as new apps.
In addition to the native SDKs, we also provide a plugin for Unity to provide our tracking functionality whilst taking full advantage of Unity's powerful rendering capabilities.
KudanCV is Kudan's Computer Vision component of the framework, responsible for detection and tracking of images and environments. This framework, written in C++, is useful for those wishing to integrate Kudan with a third-party renderer or platform, or those who do not wish to integrate the full SDK in their app.
Kudan's native SDKs support iOS and Android. The Unity plugin allows apps to be built for iOS and Android, and also allows marker tracking in the editor via Play Mode. KudanCV is also currently available for iOS and Android, with a full release for desktop planned in the future.
Demos of our products can be found on the demo page of our website.
KudanAR is encapsulated within its own module, so you can add a new view to your app and that will be your AR view. Everything is usually encapsulated within a subclass of a UIViewController. This can just be dropped into a larger app and called in the usual ways.
You can work with the ARView directly, though you will have to set everything up yourself, including the ARCamera and all the necessary viewports.
No, Kudan does NOT collect any personal information from its users. The only permissions we require from a device to run AR are related to the camera. Any other permissions, such as access to the internet or location services, are not directly required by us, and as such we do not ask for them.
Cloud recognition is appealing as it allows developers to store a large amount of markers without bloating their apps size. The trade off is that your image detection will suffer from network latency. Even with a low network latency the performance of your marker detection is going to be significantly worse than if your marker was stored locally. This results in a frustrating user experience as the time spent looking at a marker is greatly increased. In order to achieve an acceptable level of network latency developers would need to ensure they had servers close to wherever their application was being distributed, which is expensive.
An alternative suggestion to cloud recognition is to temporarily download and store the markers for a given situation such as a users location. Updating markers using this method will remove any network latency issues from marker detection, whilst still providing developers with the flexibility to update their applications content without having to alter the underlying source code. The Kudan AR Toolkit provides developers with the ability to generate KARMarker files, which can store a large amount of markers in one lightweight marker set. This is useful when downloading and updating the markers you application uses as images do not need to be downloaded one at a time and names can be set automatically.
We have a simple example that demonstrates how to load markers and augmentations from a downloaded path here: https://github.com/kudan-eu/CMS-Demo-iOS