Overall, the AR ecosystem is still small. Nevertheless, it’s fragmented. Google develops ARCore, Apple creates ARKit and Microsoft is working on the Mixed Reality Toolkit. Fortunately, Unity started unifying these APIs with the ARInterface.
The traditional mobile AR app development cycle includes compiling and deploying apps to a real device. That takes a long time and is tedious for quick testing iterations.
A big advantage of ARKit so far has been the ARKit Unity Remote feature. The iPhone runs a simple “tracking” app. It transmits its captured live data to the PC. Your actual AR app is running directly in the Unity Editor on the PC, based on the data it gets from the device. Through this approach, you can run the app by simply pressing the Play-button in Unity, without native compilation.
This is similar to the Holographic Emulation for the Microsoft HoloLens, which has been available for Unity for some time.
The great news is that the new Unity ARInterface finally adds a similar feature to Google ARCore: ARRemoteInterface. It’s available cross-platform for ARKit and ARCore.
We don’t have a Christmas tree in our apartment. But in today’s world, this is what Augmented Reality is for, right? Therefore, I decided to create an AR Christmas Tree in 5 minutes. This also gave me an opportunity to check out the new Google ARCore Developer Preview 2.
Christmas Tree 3D Model
First off, you need a 3D model of a Christmas tree. Two of the most accessible sources are Google Poly and Microsoft Remix 3D. Sticking to models created directly by Google and Microsoft, these two are the choices:
ARCore has a great feature – light estimation. The ARCore SDK estimates the global lighting, which you can use as input for your own shaders to make the virtual objects fit in better with the captured real world. In this article, I’m taking a closer look at how the light estimation works in the current ARCore preview SDK.
Following the basic project setup of the first part of this article, we now get to the fascinating details of the ARCore SDK. Learn how to find and visualize planes. Additionally, I’ll show how to instantiate objects and how to anchor them to the real world using Unity.
ARCore by Google is still in preview and only runs on a select few phones including the Google Pixel 2. In this article, I’m creating a demo app for ARCore using the ARCore SDK for Unity (Preview 1).
It’s following up on the blog post series where I segmented a 3D model of the brain from an MRI image. Instead of following these steps, you can download the final model used in this article for free from Google Poly.
In the first part of the article, we captured a 360° photo using a Samsung Gear 360 camera. Now, we’ll create a new Unity project for Android. Using the right shader and material, we can assign the cylindric projection to a Skybox. This is the perfect 360° photo viewer for Unity, which can then be easily deployed to a Google Daydream / Cardboard VR headset!
Loading the 360° Photo in Unity
The Skybox in Unity is the easiest way to show a 360° photo in VR. Note that 360° 2D and 3D video will be supported out-of-the-box in the upcoming Unity 2017.3 release, according to the current Unity roadmap.
Are there any other ways to 3D print segmented medical data coming from MRI / CT / Ultrasound by splitting it in two halves?
In the first part of this article, the result was that the support structures required by a standard 3D printer significantly reduce the details present on the surface of the printed body part.
Christoph Braun had the idea for another method to reduce the support structures to a minimum: by splitting the object in two halves, each has a flat surface area that can be used as the base for the 3D print.
Based on the 4-part tutorial where we segmented the brain from an MRI image, one of the most interesting application areas is printing such 3D models. In that sense, it makes no difference if the data is coming from an MRI (e.g., a brain or tumor), CT (e.g., the skull) or ultrasound. In this article, we’ll look at how to prepare the 3D model for 3D printing.
In this part, we print the MRI brain model using the Witbox 2 3D printer with plastic and deal with support structures. The aim is to make this process accessible for everyone – so you don’t need specialized and expensive software & hardware; we’ll instead use open source and free tools as much as possible.