MLKit-ARKit

This simple project detects objects using Firebase ML Kit and tags them in with 3D labels in Augmented Reality.

Based on: CoreML-in-ARKit

Demo Video

Language: Swift 4.0

Content Technology: SceneKit, Firebase ML Kit

Note: SceneKit can achieve a 60 FPS on iPhone7+ - though when it gets hot, it'll drop to 30 FPS.

Status

This sample is no longer actively maintained and is left here for reference only.

Footnotes

• SceneKit Text Labels are expensive to render. Too many polygons (too much text, smoothness, characters) - can cause crashes. In future, SpriteKit would be more efficient for text-labels.

• Whilst ARKit's FPS , is displayed - ML Kit's speed is not. However, it does appear sufficiently fast for real-time ARKit applications.

• Placement of the label is simply determined by the raycast screen centre-point to a ARKit feature-point. This could be altered for more stable placement.

Building Blocks (Overview)

Get ML Kit running in real time in ARKit

• What we do differently here is we're using ARKit's ARFrame as the image to be fed into ML Kit.

let pixbuff : CVPixelBuffer? = (sceneView.session.currentFrame?.capturedImage)

• We also use Threading to continuously run requests to ML Kit in realtime, and without disturbing ARKit / SceneView

let dispatchQueueML = DispatchQueue(label: "dispatchqueueml")
...
loopMLKitUpdate() // on viewLoad
...
func loopMLKitUpdate() {
    dispatchQueueML.async {
        // 1. Run Update.
        self.updateMLKit()
        // 2. Loop this function.
        self.loopMLKitUpdate()
    }
}

Add 3D Text

• Add a Tap Gesture.
• On Tap. Get the raycast centre point, translating it to appropriate coordinates.
• Render 3D text at that location. Use the most likely object.