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Contributing to HoloKit Unity SDK

This guide outlines how to enhance and introduce new features to the HoloKit Unity SDK.

Adding New iPhone Models

HoloKit Unity SDK optimizes render parameters based on iPhone hardware specifications, ensuring accurate viewport positioning and sizing.

To add a new iPhone model to the Assets/ScriptableObjects/iOSPhoneModelList asset:

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  • Model Name: This is Apple's identifier for each device. Locate the model name here.

  • Description: Provides clarity for internal developers, supplementing the less descriptive Model Name.

  • Screen Resolution: Found on Apple’s official website, list the screen height (larger number) first, followed by the width.

  • Screen DPI: Also obtainable from Apple’s website, refers to the screen's DPI (Dots Per Inch), synonymous with PPI (Pixels Per Inch).

  • Viewport Bottom Offset: Indicates the offset between the device screen bottom and the viewport bottom measured in meters. For a detailed explanation, see the Phone Calibration Guide. However, you can only measure this property manually. You can leave this value to 0 and use Screen Bottom Border instead.

  • Camera Offset: The 3D distance from the iPhone's main camera to the screen's bottom center, detailed in the Phone Calibration Guide. Calculate this using Accessory Design Guidelines for Apple Devices.

  • Screen Bottom Border: Distance between the screen's bottom border and the phone's frame bottom. Determine this using the Accessory Design Guidelines for Apple Devices. You can use either Viewport Bottom Offset or Screen Bottom Border to set viewport positioning, but not both. While Viewport Bottom Offset offers higher accuracy, it is more challenging to calculate. In contrast, Screen Bottom Border is easier to compute but less precise. If both values are provided, the SDK defaults to using Screen Bottom Border for viewport positioning. If Viewport Bottom Offset is set to 0, the SDK will automatically utilize Screen Bottom Border.

The Accessory Design Guidelines for Apple Devices provide precise specifications for Apple devices, crucial for accurately calculating Camera Offset and Screen Bottom Border.

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How The SDK Works

The HoloKit Unity SDK primarily focuses on two functionalities: stereoscopic rendering and hand pose detection. This section delves into the intricacies of these systems.

Stereoscopic Rendering

The SDK offers two rendering modes: Mono and Stereo.

  • Mono Mode: Operates as a standard ARFoundation app, utilizing ARCameraBackground.

  • Stereo Mode: The SDK's key feature, it renders two separate viewports on the iPhone screen using dual cameras against a black background. HoloKitCameraManager, attached to the Main Camera GameObject, transforms an ARFoundation camera into a HoloKit camera.

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Key Components:

  • Mono Camera: Used in Mono mode.

  • Center Eye Pose: Represents the midpoint between the user's eyes in Stereo mode, aligning with the iPhone camera in Mono mode.

  • Black Camera: Renders the black background in Stereo mode.

  • Left/Right Eye Camera: Render the respective viewports in Stereo mode.

  • IPD: stands for inter-pupillary distance, the distance between the user's two eyes.

  • Far Clip Plane: Sets the farthest visible boundary for the stereo cameras.

  • Show Alignment Marker In Stereo Mode: An optional feature to display an alignment marker in the top-right corner in Stereo mode.

  • Supported Mono Screen Orientations: The list of supported screen orientations in Mono mode. Since the screen orientation is locked to LandscapeLeft under Stereo mode, when switching back to Mono mode, the SDK needs to know which screen orientations are supported.

  • HoloKit Generation: Specifies the model of HoloKit being used.

  • iOS Phone Model List: Specs for supported iOS devices.

  • Default Android Phone Model List: Specs for supported Android devices.

  • Custom Android Phone Model List: Custom list for unsupported Android models. Refer to the Phone Calibration Guide for custom model specifications.

Hand Pose Detection

HoloKit Unity SDK offers two hand pose detection releated features: hand tracking and hand gesture recognition, both utilizing Apple Vision's hand pose detection algorithm. This algorithm, primarily 2D in nature, identifies the 2D coordinates of 21 joints in the user's hands. While these coordinates are adequate for recognizing hand gestures, they fall short for 3D hand tracking, which aims to ascertain the 3D positions of these joints. To overcome this, we integrate the Apple Vision algorithm with the iPhone's LiDAR sensor. This combination allows us to first pinpoint the 21 joints' 2D coordinates and then match them with their depth values from the LiDAR sensor's depth map, thus achieving accurate 3D tracking.

Scripts related to hand pose detection are located in the Runtime\iOS folder, reflecting their exclusivity to iOS devices. We have written native Objective-C code to capture the user's hand pose and facilitate data marshalling from Objective-C to C#. This approach underpins the implementation of both hand tracking and hand gesture recognition functionalities.

The HandTrackingManager script manages the provision of 3D positions for the user's hand joints, while the HandGestureRecognitionManager script identifies the user's hand gestures. Both scripts utilize AppleVisionHandPoseDetector to server as a conduit, fetching native data from the Objective-C side.

The Runtime/iOS/NativeCode folder houses all the native Objective-C code for the SDK. Each native functionality is represented by three types of files: a header file, an implementation file, and a bridge file. The header file outlines the interface of the native class, the implementation file provides its actual functionality, and the bridge file facilitates marshalling between the unmanaged (Objective-C) and managed (C#) code. For instance, within the Runtime/iOS/NativeCode/AppleVisionHandPoseDetector-C-Bridge file, there are four native C functions. These functions correspond to and are linked with four marshalling functions in the AppleVisionHandPoseDetector C# class.

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AppleVisionHandPoseDetector offers the flexibility to process video frame images in either 2D or 3D mode. The HandGestureRecognitionManager requires only 2D hand poses, while the HandTrackingManager necessitates 3D hand poses. Consequently, when using HandGestureRecognitionManager alone, 2D hand poses can be obtained without activating the LiDAR sensor. In contrast, HandTrackingManager usage mandates turning on the LiDAR sensor to capture the user's 3D hand poses. Both managers rely on AppleVisionHandPoseDetector to access hand data from native code. When operated concurrently, they share the same AppleVisionHandPoseDetector instance, optimizing efficiency.

Low Latency Tracking

See HoloKit Low Latency Tracking repository for detailed explanation.

How To Work With The SDK

This SDK, being a Unity package, requires a carrier Unity project for development and testing. If you want to contribute or modify the SDK, start by cloning the carrier project. This repository includes the SDK as a git submodule. Within the carrier project, the SDK is integrated as a local folder, allowing for direct modifications and testing.

In the Assets/Samples folder of the carrier project, you'll find multiple SDK samples. To update existing samples or create new ones, simply copy the desired sample folder into the SDK's Samples~ folder. After making changes or additions, don't forget to update the package.json file in the SDK to reflect these modifications. This process ensures that your contributions are properly integrated and accessible within the SDK structure.

Potential Future Improvements

Enhancing the Smoothness of 3D Hand Tracking

The inherent limitation of using Apple Vision's natively 2D hand pose detection algorithm for 3D hand tracking introduces certain inaccuracies. A notable issue arises from the misalignment between hand pose detection and the depth map. Often, fingertips are incorrectly mapped to the background of the depth map, resulting in exaggerated depth values. Currently, we employ a basic method of using the second fingertip to correct these anomalies, but this leads to occasional glitches. A more effective solution would involve interpolating the movement of each hand joint between frames to achieve smoother results. Although this correction is presently handled in the Objective-C code, it could potentially be implemented either there or on the C# side for improved performance.

Enhancing Robustness and Variety in Hand Gesture Recognition

At present, our system recognizes only two hand gestures: Pinched and Five. Theoretically, it's feasible to expand this range to include gestures like One, Two, Three, and others. However, the main challenge lies in the potential conflicts that may arise when HandGestureRecognitionManager supports a broader array of gestures simultaneously. For instance, distinguishing between gestures like Pinched and Four can be particularly difficult. This area calls for further research and development to improve accuracy and ensure that the system can robustly recognize a wider variety of gestures without confusion.

Advancing Low Latency Tracking Performance

There is a continuous need for further optimization in our low latency tracking system. This involves refining the system's responsiveness and accuracy to minimize latency even further. For detailed information and specific areas of focus, please refer to the HoloKit Low Latency Tracking repository.