是否有机会使用ARCore进行3D目标检测与3D目标追踪？

Absolutely! ARCore is fully equipped to handle 3D object detection and tracking tasks—this is a core use case for building immersive, interactive AR experiences. Let me break down your options and how to get started:

1. ARCore's Native 3D Object Tracking #

ARCore has built-in support for 3D object tracking via its Object Tracking API, which is perfect for when you have specific physical objects you want to recognize and track. Here's the workflow:

• First, you'll need to scan your target object using the ARCore Object Scanner (a free tool for Android). Walk around the object, capturing it from multiple angles—this generates an .arobject file that ARCore uses as a reference for detection.
• Integrate this trained model into your app using ARCore's SDK. The API will automatically detect the object in the camera feed and track its 6-degree-of-freedom (6DoF) pose—meaning it keeps tabs on the object's position and orientation as it moves through 3D space.
• A quick code snippet to initialize object tracking in Android/Kotlin:

  val objectDetector = ArCoreApk.getInstance().createObjectDetector(
      context,
      ObjectDetectorOptions.builder()
          .setObjectLabel("MyCustomObject")
          .build()
  )
  

2. Pairing with Custom 3D Detection Models #

If you need to detect generic 3D objects (without pre-scanning each one), you can combine ARCore with a custom computer vision model (like TensorFlow Lite or PyTorch Mobile):

• Use ARCore to access the device's camera feed and spatial data. Pass each camera frame to your 3D detection model, which will identify objects and output their 3D poses or bounding boxes.
• Once an object is detected, use ARCore's Anchor API to pin a virtual marker or content to the object. ARCore will then handle the spatial tracking to keep that content aligned with the real-world object as it moves.
• This approach is ideal for use cases like detecting everyday items (e.g., cans, books) where pre-scanning every possible variant isn't feasible.

3. Key Tips for Success #

• Object Requirements: ARCore's native tracking works best with objects that are at least 10cm in size and have distinct textures or geometric features. Plain, reflective, or overly small objects might be harder to track reliably.
• Lighting Matters: Like most computer vision tools, ARCore performs best in well-lit environments with even lighting. Avoid extreme low light or harsh glare that washes out object details.
• Optimize for Performance: For real-time tracking, make sure to optimize your custom model (e.g., quantize TensorFlow Lite models) to run smoothly on mobile hardware—laggy detection will break the AR experience.

4. Real-World Use Cases #

• Product Visualization: Let users place 3D furniture models in their home and move them around by tracking the physical floor or existing furniture.
• Industrial Maintenance: Track machinery parts to overlay step-by-step repair instructions directly on the component.
• Educational Tools: Detect physical objects (like fossils or historical artifacts) to display 3D educational content that stays aligned with the real item.

内容的提问来源于stack exchange，提问作者Amarsaikhan Batjargal