3D Object Detection in Augmented Reality: How It Works
Object tracking lets an AR app recognize a physical object by its 3D shape and keep digital content anchored to it as the camera and object move. Unlike image tracking, it needs no markers—just the object itself. Our team brings 5+ years of AR development experience and 10+ successful object tracking projects. Contact us to evaluate your object.
At its core, object tracking builds a point cloud and extracts feature points (typically 500–2000 points per object). ARKit on iOS uses ARObjectScanningConfiguration for scanning and ARWorldTrackingConfiguration with detectionObjects for recognition. A full API description is available in the ARKit documentation. Recognition accuracy reaches 5–10 mm under good lighting. On A12+ devices, scanning takes 10–15 minutes, and the resulting .arobject file ranges from 1 to 50 MB. The algorithm uses ORB feature descriptors for robust matching. The system supports up to 100 objects in a single session with 50% memory usage. Typical project costs range from $5,000 to $20,000; using existing CAD models can save up to 30%.
When Should You Use 3D Tracking?
Image tracking works with flat images—stickers, posters, screens. If the object is three-dimensional, content will "float" as the viewing angle changes. Object tracking uses a point cloud to determine precise position and orientation in 3D. It is the only way to annotate an engine block or overlay a schematic on industrial machinery.
Object Recognition Suitability
| Good candidates | Poor candidates |
|---|---|
| Toys with intricate patterns | Monochrome plastic casings |
| Appliances with control panels | Glass/transparent objects |
| Industrial equipment with labels | Polished metal surfaces |
| Packaged boxes | Soft deformable objects |
| Automotive parts | Objects without a fixed shape |
Glass and mirror surfaces are poor candidates; for them we use markers or LiDAR mesh matching.
Solving the Dirty Object Problem: A Case Study
In a service AR app project for a service center, our client faced a problem: a technician points the camera at an engine block, and ARKit fails to recognize it because of oil and grime. The clean reference .arobject did not work. We scanned several variants (clean, moderately dirty) and added all of them to detectionObjects. Recognition accuracy rose from 60% to 92%. The technician found parts 30% faster than manual catalog lookup.
Vuforia Model Targets: An Alternative to ARKit
ARKit Object Detection requires physical scanning of the object. Vuforia Model Targets recognizes objects from CAD models (STEP, OBJ, FBX) with no physical scanning. This is a game-changer for industries where CAD data already exists. Vuforia licenses start at $840/year, saving up to 50% of the budget compared to a custom solution.
| Feature | ARKit Object Detection | Vuforia Model Targets |
|---|---|---|
| Requires physical object? | Yes | No (needs CAD model) |
| Accuracy | 5–10 mm | 5–10 mm |
| Tolerance to dirt/grime | Low | Medium (more robust algorithm) |
| Licensing | Free | Paid, from $840/year |
| Supported platforms | iOS | iOS + Android |
ARKit is faster to set up; Vuforia wins when a CAD model is available. We help you choose the right technology—ask for a free consultation.
What We Deliver (Project Deliverables)
- Object Analysis Report: evaluation of texture, shape, and operating conditions; technology recommendation (ARKit / Vuforia / ARCore).
- Reference Object File: scanned
.arobjector prepared CAD model. - AR Module: fully configured detection and content anchoring.
- Backend Integration: persistent state and content loading from database.
- Testing Documentation: results under various lighting and dirt conditions.
- User Training: detailed scanning guidelines and manuals.
- Post-Launch Support: maintenance and updates for 6 months.
Typical Timeline and Milestones
- Analysis and scanning — 3–5 days.
- Prototype development — 5–10 days.
- Content integration and testing — 5–10 days.
- Final polish and deployment — 3–5 days.
Basic object detection with one object takes 1–2 weeks. Complex projects take 3–5 weeks. Pricing is determined after analysis. Request development—we will prepare a commercial proposal.
Tracking Moving Objects
For moving objects (conveyor parts, robots) we use MediaPipe Object Detection (COCO SSD) combined with depth estimation via LiDAR. Tracking accuracy degrades above 0.5 m/s. We applied this approach on a factory assembly line, reducing part identification time by 40%.
Our AR object recognition and object tracking capabilities leverage ARKit Object Detection for precise results. Get a consultation—let's discuss your object and choose the right solution. Our company has over 5 years of experience and has completed 12 object tracking projects for industrial clients. We guarantee a recognition accuracy of at least 90% for textured objects under standard lighting. Our expertise spans AR application development, ARCore, and AR development for various platforms.
Note: ARKit Object Detection, Vuforia Model Targets, and ARCore are examples of technologies used in industrial AR and service AR apps.