AI-Powered 3D Creation from a Single Photo

A client wants to add a feature: scan an object with one camera and instantly see its 3D model in AR—no LiDAR required, no photogrammetry, just neural networks. The challenge: limited device memory and server latency. Our solution mixes on-device depth estimation with cloud-based reconstruction, adapting the architecture to hardware and use case.

Classic approaches need dozens of photos or special equipment. Neural network generation from a single image is realistic but has quality limits when done entirely on-device. Our extensive experience in mobile AR/ML (over 8 years) lets us find the best balance. If you face a similar problem, we design a pipeline for your needs—contact us and we'll provide a tailored proposal.

Problems We Tackle

• Limited On-Device Resources: Mobile devices have restricted memory and compute. Running a full 3D reconstruction network locally is impossible in many cases. None of the current mobile chips can handle large models (>100M parameters). We offload heavy computations to the cloud, using local models as fallback.
• Depth Estimation Quality: Single-image depth is noisy. We use DepthPro Core ML on iOS for rough geometry, then refine with server-side TripoSR. Depth error averages 5-15% depending on lighting. Post-processing improves it to 2-5%.
• Format Compatibility: Export must support AR Quick Look (USDZ), Android (glTF), and web (glTF/OBJ). We convert to all three. See comparison table below.
• User Experience: Scanning must be simple. We guide the user to capture a clear image. No manual calibration required.
• Real-time Preview: On devices with LiDAR, ARKit provides a live mesh within 0.5 seconds. On others, we show a low-resolution point cloud. These previews assure the user that scanning works.

How We Implement

Step 1: Image Capture

• Single photo via camera. For LiDAR, we also capture depth frames.
• Our apps need at most one image for initial geometry.

Step 2: On-Device Depth Estimation

• iOS: Use DepthPro Core ML to generate a disparity map in ~0.2s.
• Android: Use the mediapipe depth model in ~0.3s.
• These models are accurate to ~5% near centers but degrade at edges.

Step 3: Server-Side Reconstruction

• Send depth map and RGB to server.
• Run TripoSR (20x faster than traditional SfM) to generate a watertight mesh in 2-5 seconds.
• Poisson reconstruction smooths surfaces. Edge cases (e.g., textures with low contrast) may reduce quality by 10-20%.
• Server uptime is guaranteed at 99.95%.

Step 4: Texture Projection

• Project original photo onto mesh using UV mapping.
• For multiple angles, we use a video sequence; one image suffices for initial textures.

Step 5: Export

• Export to USDZ, glTF (GLB), and OBJ.
• Files rarely exceed 50 MB for typical objects.
• Provide AR Quick Look for iOS with automatic detection.

Apple Developer Documentation on ARKit Depth Maps, 2024 TripoSR: Fast 3D Object Reconstruction from a Single Image, Zhengyi et al., ArXiv 2024

What's Included in Our Service

• Documentation: Complete API references and integration guides.
• Access: Private cloud endpoints with SLA 99.9%.
• Training: 2-day workshop for your team.
• Support: 6 months post-launch, including bug fixes and performance tuning.

Company Metrics

• 8+ years of experience in mobile AR/ML.
• 50+ projects delivered for clients worldwide.
• Guaranteed fast turnaround: basic pipeline in 3 weeks, advanced in 10 weeks.
• 5 years on the market with verified client success stories.

In summary, we provide a robust solution for single-photo 3D generation. None of the steps are overly complex, and we ensure format compatibility. Contact us to discuss your specific needs—our team is ready to deliver a cost-effective solution typically ranging from $30k to $60k.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.