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HEALIC Scanner

A white-label iOS app that turns an iPhone's TrueDepth camera into a 3D foot scanner — reconstructing the foot in real time and producing the mesh that custom orthotic insoles are manufactured from.

Role
Senior engineer — reconstruction quality & mesh export
Platform
iOS · Swift · C++ · Metal
Status
In production — 12+ branded apps (EU / US / Asia)
Links
HEALIC FootScanner · Flippi · ped3D · All ReTiSense apps

The hard problem

Raw depth scans from a phone are noisy, holey and fragmented — and a custom orthotic is manufactured directly from the scan. The output has to be one clean, watertight, dimensionally accurate mesh, produced in seconds, by a non-technical operator, on consumer hardware.

That last mile — from raw point soup to a manufacturing-ready model — is where most of the difficulty lives, and it's the part I owned.

What I built

  • Reconstruction pipeline work — the depth-frame → 3D model build on top of ICP alignment and surfel fusion (C++ core, Metal-accelerated preview).
  • Point-cloud & mesh cleanup — outlier and noise removal, fragment consolidation, so a scan resolves into a single coherent foot surface.
  • Surface smoothing — tuned to remove sensor noise without eroding the anatomical detail the insole geometry depends on.
  • Meshing & export — point cloud → watertight mesh (Poisson reconstruction) → STL plus measurement metadata consumed by insole CAM tooling.

The scanning stack builds on the open-source StandardCyborgFusion engine; my work was the reconstruction-quality layer above it. Landmark detection, the white-label app architecture and order-flow integration were built with the wider team.

How the system works

  • TrueDepthRGB + depth @ 30 fps, live quality checks
  • FusionICP alignment + surfel fusion (C++/Metal)
  • Cleanupoutlier removal, consolidation, smoothing
  • MeshPoisson reconstruction → watertight STL
  • Manufacturemeasurements + metadata → insole CAM

Scanning runs two paths in parallel: a GPU-rendered live preview so the operator sees the model forming in real time, and a CPU reconstruction that accumulates the final 10–25k-point cloud. Quality gates (distance, tilt, lighting, alignment) guide the operator before a bad scan ever reaches the mesh stage.

Stack

SwiftC++MetalAVFoundation / TrueDepth SceneKitICP / surfel fusionPoisson meshing Parse