LiDAR Foot Scanner App Development

@corpmember29

Hello, LiDAR Foot Scanner App I HAVE CREATED SIMILAR PROJECTS BEFORE AND I CAN SHOW YOU. I have gone through your requirements and understand that you need an iOS LiDAR foot-scanning app capable of detailed 3D capture, accurate measurements, dorsal outline extraction, image capture, watertight STL generation, and secure data transmission. I have 13+ years of experience and can develop this using Swift, ARKit, LiDAR/SceneDepth, RealityKit, Metal, and appropriate 3D processing libraries. I will implement guided scanning with distance/movement feedback, automated girth measurements, mesh cleanup, STL/CSV/JSON generation, secure upload, and TestFlight delivery. Accuracy will be validated against your calibrated reference and manual measurements during testing. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE FROM ZERO TO PUBLISHING ON STORES. I am available according to your convenient time zone and can successfully implement this project from start-to-finish. Thanks Christina

@ozbas

❤️❤️❤️ Greetings ❤️❤️❤️ I have carefully reviewed your LiDAR Foot Scanner requirements. I understand that the key challenge is not simply creating an iOS camera app, but producing repeatable 3D geometry and measurements that satisfy your calibrated accuracy targets. I am Ozgur Ozbas, a Full-Stack Mobile Developer with 10+ years of experience in mobile applications, Flutter, iOS/Android, APIs and backend integrations. For this project, I would structure development around native Swift/ARKit components where required, with clear milestones for: • LiDAR/SceneDepth capture • Guided scanning distance and movement • Foot-region isolation • Depth/mesh processing • Watertight STL generation • 12-point girth measurements • Dorsal outline extraction • ~24 reference photos • CSV/JSON measurement export • Secure endpoint upload • TestFlight delivery I would treat the sub-millimetre and ±1 mm requirements as measurable validation criteria rather than simply claiming accuracy. The scanning pipeline should be calibrated against your physical reference last and tested repeatedly before the final implementation is accepted. Q1. Which LiDAR-enabled iPhone/iPad models must be supported? Q2. Can you provide the calibrated last/reference dataset and exact locations/formulas for the 12 girth measurements?

@Hammadhassan21

Hey there, "I can show you some similar apps I’ve built previously" What caught my attention is how detailed this scan needs to be. For bootmaking, missing something as small as a bunion or overlapping toe could affect the final fit, so accuracy has to come first. I’ve worked with iOS apps and LiDAR/3D capture workflows, including ARKit and SceneDepth, and I’m comfortable working with the scan data through to STL generation. I’d first get the guided scanning and depth capture reliable, then add the 12 measurements, 24 reference photos, watertight STL export, and secure upload before validating against your calibrated last. Do you already have the calibrated last ready for the initial accuracy testing? Looking forward to collaborating with you. Portfolio: https://www.freelancer.pk/u/Hammadhassan21/Top-Mobile-App-Expert Best regards, Hammad Hassan...

@starttechno

This LiDAR foot scanner needs to capture more than a basic AR mesh: bunions, hammertoes, callouses, overlapping toes and nail contours must remain usable in the final STL. I will build a native Swift iOS/iPadOS app using ARKit SceneDepth, RealityKit and Metal, with an ARSession pipeline that provides live distance and movement-speed guidance during capture. The scanner will generate a stable foot mesh, isolate the scan region, close holes, remove noise, and validate/watertight the result before STL export. I will implement the twelve girth measurements, dorsal outline tracing, and approximately 24 synchronized still images for colour and surface reference. The export package will contain STL plus JSON or CSV measurements and images, uploaded to your secure endpoint through a clear API layer. I will also deliver the TestFlight build and complete Xcode project. Since sub-millimetre accuracy depends on the specific LiDAR device, foot positioning and calibrated-last reference, I will include a calibration and comparison workflow to measure mesh and girth variance against your test last, then tune filtering and measurement planes accordingly. I have extensive native mobile and 3-D/depth workflow experience, though I would confirm your exact scanning protocol before finalizing the capture UX. Should the secure endpoint accept one multipart upload containing the STL, measurements and image set? Muhammad Saad

@ahmadayaz

I specialize in developing a custom bootmaking app for LiDAR-enabled iOS devices. Using ARKit, LiDAR, and CoreML, I will create a precise foot scanner for sub-millimeter accuracy, automated measurements, and seamless data transmission. With expertise in measurement tools and 3-D scanning, I will ensure intuitive scanning, accurate results, and smooth visualization using SceneDepth, CoreML, and RealityKit. The app will deliver precise mesh accuracy, automatic measurements within ±1 mm, seamless STL generation, a TestFlight build for testing, and a secure data transmission system. Let's discuss color reference integration, image processing requirements, scalability plans, and potential integrations to tailor the solution for your long-term success. Let's transform the custom bootmaking industry together.

@InterconnectBPO

Understanding the goal of creating a professional-grade foot scanner app is essential. Our team will leverage ARKit and LiDAR capabilities to ensure sub-millimetre accuracy while capturing every contour of the foot. To meet your requirements, we’ll implement real-time feedback for users to maintain optimal scanning speed and distance. The automatic girth measurements and image capture will be integrated seamlessly, ensuring the data is packaged and transmitted securely. Our expertise in Swift, Metal, and STL export libraries aligns perfectly with your project needs. Also, communication, quality, and on-time delivery are priorities. If you'd like, I can also share similar work we've completed and discuss the best approach for your project. Regards, JP

@n2rtechnologies

Hi, When you say sub-millimetre accuracy on the mesh, are you testing that against a physical last in your shop, or do you have a calibrated reference model you'll provide? That'll shape how we validate the depth capture and whether we need multiple scan passes or real-time feedback loops to hit that tolerance. We've shipped depth-based mobile work before and know the ARKit + LiDAR pipeline well enough to nail this. Regards, Nurul Hasan

@rbtech1984

Hello!! A professional LiDAR foot scanner will be built for iPhone and iPad, capturing detailed 3D contours, accurate girth measurements, reference photos, and a clean watertight STL with secure data delivery. * Will the scanner be used mainly with bare feet? * Do you already have the secure endpoint for file uploads? * Would you prefer CSV or JSON for measurements? The solution will use ARKit, SceneDepth, Metal, and Swift with guided scanning, real-time distance and movement alerts, automated measurements, photo capture, mesh cleanup, STL export, and TestFlight delivery. Relevant 3D scanning and mobile projects have been completed, with strong focus on accuracy and reliable production-ready results. Turning precise foot data into a practical bootmaking workflow is exactly the goal. Let us discuss your workflow and accuracy testing in chat. Best regards Farhin B

@jitendras097

Hi, I understand this is not a basic AR scanning app—the key requirement is a production-quality foot scan that captures fine contours and produces a watertight STL with reliable measurements for custom bootmaking. I’m Jitendra, a full-stack developer with 10+ years of experience, and I have already worked on similar 3D/AR mobile workflows involving camera/depth data, measurement capture and automated file processing. I can build the iOS app in Swift using ARKit/SceneDepth, RealityKit/Metal and LiDAR, with guided scanning, real-time distance/speed validation, 12 automatic girth measurements, dorsal outline extraction, ~24 reference images and STL/CSV/JSON generation. The pipeline will include mesh cleanup, hole filling, watertight validation and calibration testing against your reference last. I’ll also implement secure API upload and deliver the TestFlight build plus complete Xcode source. I’d suggest validating the sub-mm accuracy target with an early technical prototype before committing to the full build, since real-world LiDAR accuracy depends heavily on device, calibration and scanning conditions. I can share relevant 3D/AR work and propose milestones after reviewing your reference scan and accuracy test procedure. Thanks, Jitendra

@a4logic

Hello, I'm Yasir Shah, the seasoned developer behind the A4Logic team with 15+ years of building high-quality, scalable solutions under my belt. My core skills in mobile app development touch on nearly every technology mentioned in your project description. As an expert in ARKit, LiDAR, SceneDepth, Metal, Swift, and Objective-C among others, I promise to deliver not just a functional app but one that meets and exceeds your acceptance criteria. While I don't have direct experience with orthotics or shoe-making, I have valuable experience in creating precise and detailed applications that align perfectly with your objectives for this LiDAR Foot Scanner App. The attention to detail required for capturing and preserving each foot contour is second nature to me given my work in different industries. Plus, I am no stranger to delivering watertight STLs with sub-millimeter accuracy at every stage important in 3d scanning. With such a complex project, communication is essential. My dedication to clear communication, commitment to quality and providing long-term support ensures that you won’t just hire a developer but a partner you can count on throughout the development process and beyond completion. Let's get started on this exciting endeavor together! Thanks!

@TeamGTW

Hello! Based on your project description, you are looking to build an iOS LiDAR foot scanning application that captures highly detailed 3D foot geometry for custom bootmaking, including fine contours, girth measurements, dorsal outlines, reference photographs, and accurate STL generation. The workflow will guide users during scanning, detect movement or distance issues, package the mesh and measurement data, and securely transmit the complete scan set to your endpoint. I will focus on developing the app in Swift using ARKit, LiDAR SceneDepth, RealityKit and Metal where appropriate, with careful depth processing, mesh reconstruction, surface cleanup, watertight STL generation, automated measurement extraction, and scan quality validation. I will structure the capture pipeline around your accuracy targets and provide TestFlight builds, testing support, and the complete Xcode project. I specialize in mobile app development with 7+ years experience and I have done similar work in past please open the chat window so I can share with you. Thank you for considering my proposal. I look forward to hearing from you soon. Best regards, Nikita Gupta.

@cliftonc20

Hello, this is a precise mobile 3D capture problem, not a typical app build, and the acceptance criteria make that clear. The real engineering risk is measurement repeatability under handheld capture drift, not just producing a visually good mesh from ARKit data. I usually structure systems like this around separate capture, reconstruction, measurement, and delivery stages so each can be validated independently. The guided UX matters as much as the reconstruction path because distance, sweep speed, and foot pose will dominate downstream accuracy. The closest work in my background is AI Translator Plugin for real-time native processing under live input constraints, and Enterprise ProxyTool Client App for production client architecture with strict local processing and backend handoff. For this app, I’d separate scan guidance from geometry extraction, then treat girth calculations and dorsal tracing as deterministic post-processing against a stable reference frame. I’d also define validation passes for watertight mesh quality, measurement variance, and upload integrity before packaging STL, images, and JSON. This kind of system needs to be designed for repeatable production use, not a one-off demo. If useful, I can sketch the validation and packaging architecture first, including where repeatability checks should sit before TestFlight. Clifton

@invoketech

HELLO, I HAVE CREATED SIMILAR LiDAR, 3D SCANNING AND iOS APPLICATIONS BEFORE AND I CAN SHOW YOU RELEVANT WORK. I have 10+ years of experience in iOS development, Swift, ARKit, LiDAR/SceneDepth, RealityKit, 3D processing and secure API integration. I understand you need a guided foot-scanning workflow that captures detailed contours, automatically measures key points, generates a watertight STL and securely transfers the complete scan package. I can implement real-time distance/speed guidance, depth capture, mesh processing, 12-point measurements, photo capture, STL/CSV/JSON generation and secure upload. WE WILL WORK WITH AGILE METHODOLOGY AND I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. I eagerly await your positive response. Thanks, InvokeTech

@mobiwebsolutions

✋ Hi There!!! ✋ THE PROJECT GOAL: BUILD A PROFESSIONAL LIDAR FOOT SCANNER APP WITH ACCURATE 3D CAPTURE, MEASUREMENTS, PHOTOS, AND STL EXPORT. 1. Swift iOS app using ARKit, LiDAR, SceneDepth, and Metal. 2. Guided scanning with real-time distance and movement feedback. 3. Automatic 12-point girth measurements and dorsal outline tracing. 4. High-quality watertight STL generation with CSV or JSON data. 5. Secure upload, TestFlight build, accuracy testing, and complete Xcode delivery. Similar LiDAR and 3D scanning projects have been completed involving depth capture, mesh processing, measurement workflows, and mobile visualization. <-- Questions --> 1. Do you have a calibrated last and reference scans for accuracy testing? 2. What secure endpoint and upload authentication should the app use? Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@WebcoderSaurabh

Hi, here's how I'd hit your accuracy targets. Capture: ARKit SceneDepth on LiDAR devices, fusing depth frames with confidence maps while guided UI prompts (distance/speed warnings driven by tracking-state + frame-to-frame motion) keep the user in the sweet spot — drift or fast movement flags instantly. Mesh: TSDF fusion over the accumulated depth (Metal-accelerated), Poisson surface reconstruction for a watertight, manifold STL that slices with no repair. Sub-millimetre on foot-scale objects is achievable with multi-pass fusion and confidence-weighted filtering; I'd validate against your calibrated last during milestone 2 and tune until we're inside spec. Measurements: the 12 girths computed by slicing the reconstructed mesh perpendicular to the foot axis at anatomical landmarks (circumference of the cross-section polygon), dorsal outline traced by projecting the upper surface — exported as CSV + JSON alongside ~24 auto-captured stills for colour/surface reference. Everything packaged and uploaded to your secure endpoint (HTTPS multipart with retry). Milestones: (1) capture + guidance UI, (2) mesh pipeline validated vs your last, (3) measurements + photos + upload, (4) TestFlight build + full Xcode project. Swift + ARKit/Metal developer since 2017 on Freelancer (4.9 stars, 87 reviews) with 3D scanning/depth workflow experience. Which iPhone/iPad models should I optimize for first?

@danielboggs

Hi, I came across your project and I'm confident it's a great match for my skills and experience. I have hands-on experience with exactly what you're looking for, and I'm ready to hit the ground running. Please take a moment to review my profile and portfolio — I believe my background speaks for itself. I'd love to discuss the project in more detail and share how I can deliver the results you're looking for. Feel free to reach out anytime. Looking forward to working with you.

@technologiesit

Drawing on my 8+ years of industry experience and expertise in mobile app development, I am confident that I not only understand the complexity of your project but can also deliver to the highest standards. I have an extensive skill set with ARKit, LiDAR, SceneDepth, Metal, RealityKit, CoreML, Swift, Objective-C, and STL export libraries which are all relevant to creating the app you need. Though I have no previous experience specifically with orthotics or 3-D scanning, I see this as an opportunity to leverage my expertise to meet your accuracy targets through meticulous attention to detail and precise coding techniques. Beyond just delivering the project scope, as a professional invested in long-term relationships with clients, I provide ongoing support and maintenance for any issues that may arise. An added assurance factor is my track record—over 200 successfully delivered projects including mobile applications—that speaks volumes about my ability to consistently deliver projects on time while exceeding client expectations. So let's embrace this challenge together and create something amazing that revolutionizes custom bootmaking!

@ianb65

Hi there, Your LiDAR foot scanner needs sub-millimetre capture and watertight STL output, and I can build exactly that. I have strong expertise in iOS Development and iPad workflows, with Objective C where needed, and I’ll use ARKit, LiDAR, SceneDepth, Metal, and RealityKit to guide the scan, flag distance or speed drift instantly, capture the twelve girth measurements plus dorsal outline, and package STL, CSV/JSON, and the image set for secure upload. I’ll also structure the Xcode project and TestFlight build so internal testing is straightforward. Best regards, Ian

@AndrewJSMAN

I’ve built LiDAR-based 3D scanning pipelines for orthotic workflows before, so this is right in my wheelhouse. I’ll anchor the scan pipeline in ARKit’s LiDAR/SceneDepth capture, feed raw depth maps into RealityKit for mesh reconstruction, and validate raw mesh accuracy against a calibrated reference before cleanup. Surface detail and color mapping will come from synchronized RGB frames captured via AVCapturePhotoOutput; twelve girths and dorsal outline will be extracted via CoreML model trained on annotated foot datasets, then exported as CSV/JSON. STL export will use SceneKit’s built-in triangulation adjusted for watertightness via a lightweight Metal-based post-process that removes non-manifold edges without user intervention. Final bundle will include ARKit session logs for reproducibility and a signed TestFlight build delivered via Apple’s distribution profile. I can start immediately. Thanks, Andrii.