Room Dimension Measuring App

@iamhamzazaki

As an expert in electronics programming and embedded systems, I've been fortunate enough to work on projects that encompassed similar features to the one you're describing. With a deep knowledge of technologies such as ARCore/ARKit, open-source photogrammetry libraries like OpenCV, OpenMVG, COLMAP, that you mentioned in your project description, I believe I'm highly suited to tackle the challenges your room dimension measuring app presents. I've got extensive experience developing cross-platform applications using languages like Kotlin, Swift, Java, C/C++, JavaScript; giving me the diversity necessary for this cross-functionality task. Furthermore, I am proficient in 3D modelling and I am confident in my ability to deliver a smooth workflow resulting in highly accurate room measurements. Thanks to cutting-edge computer-vision techniques integrated into my app's environment; such as depth estimation and state-of-the-art photogrammetry algorithms; error reduction that matches your requirements is a guarantee.

@corpmember29

Hello, AR Room Measurement App {{{ I HAVE CREATED SIMILAR AR, COMPUTER-VISION AND MOBILE MEASUREMENT SOLUTIONS BEFORE AND I CAN SHOW YOU RELEVANT WORK. }}} I have 11+ years of experience and can build native Android and iOS apps that process both photo sequences and video walkthroughs for room reconstruction and measurement. I’ll use ARCore/ARKit with depth estimation/photogrammetry as appropriate, generate accurate wall, floor and ceiling measurements, and create a clean 2D floor plan with PDF/PNG export. I’ll also handle calibration, measurement validation, device compatibility, performance optimization and TestFlight/internal testing, with the CV pipeline and accuracy benchmarks documented. Thanks, Christina

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Mobile App for Room Measurement and 2D Floor Plans ❇️ Hi My Friend, I hope you're doing well. I’ve reviewed your project requirements and see you're looking for a mobile app that captures room dimensions and creates floor plans. You don’t need to look any further; Zohaib is here to help you! My team has already completed over 50 similar projects for mobile applications. I will use advanced computer vision techniques to ensure accurate measurements and a smooth user experience. ➡️ Why Me? I can easily build your mobile app as I have 5 years of experience in mobile development, specializing in Android and iOS. My skills include working with ARCore, ARKit, and photogrammetry libraries. I also have a strong grip on Kotlin, Swift, and Java, ensuring your app functions perfectly on both platforms. ➡️ Let's have a quick chat to discuss your project details and allow me to show you samples of my previous work. I look forward to discussing this with you! ➡️ Skills & Experience: ✅ Mobile App Development ✅ Android (Kotlin/Java) ✅ iOS (Swift/Objective-C) ✅ Computer Vision ✅ ARCore/ARKit ✅ Photogrammetry ✅ 3D Reconstruction ✅ Measurement Extraction ✅ User Interface Design ✅ PDF/PNG Export ✅ Source Code Documentation ✅ Technical Documentation Waiting for your response! Best Regards, Zohaib

@C0RETECHIES

Hi, You need a native iOS/Android utility that digests live video feeds or photo batches to reconstruct a 3D spatial mesh of a room, extracting wall-to-wall and ceiling measurements with sub-5cm precision. The operational loop involves translating this depth data into an exportable, scaled 2D floor plan directly on-device. Technical approach: - Core CV pipeline utilizing ARKit (iOS with LiDAR support) and ARCore (Android) for real-time point cloud and depth generation. - Fallback photogrammetry (OpenCV/OpenMVG) for batch photo-series input processing. - Plane detection algorithms (floors/ceilings/walls) combined with ray-casting for orthogonal distance calculation. - Native Swift and Kotlin structures to guarantee low-latency sensor and camera hardware access. Core modules: - Media ingestion engine with format auto-detection (video continuous vs. photo sequence). - Spatial calibration handling variable indoor lighting conditions. - 2D floor plan rendering matrix mapping 3D planes to flat geometries for PDF/PNG export. Relevant systems: - Alpha Robot (Hardware-level AI sensor integration and spatial mapping) - CoreWipe (Native Kotlin architecture with deep device hardware interaction) Implementation strategy: We will start with an ARKit/ARCore POC focused strictly on validating the ≤2% accuracy benchmark for one wall. Once calibrated, we will expand to full 3D room generation and layer the 2D rendering workflows. Regards, Rohit

@Hammadhassan21

Hey, Turning a phone camera into reliable room measurements sounds simple until accuracy, different capture conditions, and both iOS and Android are involved. That’s exactly where I can help. I can work around AR based depth and reconstruction to build a workflow where both photo sequences and video feed into the same measurement and floor plan process, while keeping the capture experience as simple as possible for the user. I would keep accuracy at the center from the beginning, including calibration, validation and testing against known room dimensions instead of assuming the 3 D reconstruction itself is accurate enough. I can also structure the measurement logic so it can be shared where practical between Android and iOS while still keeping the native implementation reliable on each platform. I have experience working on similar computer vision, AR, measurement, and mobile workflows and I can show you relevant examples in chat if there’s a specific part of the project you’d like to compare. Portfolio: https://www.freelancer.com/u/Hammadhassan21 Best regards, Hammad Hassan

@intelliresponse

Hi, I can build this mobile app for both Android and iOS, including photo/video room capture, 3D reconstruction, accurate room measurements, 2D floor-plan generation, and PDF/PNG export. I have experience with computer vision, OpenCV, AR-based workflows, camera calibration, and 3D reconstruction. I’d use ARKit/ARCore and device depth/LiDAR where available, with a fallback vision pipeline for other devices. I can also provide the complete source code, build instructions, technical documentation, and accuracy testing against your 4m × 3m × 2.5m acceptance case. I’m happy to discuss the best CV approach and milestones.

@sunainawbst

Hey there, I have carefully reviewed your project requirements. With over 10+ years of experience in game development, I have built and delivered high-quality games across multiple platforms, focusing on performance, scalability, and engaging player experiences. My expertise includes Unity, Unreal Engine, C#, C++, multiplayer systems, game mechanics, UI/UX for games, in-app purchases, and third-party SDK integrations—all highly relevant to building polished and production-ready games. I would love the opportunity to discuss your game idea in detail and collaborate on bringing it to life with a strong technical foundation and engaging gameplay. Due to NDAs, links aren’t public—but once you open the chat, I’ll share live demos and walkthroughs. NOTE: Please consider the current budget as flexible — we can finalize it after discussing the complete scope and feature set. Thanks & Regards, Ashraf

@kkirandeep582

My name is Kirandeep Kaur and I am thrilled to introduce you to my unique set of skills that are a perfect match for your project. As a 3D artist and experienced animator, I not only have expertise in modeling but also in Architectural Visualization and could execute the 3-D reconstruction stage of your workflow flawlessly. I have hands-on experience with LiDAR, ARCore/ARKit as well as open-source photogrammetry libraries such as OpenCV which will be crucial for developing accurate and efficient depth estimation processes. Additionally, my proficiency in Graphic Design and Web Development complements the intuitiveness of your requirements. The ability to generate a floor plan from the room's measurements aligns perfectly with my expertise in rendering and 2-D modeling. My familiarity with the native languages of both Android (Kotlin/Java) and iOS (Swift/Objective-C) platforms means I can deliver fully-packaged apps ready for TestFlight/internal testing on both platforms. Finally, my mission has always been to turn ideas into professional digital solutions that exceed client expectations. I am committed to delivering precise, high-quality solutions on time—exactly what you need for your Room Dimension Measuring app. Thank you for considering me for this project, and I'm eagerly looking forward to discussing in further detail how we can bring your vision to life!

@mninfotech2

Hi, Your app needs to solve more than room scanning—it needs to turn a simple phone walkthrough into measurements users can actually trust. I can build the Android and iOS apps around a calibrated computer-vision/AR pipeline focused on measurement accuracy and a smooth capture experience. I’d approach the solution with: • ARCore/ARKit for camera tracking, planes and device-supported depth • OpenCV/computer vision for feature detection and geometric refinement • Photogrammetry/depth processing for devices where it provides better reconstruction • Guided photo/video capture with real-time overlays • Automatic detection of walls, floor and ceiling • Wall-to-wall and height measurements in metric units • 2-D floor-plan generation with dimension annotations • PDF/PNG export • Local processing where practical for speed and privacy • Native Kotlin/Android and Swift/iOS implementations I’ll calibrate and benchmark the measurement pipeline against known room dimensions, with the 4m × 3m × 2.5m acceptance test as a key validation milestone. I’ll also test different lighting and device conditions to identify accuracy limitations rather than simply assuming every phone can achieve the same precision. You’ll receive complete source code, TestFlight/internal-test builds, build instructions, and a technical document covering the CV pipeline, calibration and accuracy benchmarks. Best regards

@devendrathakur12

i’ve done very similar recently with ARCore/ARKit, OpenCV, depth estimation, camera calibration, and mobile CV pipelines. For this, I’d use ARKit/ARCore depth and plane detection where available, with a fallback CV pipeline for devices without depth sensors. Are you targeting LiDAR-capable iPhones specifically, or must the ≤5 cm target work across standard phones too? Should video and photo-series inputs produce the same internal room model before measurement? I’d keep reconstruction separate from measurement and floor-plan generation, which makes accuracy testing much easier. I’d also use calibration and known plane constraints to reduce drift and improve measurements across longer rooms. I’ll build capture first, then reconstruction, wall/ceiling extraction, measurement validation, and 2-D rendering. Finally, I’ll benchmark against measured rooms, add PDF/PNG export, and prepare TestFlight/Android builds with setup documentation. Best, Dev S.

@alimumtaz112

I can build the native iOS and Android application around a shared measurement workflow while using the platform-specific AR/depth capabilities required for accuracy. For iOS, I would evaluate ARKit/RoomPlan and LiDAR where available, with a camera-based fallback for devices without LiDAR. On Android, ARCore Depth/Plane detection can provide the foundation, with OpenCV or an appropriate photogrammetry pipeline where additional reconstruction is required. The app would support: • Guided photo-series and continuous video capture • Automatic media-mode detection and processing • Room/plane detection and 3-D reconstruction • Wall-to-wall and floor-to-ceiling measurements • 2-D floor-plan generation with annotated dimensions • PDF/PNG export • Calibration and confidence/error reporting I would design the measurement engine around your stated ≤2% typical-error target and validate it against controlled rooms, including your 4m × 3m × 2.5m acceptance case. Accuracy will be benchmarked separately across LiDAR-capable and camera-only devices rather than assuming identical results. Deliverables include complete Swift and Kotlin source, TestFlight/internal-test builds, build documentation and a technical document covering the CV/AR pipeline, calibration method and measured accuracy. I have extensive native iOS/Android experience and can structure the project so the platform-specific AR capabilities remain maintainable and testable.

@ciss26

Hello there, Hope you are doing well We can build your Room Dimension Measuring App for both iOS and Android with guided camera capture, 3D room reconstruction, automatic measurements, and 2D floor-plan generation. Recommended Technical Approach - iOS: Swift + ARKit / RoomPlan where supported - Android: Kotlin + ARCore / Depth API - Computer Vision: OpenCV - 3D Reconstruction: AR/depth-based pipeline with photogrammetry fallback where required - Floor Plan Rendering: Native 2D drawing layer - Export: PDF / PNG - Local Processing: On-device where practical for speed and privacy Key Features - Guided photo capture - Guided video walkthrough - Automatic media-type detection - Room boundary detection - Wall length measurement - Room width measurement - Ceiling-height estimation - 3D room mesh generation - Real-time / near-real-time measurements - Measurement overlays in cm / m - 2D floor-plan generation - Annotated dimensions - PDF export - PNG export - Camera guidance overlays - Calibration and quality checks - Error/warning prompts for poor lighting or insufficient coverage

@vishal1145

Your acceptance criteria of ≤5 cm error across a 4×3 m room will fail without LiDAR-equipped devices if you rely solely on photogrammetry—standard RGB cameras introduce 8-12 cm drift in depth estimation under typical indoor lighting. ARKit's plane detection on iPhone 12+ and ARCore's Depth API on Pixel/Galaxy flagships can hit that tolerance, but older devices will need fallback logic that trades accuracy for compatibility. Quick questions - are you targeting only LiDAR-capable devices (iPhone 12 Pro onward, iPad Pro 2020+) or do you need a degraded-accuracy mode for older hardware? And what's your floor-plan export priority: vector-based SVG with editable dimensions or raster PNG with baked annotations? Here is the architectural approach: - ARKIT/ARCORE DEPTH FUSION: Implement native ARKit RoomPlan API (iOS 16+) and ARCore Depth API with plane-merging algorithms to reconstruct wall boundaries within 2 cm tolerance on supported devices, falling back to ORB-SLAM3 photogrammetry for older models. - KOTLIN/SWIFT NATIVE BUILDS: Write separate codebases sharing a common C++ OpenCV processing layer via JNI/Swift bridging to handle video frame extraction, feature matching, and mesh triangulation without cross-platform framework overhead that kills real-time performance. - 2D FLOOR-PLAN RENDERING: Project the 3D mesh onto the XZ plane, apply Douglas-Peucker simplification to wall polylines, then render dimension annotations using Core Graphics (iOS) and Canvas API (Android) with PDF export via platform-native libraries. I've built AR measurement tools for two construction-tech clients that achieved 1.5 cm accuracy on LiDAR devices and deployed them to 12K field workers. Let's schedule a 20-minute technical call to align on device support tiers before you commit budget.

@pinkeegupta

Hi I will be able to help you. Please message me so that we will have detail technical discussion. I have 9+ years of combined experience in Mobile Application development, Website development, Desktop application development, 3rd party Artificial Intelligence api, AR/ VR, Chatbot, Blockchain- Cryptocurrency, CRM & ERP, Game Development and any other Software development. I am having expertise in Native on Android Java, Kotlin and IOS Swift, and For Hybrid Cross platform on Flutter Dart & React- Native, and for web and backend on react js and node js, Python Django. Please consider me and initiate a chat for further detailed discussion. Regards, Anju

@shaktistanwar

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@topcreator100

Hi , Good evening! I am professional mobile computer programmer with skills including Android, Computer Vision, Unity 3D, Mobile App Development, iOS, Augmented Reality, 3D Modelling, Kotlin and Python. Please contact me to discuss more about this project. Best Regards

@denisSolution

Hi, I'm Denis, a full-stack developer with hands-on experience building mobile apps that process visual data in real time. I've worked on AR systems and computer vision pipelines, including depth estimation and 3D reconstruction on both Android and iOS. I understand your project: a native app that captures room geometry through photos or video, reconstructs 3D space, extracts wall and ceiling measurements, and generates a floor plan with sub-5cm accuracy without external hardware. For this, I'd use ARKit on iOS and ARCore on Android as the foundation. Both provide real-time depth estimation and plane detection—more practical than pure photogrammetry for a single walkthrough. I'd layer in OpenCV for additional feature tracking and measurement refinement. The 3D mesh builds incrementally as the camera moves, then I extract wall positions by analyzing plane intersections and depth data. The floor plan renders as a 2D projection with dimension annotations. The workflow: capture UI with guidance overlays, stream depth and pose data during recording, reconstruct room geometry post-capture, extract measurements by fitting planes to the point cloud, and render both 3D and 2D views. Export to PDF/PNG uses standard libraries. For accuracy, I'd build in a calibration step using a known reference object to validate measurements and maintain 5cm tolerance. ARCore and ARKit estimate scale from device motion and plane detection, but lighting and wall texture affect depth quality, so ba

@Arunmohan001

Hi, I can build this room-measurement app for both Android and iOS using ARCore/ARKit with a suitable computer-vision pipeline. I’ll implement guided photo/video capture, room reconstruction, wall and ceiling measurements, and automatic 2-D floor-plan generation. The app will support PDF/PNG export and be optimized for modern smartphones without requiring external hardware. I’ll provide the complete Android/iOS source code, test builds, documentation, and accuracy testing against real rooms. I can first validate the measurement approach on your target devices and then define milestones around reconstruction accuracy, floor plans, and final app packaging.