Computer Vision • iOS
Fitbox
iOS object measurement for moving and packing
A Swift-based iOS project for estimating object dimensions to support moving, packing, and space planning workflows.
Project focus
LiDAR, Core ML, and camera-based measurement
Case study
Fitbox explores object measurement as a practical iOS workflow for moving, packing, and space planning. The project centers on turning camera and spatial sensing input into estimates that remain understandable to the person making the packing decision.
Problem
Moving and packing decisions often happen before a person has a tape measure, a floor plan, or a finished inventory. Fitbox targets that gap by making quick object dimension estimates available from a familiar mobile capture flow.
Approach
The build combines Swift, Core ML, LiDAR, and camera-based measurement patterns. The interface is organized around capture, dimension estimation, and review so the measurement feels inspectable rather than opaque.
What it shows
The project demonstrates iOS computer vision, spatial sensing, and mobile product design for a real physical workflow.
