Breaking the Sub-Millimeter Barrier: Eyeframe Acquisition from Color Images

TL;DR

This paper introduces a vision-based method for precise eyeframe measurement from color images, eliminating the need for mechanical tools and improving workflow efficiency in optical manufacturing.

Contribution

It presents a novel multi-view image processing pipeline for sub-millimeter eyeframe acquisition using standard color images, reducing reliance on specialized equipment.

Findings

Achieves competitive measurement accuracy from color images.

Eliminates need for mechanical tracing tools.

Reduces workflow complexity for opticians.

Abstract

Eyeframe lens tracing is an important process in the optical industry that requires sub-millimeter precision to ensure proper lens fitting and optimal vision correction. Traditional frame tracers rely on mechanical tools that need precise positioning and calibration, which are time-consuming and require additional equipment, creating an inefficient workflow for opticians. This work presents a novel approach based on artificial vision that utilizes multi-view information. The proposed algorithm operates on images captured from an InVision system. The full pipeline includes image acquisition, frame segmentation to isolate the eyeframe from background, depth estimation to obtain 3D spatial information, and multi-view processing that integrates segmented RGB images with depth data for precise frame contour measurement. To this end, different configurations and variants are proposed and analyzed on real data, providing competitive measurements from still color images with respect to other solutions, while eliminating the need for specialized tracing equipment and reducing workflow complexity for optical technicians.