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

TL;DR

This work presents a novel approach based on artificial vision that utilizes multi-view information that integrates segmented RGB images with depth data for precise frame contour measurement, providing competitive measurements from still color images with respect to other solutions.

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.

Figures (6)

• Figure 1: Invision Head. The acquisition system consists of four calibrated cameras and two sources of light: visible and IR. Just the 1296$\times$1296 RGB images are considered in this paper.
• Figure 2: Color and depth information to obtain measurements. The figure shows a particular image acquisition, its corresponding depth information; as well as the same information after applying our segmentation model to infer the region of interest (this represents our input data).
• Figure 3: Main architecture for eye-frame trace. Multi-view RGB and depth channels are processed at the same networks.
• Figure 4: Qualitative evaluation of our segmentation strategy with the base+ model for different users. The glass segmentation applying DeepLabv3+ c20 and ours is displayed in red and blue, respectively. In last column, a zooming plot is shown. Best viewed in color.
• Figure 5: Qualitative results on depth estimation. From left to right: Test input image, depth prediction using ViT-Small, ViT-Base, and ViT-Large, respectively.