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.

翻译：眼镜框镜片追踪是光学行业中的一项重要工艺，需达到亚毫米级精度以确保镜片精准适配与视力矫正效果最优化。传统镜框追踪仪依赖机械工具，需精确定位与校准，过程耗时且需额外设备，导致配镜师工作流程效率低下。本研究提出一种基于人工视觉并利用多视角信息的新方法。所提算法在InVision系统采集的图像上运行。完整流程包括图像采集、镜框分割（从背景中分离眼镜框）、深度估计（获取三维空间信息）以及多视角处理（将分割后的RGB图像与深度数据融合以实现精确的镜框轮廓测量）。为此，我们在真实数据上提出并分析了多种配置方案与变体，仅通过静态彩色图像即可获得相较于其他解决方案具有竞争力的测量结果，同时无需专用追踪设备，显著降低了光学技师的工作流程复杂度。