MIN: Moiré Inpainting Network With Position Adaptive Mask for 3-D Height Reconstruction

In the AI-driven computer vision industry, height measurement of Printed Circuit Board images typically relies on laser or Moiré methods. In this paper, we focus on the Moiré method, known for its high accuracy and fast measurement speed. However, when using Moiré method, shadows and light reflections are generated on Printed Circuit Board surface that cause significant errors in height measurement. To address this problem, we propose a Moiré Inpainting Network, which integrates the Moiré method with an image inpainting model architecture. Our approach leverages a Generative Adversarial Network to accurately identify and reconstruct shadow and reflection regions. The network takes 2D Printed Circuit Board Moiré images as input and outputs heights of Printed Circuit Board. We evaluate performance using Height Reconstruction Rate, Shadow Reconstruction Rate, and Reflection Reconstruction Rate, metrics we define in this paper. Comparative experiments show that our method outperforms state-of-the-art inpainting models for Moiré images, proving its effectiveness in computer vision applications. Moreover, we achieve a reasonable inference time, enabling real-time deployment in Printed Circuit Board manufacturing.