M3D: A Benchmark Dataset and Model for Microscopic 3D Shape Reconstruction

Abstract

Microscopic 3D shape reconstruction using depth from focus (DFF) is crucial in precision manufacturing for 3D modeling and quality control. However, the absence of high-precision microscopic DFF datasets and the significant differences between existing DFF datasets and microscopic DFF data in optical design, imaging principles and scene characteristics hinder the performance of current DFF models in microscopic tasks. To address this, we introduce M3D, a novel microscopic DFF dataset, constructed using a self-developed microscopic device. It includes multi-focus image sequences of 1,952 scenes across five categories, with depth labels obtained through the 3D TFT algorithm applied to dense image sequences for initial depth estimation and calibration. All labels are then compared and analyzed against the design values, and those with large errors are eliminated. We also propose M3DNet, a frequency-aware end-to-end network, to tackle challenges like shallow depth-of-field (DoF) and weak textures. Results show that M3D compensates for the limitations of macroscopic DFF datasets and extends DFF applications to microscopic scenarios. M3DNet effectively captures rapid focus decay and improves performance on public DFF datasets by leveraging superior global feature extraction. Additionally, it exhibits strong robustness even in extreme conditions. Dataset and code are available at https://github.com/jiangfeng-Z/M3D.