A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures

Published: 6 October 2016| Version 1 | DOI: 10.17632/tj4syyj9mr.1
Elizabeth Holm,


This data article presents a data set comprised of 2048 synthetic scanning electron microscope (SEM) images of powder materials and descriptions of the corresponding 3D structures that they represent. These images were created using Blender, an open source computer graphics suite, and the generating scripts are included with the data set. Eight particle size distributions are represented with 256 independent images from each. The particle size distributions are relatively similar to each other, so that the dataset offers a useful benchmark to assess the fidelity of image analysis techniques.


Steps to reproduce

The dataset of 3D structures and their corresponding 2D images was created using Blender, an open source computer graphics suite used for 3D modeling, rendering, animation, and scientific visualization. In this dataset, powders are assumed to be comprised of spherical particles with sizes drawn at random from the appropriate PSD. We consider eight PSDs and construct 256 independent structure/image pairs for each PSD, resulting in 2048 synthetic powder micrographs. To synthesize each image, we use an 11×11×2 (arbitrary Blender units) render volume and insert 800 particles placed at random. Particle radii are selected at random from one of the eight generating PSDs, and they are permitted to intersect and/or occlude each other. Particles are rendered using a spherical mesh, with a surface texture achieved by wrapping the particle with an image of zinc grains, included in the dataset. The particles are imaged on the z = 0 plane, which intersects the centroid of the render volume. The camera is located in the center of the volume at height z = 10, and the resulting image resolution is 512 × 512 pixels. Python scripts used to perform these operations are included in the dataset files.


Computer Vision, Computational Materials Science, Image Databases, Metal Powder, Microstructural Analysis