Texture Classification

ElBa is composed of procedurally-generated realistic renderings, where we vary in a continuous way element shapes and colors and their distribution, to generate 30K texture images with different local symmetry, stationarity, and density of (3M) localized texels, whose attributes are thus known by construction. Download

Texture-based studies and designs have been in focus recently. Whisker-based multidimensional surface texture data is missing in the literature. This data is critical for robotics and machine perception algorithms in the classification and regression of textural surfaces. We present a novel sensor design to acquire multidimensional texture information. The surface texture's roughness and hardness were measured experimentally using sweeping and dabbing. The data is made available to the research community for further advancing texture perception studies.

Dataset of >200 synthetic cardboard texture images that were rendered with DoubeGum's cardboard shader in Blender. Used to generate Parcel3D, the dataset for our paper on single image 3D reconstructions of potentially damaged parcels.

ISOD contains 2,000 manually labelled RGB-D images from 20 diverse sites, each featuring over 30 types of small objects randomly placed amidst the items already present in the scenes. These objects, typically ≤3cm in height, include LEGO blocks, rags, slippers, gloves, shoes, cables, crayons, chalk, glasses, smartphones (and their cases), fake banana peels, fake pet waste, and piles of toilet paper, among others. These items were chosen because they either threaten the safe operation of indoor mobile robots or create messes if run over. In addition to RGB images, ISOD also includes corresponding depth images and IMU readings. A reference image of each floor type was also recorded using a smartphone. This dataset was used as a real-world validation dataset in the original work to explore the performance of the model beyond synthetic data, specifically focusing on the potential application of real-time robot navigation.

This dataset aims to provide a color dataset with real industrial fabric defect gathered in a visiting machine with several industrial cameras. It has been designed with the same nomenclature as MVTEC AD dataset