MINC is a large-scale, open dataset of materials in the wild.
53 PAPERS • NO BENCHMARKS YET
Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. Each scene has images for four modalities: RGB, angle of linear polarization (AoLP), degree of linear polarization (DoLP), and near-infrared (NIR). The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel. The dataset is divided training set with 302 image sets, validation set with 96 image sets, and test set with 102 image sets. Each image has 1224 x 1024 pixels and a total of 20 class labels per pixel.
10 PAPERS • 1 BENCHMARK
LabPics Chemistry Dataset
5 PAPERS • NO BENCHMARKS YET
MatSeg Dataset for Zero-Shot Material States Segmentation: The dataset contains large-scale synthetic images for training data and highly diverse real-world image benchmarks for testing. Focusing on zero-shot class-agnostic segmentation of materials and their states. This means finding the region of materials states without pre-training on the specific material classes or states. The benchmark contains a wide range of real-world materials and states. For example: wet regions of the surface, scattered dust, minerals of rocks, the sediment of soils, rotten parts of fruits, degraded and corrosive surface regions, food and liquid states, and many others. The focus is on scattered and fragmented materials, as well as soft boundaries partial transition, and partial similarity between regions. It contains both hard segmentation maps and soft and partial similarity annotations for similar but not identical materials.
1 PAPER • NO BENCHMARKS YET
MatSim is a synthetic dataset, and natural image benchmark for computer vision-based recognition of similarities and transitions between materials and textures, focusing on identifying any material under any conditions using one or a few examples (one-shot learning), including materials states and subclasses.
SpectroVision is a dataset of 14,400 high resolution texture images and spectral measurements collected from a PR2 mobile manipulator that interacted with 144 household objects from eight material categories.
The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.
1 PAPER • 1 BENCHMARK
The Dense Material Segmentation Dataset (DMS) consists of 3 million polygon labels of material categories (metal, wood, glass, etc) for 44 thousand RGB images. The dataset is described in the research paper, A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing.
0 PAPER • NO BENCHMARKS YET
OpenSurfaces is a large database of annotated surfaces created from real-world consumer photographs. The framework used for the annotation process draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information.