Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel.
10 PAPERS • 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
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. It contains both hard segmentation maps and soft and partial similarity annotations for similar but not identical materials.
1 PAPER • NO BENCHMARKS YET
…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
…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.
1 PAPER • 1 BENCHMARK
LabPics Chemistry Dataset Dataset for computer vision for materials segmentation and classification in chemistry labs, medical labs, and any setting where materials are handled inside containers. In addition to instance segmentation maps, the dataset also includes semantic segmentation maps that give each pixel in the image all the classes to which it belongs.
5 PAPERS • NO BENCHMARKS YET