Material Recognition
13 papers with code • 0 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Material Recognition
MINC
MCubeS
LabPics
SpectroVision
TransProteus
MatSim
OpenSurfaces
Most implemented papers
Deep TEN: Texture Encoding Network
zhanghang1989/Deep-Encoding
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CVPR 2017
The representation is orderless and therefore is particularly useful for material and texture recognition.
Describing Textures in the Wild
deeplearning-wisc/knn-ood
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CVPR 2014
Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly.
Classification of Household Materials via Spectroscopy
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
PV-Lab/AUTO-XRD
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npj Computational Materials 2019
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.)
Deep Filter Banks for Texture Recognition and Segmentation
Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Healthcare-Robotics/spectrovision
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Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.
Differential Viewpoints for Ground Terrain Material Recognition
jiaxue1993/pytorch-material-classification
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A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition.
