Material Recognition
15 papers with code • 0 benchmarks • 9 datasets
Material recognition focuses on identifying classes, types, states, and properties of materials.
Benchmarks
These leaderboards are used to track progress in Material Recognition
MINC
MCubeS
LabPics
TransProteus
SpectroVision
MatSim
MatSeg
OpenSurfaces
Latest papers
MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
People with Visual Impairments (PVI) typically recognize objects through haptic perception.
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
The synthetic images were rendered using giant collections of textures, objects, and environments generated by computer graphics artists.
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.)
Encoding Spatial Distribution of Convolutional Features for Texture Representation
csfengli/fenet
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NeurIPS 2021
Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation.
Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-Grained Recognition
shaoli-huang/sps
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ICCV 2021
Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization capability.
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.
Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set
sagieppel/Detecting-and-segmenting-and-classifying-materials-inside-vessels-in-images-using-convolutional-net
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ACS Central Science 2020
Visual recognition of vessels and their contents is essential for performing this task.
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.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
PV-Lab/autoXRD
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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.
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.
