Material Classification
12 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Material Classification
MINC
LabPics
SpectroVision
OpenSurfaces
Most implemented papers
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 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.
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying.
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.
Roof material classification from aerial imagery
ZFTurbo/DrivenData-Open-AI-Caribbean-Challenge-2nd-place-solution
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Proposed methods includes: method of converting ImageNet weights of neural networks for using multi-channel images; special set of features of second level models that are used in addition to specific predictions of neural networks; special set of image augmentations that improve training accuracy.
SimTreeLS: Simulating aerial and terrestrial laser scans of trees
We present an open source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters.
How well does CLIP understand texture?
We investigate how well CLIP understands texture in natural images described by natural language.
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
prakashchhipa/depth-contrast-self-supervised-method
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This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt.
