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 with no code
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
This unsupervised approach allows the generated data to capture the vast complexity of the real world while maintaining the precision and scale of synthetic data.
Intelligent Parsing: An Automated Parsing Framework for Extracting Design Semantics from E-commerce Creatives
This framework comprises material recognition, preprocess, smartname, and label layers.
UMat: Uncertainty-Aware Single Image High Resolution Material Capture
We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method.
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera.
Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels
Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data.
GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition
We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition.
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images.
The joint role of geometry and illumination on material recognition
In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks.
Deep Learning for Material recognition: most recent advances and open challenges
Recognizing material from color images is still a challenging problem today.
Material Recognition via Heat Transfer Given Ambiguous Initial Conditions
We also found that robots can overcome this ambiguity using two temperature sensors with different temperatures prior to contact.
