This course aims to teach students how to visually recognize materials using generative and discriminative material recognition techniques. The course covers topics such as taming reflectance, radiometric decomposition, single and multimaterial estimation, and using multiple images for material recognition. The teaching method includes lectures on material traits, convolutional material trait kernels, and material category classification. This course is intended for individuals interested in computer vision, material recognition, and image processing.
Overview
Syllabus
Intro
Acknowledgements
Visually Recognizing Materials
Generative vs. Discriminative
Generative Material Recognition
Taming Reflectance
A Novel Reflectance Model . An expressive yet parametric (isotropic) BRDF model
Connecting the Slices
The Space of Real-World BRDFs
Radiometric Decomposition
Single Image Single Material Estimation
A Reflectance Prior
Single Material Results
Single Image Multimaterial Estimation
Multimaterial Results
Using Multiple Images
Reflectance as A Bandpass Filter
Natural Illumination Entropy Prior
Reflectance and Natural Illumination from A Single Image
Real Object / Real Illumination
Real Images
Discriminative Material Recognition
Taming Material Appearance
Visual Material Traits
Convolutional Material Trait Kernels
Material Trait Features
Material Trait Recognition
Materials from Material Traits
Material Category Classification
Material Category Failure Cases
Material as Visual Context
Summary - Towards visual material recognition
Taught by
UCF CRCV
