Material Fingerprinting: Identifying and Predicting Perceptual Attributes of Material Appearance

TL;DR

This paper presents a novel method for material identification by creating perceptual fingerprints from videos, enabling efficient retrieval and filtering of materials based on perceptual attributes using machine learning.

Contribution

It introduces a new approach to encode perceptual features into material fingerprints and trains a neural network to predict these attributes from image features, improving material identification.

Findings

Successful selection and validation of 16 perceptual attributes.

High accuracy in material retrieval based on perceptual fingerprints.

Effective prediction of perceptual attributes from image features.

Abstract

The world is abundant with diverse materials, each possessing unique surface appearances that play a crucial role in our daily perception and understanding of their properties. Despite advancements in technology enabling the capture and realistic reproduction of material appearances for visualization and quality control, the interoperability of material property information across various measurement representations and software platforms remains a complex challenge. A key to overcoming this challenge lies in the automatic identification of materials' perceptual features, enabling intuitive differentiation of properties stored in disparate material data representations. We reasoned that for many practical purposes, a compact representation of the perceptual appearance is more useful than an exhaustive physical description.This paper introduces a novel approach to material identification by encoding perceptual features obtained from dynamic visual stimuli. We conducted a psychophysical experiment to select and validate 16 particularly significant perceptual attributes obtained from videos of 347 materials. We then gathered attribute ratings from over twenty participants for each material, creating a 'material fingerprint' that encodes the unique perceptual properties of each material. Finally, we trained a multi-layer perceptron model to predict the relationship between statistical and deep learning image features and their corresponding perceptual properties. We demonstrate the model's performance in material retrieval and filtering according to individual attributes. This model represents a significant step towards simplifying the sharing and understanding of material properties in diverse digital environments regardless of their digital representation, enhancing both the accuracy and efficiency of material identification.