# A Similarity Measure for Material Appearance

arXiv: https://arxiv.org/abs/1905.01562 · HTML: https://mlsanity.com/paper/1905.01562/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2019.

## Built on by (most cited first, 19 of 19)

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- [One-shot recognition of any material anywhere using contrastive learning with physics-based rendering](https://mlsanity.com/paper/2212.00648/) (2022)
- [Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers](https://mlsanity.com/paper/2305.10018/) (2023)
- [Shape and Texture Recognition in Large Vision-Language Models](https://mlsanity.com/paper/2503.23062/) (2025)
- [Fine-Grained Spatially Varying Material Selection in Images](https://mlsanity.com/paper/2506.09023/) (2025)
- [Material Measurement Units for a Circular Economy: Foundations through a Review](https://mlsanity.com/paper/2103.01997/) (2021)
- [Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data](https://mlsanity.com/paper/2403.03309/) (2024)
- [MaRI: Material Retrieval Integration across Domains](https://mlsanity.com/paper/2503.08111/) (2025)
- [A Controllable Appearance Representation for Flexible Transfer and Editing](https://mlsanity.com/paper/2504.15028/) (2025)
- [MatPredict: a dataset and benchmark for learning material properties of diverse indoor objects](https://mlsanity.com/paper/2505.13201/) (2025)
- [Predicting Perceived Gloss: Do Weak Labels Suffice?](https://mlsanity.com/paper/2403.17672/) (2024)
- [Material Fingerprinting: Identifying and Predicting Perceptual Attributes of Material Appearance](https://mlsanity.com/paper/2410.13615/) (2024)
- [Visual Material Characteristics Learning for Circular Healthcare](https://mlsanity.com/paper/2309.04763/) (2023)
- [A Neural Quality Metric for BRDF Models](https://mlsanity.com/paper/2508.02131/) (2025)
- [LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data](https://mlsanity.com/paper/2602.04192/) (2026)

## Abstract (arXiv)

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.