[Paper Review] Deep Learning for Material recognition: most recent advances and open challenges

Alain Trémeau, Sixiang Xu+1 more|arXiv (Cornell University)|Dec 14, 2020

Advanced Neural Network Applications23 references4 citations

TL;DR

This paper reviews recent advances in deep learning for material recognition, focusing on datasets, contextual influence, and specialized descriptors for material appearance. It synthesizes state-of-the-art methods, presents original research, and identifies key open challenges in achieving robust, high-accuracy material recognition despite progress in deep learning.

ABSTRACT

Recognizing material from color images is still a challenging problem today. While deep neural networks provide very good results on object recognition and has been the topic of a huge amount of papers in the last decade, their adaptation to material images still requires some works to reach equivalent accuracies. Nevertheless, recent studies achieve very good results in material recognition with deep learning and we propose, in this paper, to review most of them by focusing on three aspects: material image datasets, influence of the context and ad hoc descriptors for material appearance. Every aspect is introduced by a systematic manner and results from representative works are cited. We also present our own studies in this area and point out some open challenges for future works.

Motivation & Objective

• To systematically review recent progress in deep learning for material recognition across key dimensions: datasets, context, and appearance descriptors.
• To identify persistent challenges in achieving performance comparable to object recognition in material recognition tasks.
• To present original research contributions in material recognition using deep learning.
• To highlight open challenges and future research directions in the field.

Proposed method

• Systematic review of recent literature on deep learning for material recognition, emphasizing datasets, contextual influence, and specialized descriptors.
• Analysis of representative deep learning architectures applied to material recognition tasks.
• Evaluation of the impact of contextual information (e.g., surrounding objects, scene context) on material classification accuracy.
• Design and implementation of ad hoc deep features tailored to material appearance, such as texture, gloss, and albedo.
• Comparison of performance across different network architectures and data augmentation strategies.
• Incorporation of domain-specific priors and geometric cues to improve robustness in material recognition.

Experimental results

Research questions

• RQ1 How do existing material image datasets compare in terms of scale, diversity, and annotation quality?
• RQ2 To what extent does contextual information improve material recognition accuracy in deep learning models?
• RQ3 What types of deep features or descriptors are most effective for capturing material-specific appearance characteristics?
• RQ4 Why do deep learning models still underperform on material recognition compared to object recognition?
• RQ5 What are the most critical open challenges hindering the advancement of material recognition with deep learning?

Key findings

• Recent deep learning models achieve strong performance on benchmark material recognition datasets, but still fall short of object recognition accuracy levels.
• Contextual information significantly improves material recognition, especially in complex or ambiguous scenes.
• Custom-designed deep features for material appearance—such as those capturing micro-texture and reflectance—outperform generic CNN features.
• Large-scale, diverse, and accurately annotated datasets remain scarce, limiting model generalization and training stability.
• Despite progress, challenges such as domain shift, lighting variation, and material similarity persist as major obstacles.
• The integration of geometric and physical priors into deep learning frameworks shows promise but requires further exploration.