Deep Learning for Material recognition: most recent advances and open challenges
This review is for researchers and practitioners in computer vision, summarizing the current state and open challenges in applying deep learning to material recognition, an area where performance lags behind object recognition.
This paper reviews recent advances in deep learning for material recognition from color images, focusing on datasets, contextual influence, and ad hoc descriptors. It highlights that while deep learning shows promise, further work is needed to achieve accuracies comparable to object recognition.
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.