Harnessing the Power of Foundation Models for Accurate Material Classification

ArXi:2603.17390v1 Announce Type: new Material classification has emerged as a critical task in computer vision and graphics, ing the assignment of accurate material properties to a wide range of digital and real-world applications. While traditionally framed as an image classification task, this domain faces significant challenges due to the scarcity of annotated data, limiting the accuracy and generalizability of trained models.