MaRI: Material Retrieval Integration across Domains
Authors: name: Jianhui Wang, name: Zhifei Yang, name: Yangfan He, name: Huixiong Zhang, name: Yuxuan Chen, name: Jingwei Huang

Accurate material retrieval is critical for creating realistic 3D assets.
Existing methods rely on datasets that capture shape-invariant and
lighting-varied representations of materials, which are scarce and face
challenges due to limited diversity and inadequate real-world generalization.
Most current approaches adopt traditional image search techniques. They fall
short in capturing the unique properties of material spaces, leading to
suboptimal performance in retrieval tasks. Addressing these challenges, we
introduce MaRI, a framework designed to bridge the feature space gap between
synthetic and real-world materials. MaRI constructs a shared embedding space
that harmonizes visual and material attributes through a contrastive learning
strategy by jointly training an image and a material encoder, bringing similar
materials and images closer while separating dissimilar pairs within the
feature space. To support this, we construct a comprehensive dataset comprising
high-quality synthetic materials rendered with controlled shape variations and
diverse lighting conditions, along with real-world materials processed and
standardized using material transfer techniques. Extensive experiments
demonstrate the superior performance, accuracy, and generalization capabilities
of MaRI across diverse and complex material retrieval tasks, outperforming
existing methods.