MaRI: Material Retrieval Integration across Domains
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.
67d69afb060a3df28c886b2b
Jianhui Wang
admin_assigned
2025-03-17T14:58:11.013Z
6716120136c0c14b5b18a16b
/avatars/9d759fb7c0849aa9f5283a514b4ff2e7.svg
Jianhui Wang
user
xchrysalis
67d69afb060a3df28c886b2c
Zhifei Yang
claimed_verified
2025-03-16T21:12:12.522Z
6464c4ef92773d5eeb588525
/avatars/65fc839453d892016640249cc1acf277.svg
Zhifei Yang
user
yangzhifei
67d69afb060a3df28c886b2d
Yangfan He
67d69afb060a3df28c886b2e
Huixiong Zhang
67d69afb060a3df28c886b2f
Yuxuan Chen
67d69afb060a3df28c886b30
Jingwei Huang
MaRI: Material Retrieval Integration across Domains
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.
MaRI is a framework that improves material retrieval by creating a shared embedding space through contrastive learning of visual and material attributes.
contrastive learning feature space image and material encoder material retrieval material transfer techniques synthetic materials real-world materials