title: MaRI: Material Retrieval Integration across Domains
summary: 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.
paper_authors._id: 67d69afb060a3df28c886b2b
paper_authors.name: Jianhui Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:58:11.013Z
paper_authors._id: 6716120136c0c14b5b18a16b
paper_authors.avatarUrl: /avatars/9d759fb7c0849aa9f5283a514b4ff2e7.svg
paper_authors.fullname: Jianhui Wang
paper_authors.type: user
paper_authors.user: xchrysalis
paper_authors._id: 67d69afb060a3df28c886b2c
paper_authors.name: Zhifei Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:12:12.522Z
paper_authors._id: 6464c4ef92773d5eeb588525
paper_authors.avatarUrl: /avatars/65fc839453d892016640249cc1acf277.svg
paper_authors.fullname: Zhifei Yang
paper_authors.type: user
paper_authors.user: yangzhifei
paper_authors._id: 67d69afb060a3df28c886b2d
paper_authors.name: Yangfan He
paper_authors._id: 67d69afb060a3df28c886b2e
paper_authors.name: Huixiong Zhang
paper_authors._id: 67d69afb060a3df28c886b2f
paper_authors.name: Yuxuan Chen
paper_authors._id: 67d69afb060a3df28c886b30
paper_authors.name: Jingwei Huang
paper_title: MaRI: Material Retrieval Integration across Domains
paper_summary: 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.
paper_ai_keywords: contrastive learning embedding space feature space image encoder material encoder material transfer techniques synthetic materials real-world materials shape variations lighting conditions material retrieval tasks