MaRI：跨領域材料檢索整合

摘要

精確的材料檢索對於創建逼真的3D資產至關重要。現有方法依賴於捕捉形狀不變和光照多樣的材料表示的數據集，這些數據集稀缺且因多樣性有限和現實世界泛化能力不足而面臨挑戰。目前大多數方法採用傳統的圖像搜索技術，這些技術在捕捉材料空間的獨特屬性方面表現不足，導致檢索任務的性能欠佳。為應對這些挑戰，我們引入了MaRI，這是一個旨在彌合合成與現實世界材料之間特徵空間差距的框架。MaRI通過對比學習策略構建了一個共享的嵌入空間，該策略通過聯合訓練圖像和材料編碼器，使相似的材料和圖像在特徵空間中更接近，同時分離不相似的對。為支持這一點，我們構建了一個全面的數據集，包含使用受控形狀變化和多樣光照條件渲染的高質量合成材料，以及使用材料傳輸技術處理和標準化的現實世界材料。大量實驗表明，MaRI在多樣且複雜的材料檢索任務中表現出卓越的性能、準確性和泛化能力，優於現有方法。

English

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：跨領域材料檢索整合

MaRI: Material Retrieval Integration across Domains

摘要

Summary

Support

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