MaRI: Material Retrieval Integration across Domains

• URL: http://arxiv.org/abs/2503.08111v3
• Date: Tue, 08 Apr 2025 08:53:57 GMT
• Title: MaRI: Material Retrieval Integration across Domains
• Authors: Jianhui Wang, Zhifei Yang, Yangfan He, Huixiong Zhang, Yuxuan Chen, Jingwei Huang,
• Abstract summary: Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of materials.<n>We introduce MaRI, a framework designed to bridge the feature space gap between synthetic and real-world materials.
• Score: 13.686066106732696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
  Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation.
  arXiv  Detail & Related papers  (2025-02-26T10:25:32Z)
• MaterialFusion: High-Quality, Zero-Shot, and Controllable Material Transfer with Diffusion Models [1.7749342709605145]
  We present MaterialFusion, a novel framework for high-quality material transfer.<n>It allows users to adjust the degree of material application, achieving an optimal balance between new material properties and the object's original features.
  arXiv  Detail & Related papers  (2025-02-10T16:04:33Z)
• IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations [64.07859467542664]
  Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics.<n>Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs.<n>We introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations.
  arXiv  Detail & Related papers  (2024-12-16T18:52:56Z)
• OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction [54.706361479680055]
  We introduce the OpenMaterial dataset, comprising 1001 objects made of 295 distinct materials. OpenMaterial provides comprehensive annotations, including 3D shape, material type, camera pose, depth, and object mask. It stands as the first large-scale dataset enabling quantitative evaluations of existing algorithms on objects with diverse and challenging materials.
  arXiv  Detail & Related papers  (2024-06-13T07:46:17Z)
• Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials [108.59709545364395]
  GPT-4V can effectively recognize and describe materials, allowing the construction of a detailed material library. The correctly matched materials are then meticulously applied as reference for the new SVBRDF material generation. Make-it-Real offers a streamlined integration into the 3D content creation workflow.
  arXiv  Detail & Related papers  (2024-04-25T17:59:58Z)
• IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination [37.96484120807323]
  This paper aims to recover object materials from posed images captured under an unknown static lighting condition. We learn the material prior with a generative model for regularizing the optimization process. Experiments on real-world and synthetic datasets demonstrate that our approach achieves state-of-the-art performance on material recovery.
  arXiv  Detail & Related papers  (2024-04-17T17:45:08Z)
• Intrinsic Image Diffusion for Indoor Single-view Material Estimation [55.276815106443976]
  We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by $1.5dB$ on PSNR and by $45%$ better FID score on albedo prediction.
  arXiv  Detail & Related papers  (2023-12-19T15:56:19Z)
• Multimodal Learning for Materials [7.167520424757711]
  We introduce Multimodal Learning for Materials (MultiMat), which enables self-supervised multi-modality training of foundation models for materials. We demonstrate our framework's potential using data from the Materials Project database on multiple axes.
  arXiv  Detail & Related papers  (2023-11-30T18:35:29Z)
• One-shot recognition of any material anywhere using contrastive learning with physics-based rendering [0.0]
  We present MatSim: a synthetic dataset, a benchmark, and a method for computer vision based recognition of similarities and transitions between materials and textures. The visual recognition of materials is essential to everything from examining food while cooking to inspecting agriculture, chemistry, and industrial products.
  arXiv  Detail & Related papers  (2022-12-01T16:49:53Z)
• Bridging Composite and Real: Towards End-to-end Deep Image Matting [88.79857806542006]
  We study the roles of semantics and details for image matting. We propose a novel Glance and Focus Matting network (GFM), which employs a shared encoder and two separate decoders. Comprehensive empirical studies have demonstrated that GFM outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-30T10:57:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.