SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909935412969472 |
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| author | Hong, Yijia Guo, Yuan-Chen Yi, Ran Chen, Yulong Cao, Yan-Pei Ma, Lizhuang |
| author_facet | Hong, Yijia Guo, Yuan-Chen Yi, Ran Chen, Yulong Cao, Yan-Pei Ma, Lizhuang |
| contents | Decomposing physically-based materials from images into their constituent properties remains challenging, particularly when maintaining both computational efficiency and physical consistency. While recent diffusion-based approaches have shown promise, they face substantial computational overhead due to multiple denoising steps and separate models for different material properties. We present SuperMat, a single-step framework that achieves high-quality material decomposition with one-step inference. This enables end-to-end training with perceptual and re-render losses while decomposing albedo, metallic, and roughness maps at millisecond-scale speeds. We further extend our framework to 3D objects through a UV refinement network, enabling consistent material estimation across viewpoints while maintaining efficiency. Experiments demonstrate that SuperMat achieves state-of-the-art PBR material decomposition quality while reducing inference time from seconds to milliseconds per image, and completes PBR material estimation for 3D objects in approximately 3 seconds. The project page is at https://hyj542682306.github.io/SuperMat/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17515 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates Hong, Yijia Guo, Yuan-Chen Yi, Ran Chen, Yulong Cao, Yan-Pei Ma, Lizhuang Computer Vision and Pattern Recognition Decomposing physically-based materials from images into their constituent properties remains challenging, particularly when maintaining both computational efficiency and physical consistency. While recent diffusion-based approaches have shown promise, they face substantial computational overhead due to multiple denoising steps and separate models for different material properties. We present SuperMat, a single-step framework that achieves high-quality material decomposition with one-step inference. This enables end-to-end training with perceptual and re-render losses while decomposing albedo, metallic, and roughness maps at millisecond-scale speeds. We further extend our framework to 3D objects through a UV refinement network, enabling consistent material estimation across viewpoints while maintaining efficiency. Experiments demonstrate that SuperMat achieves state-of-the-art PBR material decomposition quality while reducing inference time from seconds to milliseconds per image, and completes PBR material estimation for 3D objects in approximately 3 seconds. The project page is at https://hyj542682306.github.io/SuperMat/. |
| title | SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.17515 |