Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image
Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image
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DOI：
10.1007/978-3-030-01219-9_5
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发表时间：
2018-04
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影响因子：
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通讯作者：
Zhengqin Li;Kalyan Sunkavalli;Manmohan Chandraker
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文献类型：
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作者：
Zhengqin Li;Kalyan Sunkavalli;Manmohan Chandraker
我们提出了一个材质获取系统，它可以从手持手机摄像头拍摄的单幅图像中恢复近平面表面的空间变化的BRDF和法线贴图。我们的技术在打开闪光灯的情况下对任意环境照明下的表面进行成像，从而在避免阴影的同时捕获高频镜面高光。我们训练CNN从这幅图像回归SVBRDF和曲面法线。我们的网络使用大型SVBRDF数据集进行训练，旨在纳入材料评估的物理见解，包括用于建模外观的网络内渲染层和用于在训练期间提供额外监督的材料分类任务。最后，我们使用密集的CRF模块来提炼来自网络的结果，该模块的术语是专门为我们的任务设计的。我们证明了我们基于CNN的SVBRDF推理导致了对合成和真实数据的各种材料的最新结果。我们还提供广泛的消融研究来评估我们的网络，并证明与以前的工作相比有了很大的改进。
We propose a material acquisition system that can recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera. Our technique images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. We train a CNN to regress an SVBRDF and surface normals from this image. Our network is trained using a large-scale SVBRDF dataset and designed to incorporate physical insights for material estimation, including an in-network rendering layer to model appearance and a material classification task to provide additional supervision during training. Finally, we refine the results from the network using a dense CRF module whose terms are designed specifically for our task. We demonstrate that our CNN-based SVBRDF inference leads to state-of-the-art results on a wide variety of materials on both synthetic and real data. We also provide extensive ablation studies to evaluate our network and demonstrate large improvements in comparisons with prior works.