Single image multimaterial estimation
Single image multimaterial estimation
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DOI：
10.1109/cvpr.2012.6247681
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Stephen Lombardi;K. Nishino
中科院分区：
文献类型：
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作者：
Stephen Lombardi;K. Nishino
当物体表面由多种材料组成时，从单个图像估计反射率和照明变得特别具有挑战性。关键的困难在于从稀疏的角度样本中恢复反射率，同时正确地将它们分配给不同的材料。我们通过提取和充分利用反射率先验来解决这个问题。我们的想法是强烈约束可能的解决方案，使恢复的反射率符合现实世界的材料。我们实现了这一点，通过建模的参数空间的方向统计BRDF模型，并通过提取分析分布的子空间，现实世界的材料跨度。这是使用，与其他先验，在一个分层的MRF为基础的配方，模型材料的区域和它们的空间变化的反射率与连续的潜在层。该方法将物质区域及其反射率、单个点源的方向和强度联合估计。我们证明了该方法的有效性真实的和合成图像。
Estimating the reflectance and illumination from a single image becomes particularly challenging when the object surface consists of multiple materials. The key difficulty lies in recovering the reflectance from sparse angular samples while correctly assigning them to different materials. We tackle this problem by extracting and fully leveraging reflectance priors. The idea is to strongly constrain the possible solutions so that the recovered reflectance conform with those of real-world materials. We achieve this by modeling the parameter space of a directional statistics BRDF model and by extracting an analytical distribution of the subspace that real-world materials span. This is used, with other priors, in a layered MRF-based formulation that models material regions and their spatially varying reflectance with continuous latent layers. The material regions and their reflectance, and the direction and strength of a single point source are jointly estimated. We demonstrate the effectiveness of the method on real and synthetic images.