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单一图像的反射和自然照明
Reflectance and Natural Illumination from a Single Image |
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| 课程网址: |
http://videolectures.net/eccv2012_lombardi_image/
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| 主讲教师: |
Bernt Schiele; David Forsyth; Stephen Lombardi
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| 开课单位: |
德雷塞尔大学
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| 开课时间: |
信息不详。欢迎您在右侧留言补充。
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| 课程语种: |
英语
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| 中文简介: |
从已知形状物体的单个图像中估算反射率和自然光照是一项具有挑战性的任务，因为反射率和光照之间存在模糊性。虽然由于反射带限制了光照，因此可以恢复的范围存在固有限制，但对于许多计算机视觉应用来说，明确估计两者都是可取的。实现这一估计需要我们推导并对两个变量施加强大的约束。我们引入了一个概率公式，它无缝地结合了诸如先验等约束条件，从而得出反射比和自然光照的最大后验估计值。我们首先证明反射比以增加其熵的方式调节自然光照。基于这一观察，我们在符合自然图像统计的前提下，在光照上施加一个优先权，它有利于降低熵。我们还基于方向统计brdf模型对反射率施加了先验值，该模型将估计值限制在实际材料的界限和变化范围内。对多幅合成图像和真实图像的实验结果表明，该方法能够实现对不同材料和光照组合的精确联合估计。
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| 课程简介: |
Estimating reflectance and natural illumination from a single image of an object of known shape is a challenging task due to the ambiguities between reflectance and illumination. Although there is an inherent limitation in what can be recovered as the reflectance band-limits the illumination, explicitly estimating both is desirable for many computer vision applications. Achieving this estimation requires that we derive and impose strong constraints on both variables. We introduce a probabilistic formulation that seamlessly incorporates such constraints as priors to arrive at the maximum a posteriori estimates of reflectance and natural illumination. We begin by showing that reflectance modulates the natural illumination in a way that increases its entropy. Based on this observation, we impose a prior on the illumination that favors lower entropy while conforming to natural image statistics. We also impose a prior on the reflectance based on the directional statistics BRDF model that constrains the estimate to lie within the bounds and variability of real-world materials. Experimental results on a number of synthetic and real images show that the method is able to achieve accurate joint estimation for different combinations of materials and lighting.
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| 关 键 词: |
计算机视觉; 图像; 计算机科学; 自然光照
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-10：cwx
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| 阅读次数: |
68
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