传统的从明暗恢复形状(Shape from Shading,SFS)技术通常假定物体表面为Lambert表面,然而对于实际的物体表面来说,上述简化造成了重构结果误差较大。近年来,许多学者开始关注于非Lambert表面下的SFS技术的研究。结合作者自身的研究,从...传统的从明暗恢复形状(Shape from Shading,SFS)技术通常假定物体表面为Lambert表面,然而对于实际的物体表面来说,上述简化造成了重构结果误差较大。近年来,许多学者开始关注于非Lambert表面下的SFS技术的研究。结合作者自身的研究,从成像过程建模、图像辐照度方程的建立及求解数值算法出发,介绍了非Lam-bert表面从明暗恢复形状技术的研究进展。首先,简要介绍了非Lambert表面下典型的SFS技术;其次,以Oren-Nayer模型为例详细说明了非Lambert表面SFS方法的关键技术;最后,对非Lambert表面SFS技术的发展趋势进行了探讨。展开更多
在工程实践中,由于制造工艺、光源设计、封装技术等因素的影响,大部分的商用发射器光源有各自独特的辐射特性,属于非朗伯发射器的范畴。但现有无线光局域网的物理信道表征都是基于发端光源为标准的朗伯发射器。针对该问题,将两种典型的...在工程实践中,由于制造工艺、光源设计、封装技术等因素的影响,大部分的商用发射器光源有各自独特的辐射特性,属于非朗伯发射器的范畴。但现有无线光局域网的物理信道表征都是基于发端光源为标准的朗伯发射器。针对该问题,将两种典型的非朗伯发射器的辐射特性引入无线光局域网的物理多径信道表征,并通过与传统朗伯发射器比较,重点分析其对室内无线光覆盖表现的影响。量化结果显示,非朗伯发射器,特别是呈现碗状辐射特性的发射器可以有效提高光路径损耗的空间一致性,提升幅度可达0.5 d B。然而,在覆盖区域的时延特性上,两种非朗伯发射器都不同程度地抬升了均方根时延扩展,抬升幅度分别达到了0.27 ns和0.38 ns。展开更多
Background A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions.It is an ill-defined problem because the general reflectance properties of the surface a...Background A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions.It is an ill-defined problem because the general reflectance properties of the surface are unknown.Methods This paper reviews existing data-driven methods,with a focus on their technical insights into the photometric stereo problem.We divide these methods into two categories,per-pixel and all-pixel,according to how they process an image.We discuss the differences and relationships between these methods from the perspective of inputs,networks,and data,which are key factors in designing a deep learning approach.Results We demonstrate the performance of the models using a popular benchmark dataset.Conclusions Data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional methods.However,these methods suffer from various limitations,such as limited generalization capability.Finally,this study suggests directions for future research.展开更多
文摘传统的从明暗恢复形状(Shape from Shading,SFS)技术通常假定物体表面为Lambert表面,然而对于实际的物体表面来说,上述简化造成了重构结果误差较大。近年来,许多学者开始关注于非Lambert表面下的SFS技术的研究。结合作者自身的研究,从成像过程建模、图像辐照度方程的建立及求解数值算法出发,介绍了非Lam-bert表面从明暗恢复形状技术的研究进展。首先,简要介绍了非Lambert表面下典型的SFS技术;其次,以Oren-Nayer模型为例详细说明了非Lambert表面SFS方法的关键技术;最后,对非Lambert表面SFS技术的发展趋势进行了探讨。
文摘在工程实践中,由于制造工艺、光源设计、封装技术等因素的影响,大部分的商用发射器光源有各自独特的辐射特性,属于非朗伯发射器的范畴。但现有无线光局域网的物理信道表征都是基于发端光源为标准的朗伯发射器。针对该问题,将两种典型的非朗伯发射器的辐射特性引入无线光局域网的物理多径信道表征,并通过与传统朗伯发射器比较,重点分析其对室内无线光覆盖表现的影响。量化结果显示,非朗伯发射器,特别是呈现碗状辐射特性的发射器可以有效提高光路径损耗的空间一致性,提升幅度可达0.5 d B。然而,在覆盖区域的时延特性上,两种非朗伯发射器都不同程度地抬升了均方根时延扩展,抬升幅度分别达到了0.27 ns和0.38 ns。
文摘Background A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions.It is an ill-defined problem because the general reflectance properties of the surface are unknown.Methods This paper reviews existing data-driven methods,with a focus on their technical insights into the photometric stereo problem.We divide these methods into two categories,per-pixel and all-pixel,according to how they process an image.We discuss the differences and relationships between these methods from the perspective of inputs,networks,and data,which are key factors in designing a deep learning approach.Results We demonstrate the performance of the models using a popular benchmark dataset.Conclusions Data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional methods.However,these methods suffer from various limitations,such as limited generalization capability.Finally,this study suggests directions for future research.
