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元宇宙对三维显示提出了极高的要求,用户需要在三维虚拟世界中完全自由的浏览和交互。当前,动态三维计算全息面临的挑战主要集中在以下几个方面:首先,现有的计算全息图存在重建质量受限的问题,需要设计低噪声的全息方法,实现高质量、低噪声的全息重建;其次,器件的性能参数和系统的装调误差制约了全息显示质量,需要针对器件和系统特点开发像质优化和畸变校正技术,实现高精度、无畸变的全息显示;最后,显示内容的匮乏和展示度的不足阻碍了计算全息显示的应用前景,需要获取符合计算全息特点的三维内容源并构建高展示度的全息三维显示系统,实现高刷新、真三维的全息显示。
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在全息图的计算过程中,随机相位常常被用于模拟真实物体的表面散射特性[71]。目标物体的低频分量在随机相位的作用下扩散到了全息记录平面上的更大区域,避免了全息图动态范围不足对全息重建质量的限制,获得了更高的全息重建质量。然而,随机相位又造成了目标物体高频分量的过度扩散,导致部分高频信息无法被全息图完全记录,制约了重建结果的细节展现能力。此外,随机相位中不连续的相位突变点会导致散斑噪声[72],显著降低了全息图的重建质量。为了解决这一问题,全息算法中的随机相位需要被进一步优化。
Wyrowski等人提出了一种基于迭代的随机相位优化方法,提高了纯振幅型全息图的重建质量[73]。该方法迭代优化的对象是随机相位而非目标物体,因此目标物体发生改变时,无需再次进行迭代,显著提高了不同物体全息图的生成效率[74]。Ma等人提出了基于幅值约束的随机相位优化方法[75]。该方法中,随机相位的幅值会根据目标物体的不同而发生改变,经过时间平均后全息重建结果的峰值信噪比(PSNR)较传统方法可提高10 dB以上。Zhao等人提出了基于梯度的随机相位优化方法[76]。该方法在目标物体的低频和高频分量上分别叠加了不同的随机相位,避免了高频信息的过度扩散,将全息重建结果的PSNR值提高了10%以上。Nagahama等人提出了零随机相位法[77]。该方法基于球面波计算获得了全息面的复振幅分布,计算过程无需使用随机相位,可以在较低的运算量下抑制全息重建结果中的散斑噪声。Mengu和Cruz等人提出了结构相位法,通过使用结构化初始相位替代随机初始相位的方式,降低了全息重建结果中的散斑噪声,为全息图的高质量快速生成提供了一套具有操作性的解决方案[78-79]。He等人提出了基于物体频率的最优随机相位法[80]。该方法将物体分割为若干个频带,通过遍历搜索的方式确定不同频带的随机相位特征,使全息重建结果获得了更好的细节表现能力和更低的散斑对比度。但是,考虑到不同目标物体的表面散射特性差异较大,针对某些目标,上述优化方法对全息重建质量的提升效果并不显著。
除了直接优化随机相位分布的方式外,通过迭代计算改变全息面的信息分布,也是提高全息重建质量的一种重要手段。针对相位型全息图而言,Gerchberg-Saxton(G-S)算法是最常用的迭代优化方法[81]。该方法最初被用于提升傅里叶全息图的重建质量。但是,傅里叶全息图是一种远场全息图,难以用来重建三维场景。为此,Chen等人设计了基于菲涅尔变换的改进型G-S算法[82-84],实现了三维场景的高质量全息重建。不过,由于菲涅尔变换的物面采样间隔与重建距离相关,因此算法设计过程中需要根据重建距离调整不同深度平面的采样参数。当目标三维场景的深度范围较大时,该方法的算法复杂度较高。迭代优化算法获得的全息图通常为局部优化结果,其全息重建质量往往会受到目标物体强度分布、显示系统性能参数以及迭代初始条件等因素的影响。同时,迭代优化算法针对每一个目标物体均需要进行多次正、逆向波前传播计算,计算时间较长,难以应用于高实时性的三维显示系统中。
近年来,深度学习技术与计算全息技术的交叉结合,使得高质量全息图的快速计算成为可能[85-89]。如图8所示,Wu等人提出了一种基于自编码深度学习网络的计算全息算法,可以在0.15 s内生成重建信噪比高达23.2 dB的计算全息图,大幅提升了高质量、低噪声全息图的生成效率[90]。上述随机相位优化法、迭代法和深度学习方法的特点比较如表1所示。深度学习算法拥有低噪声和高效率的双重优点,在全息三维显示领域具有广阔的应用前景。
表 1 全息图优化方法的性能对比
Table 1. Comparison of optimization methods for holographic
Speed Quality Training time Generalization Random-phase-based methods Medium Medium NA Medium Iteration-based methods Slow High NA High Learning-based methods Fast High Long High -
全息图计算完成后,需要加载至SLM表面,在相干光照明下完成光学重建。通常,全息图的光学重建质量会受到光学元件制造精度和装配精度的影响。在全息光学系统中,透镜及其他折反射元件可能存在制造缺陷。这些缺陷会改变照明光波和重建光波的波前分布,进而影响全息图的光学重建效果。同时,光学元件的装配可能存在误差;长期使用过程中受到震动等因素的影响,光学元件可能偏离最初设计位置。这些误差会导致全息重建结果的波前分布偏离预设值,进而影响全息图的光学重建效果。光学元件的制造缺陷和装调误差可以通过更换元件、重新装配的方式消除,但这种校正方式步骤繁琐、工作量较大,难以应用在集成度较高的系统中。通过校正算法消除光学系统中的误差,可以避免光学元件的重新安装与调整,比较适合用在集成后的全息显示系统中。Yao和Anderson等人提出了基于波前补偿的系统误差消除方法,降低了光学元件制造缺陷和装调误差对波前分布的影响,提升了全息显示系统的光学重建质量[91-92]。上述方法需要使用Shack-Hartmann传感器以及变形镜等专门元器件,提升了系统的复杂度和构建成本。
考虑到计算全息三维显示本质上是一种波前调控技术,因此在全息图上叠加与系统误差相反的信号分布,也可以实现波前畸变的消除,进而提升全息图的光学重建质量[93-95]。Kaczorowski和Haist等人提出的波前畸变校正方法通过普通相机捕捉系统误差、通过在全息图上叠加相反信号校正系统误差,最终提升了全息图的光学重建质量[96-98]。这些方法无需采用专门元器件,凭借较低的系统复杂度和构建成本即可实现全息波前畸变的校正。不过,当前主流的校正方法通常需要使用泽尼克系数描述波前畸变的分布,对系统的计算能力要求较高。He等人提出了一种基于四步相移法和角谱传播模型的全息波前畸变校正方法[99]。该方法无需使用专门元器件,也无需计算泽尼克系数,降低了波前畸变校正系统的计算量和硬件复杂度。不过,受限于四步相移法的处理速度,该方法只能实现波前畸变的预先标定与校正,无法完成波前畸变的在线消除。上述各类畸变校正方法的特点比较如表2所示。综合考虑软硬件成本,基于通用元器件且计算量较低的校正方法是该领域的重要追求。
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元宇宙的底层需求之一是三维虚拟世界的构建与展示。建立虚拟世界的几何模型,并且在终端设备上予以显示,给用户实时真实地展现各种数字场景和数字内容,才能制造出沉浸式的用户体验。三维显示为虚拟世界的展示提供了重要的解决思路,但是当前仍面临着三维内容严重不足的重大难题[100]。对于计算全息三维显示而言,三维内容不足的情况更为严重。当前的三维视频内容通常会根据双目视觉原理进行渲染,但这些渲染结果无法直接应用于计算全息三维显示中。因此,获取适用于计算全息的三维内容,是目前全息三维显示需要面对的重大课题。
点云模型[101-102]、分层模型[103-104]以及多边形模型[105-106]均可以用在已有的计算全息算法中。获取上述三维模型的常用方法有建模法和拍摄法两类。建模法通过计算机绘制并渲染目标三维模型,具有清晰度高、深度信息准确等优点,但是渲染过程对计算机硬件要求较高,存在渲染时间长、计算量大等问题。拍摄法利用三维采集设备获取真实场景的深度信息,并根据需求将采集结果渲染成为不同种类的三维模型。常见的三维采集设备包括相机阵列[107]、时间飞行(TOF)相机[108]、透镜阵列相机[109]以及结构光相机[110]等。相机阵列分辨率高、深度范围大且深度信息准确,但渲染绘制过程对计算机硬件依赖程度较高,渲染速度较慢。TOF相机成像速度快、深度范围大,但存在图像分辨率较低的不足。透镜阵列相机在拍摄距离较短时深度信息准确,但有效分辨率较为有限,并且深度信息的准确性会随着拍摄距离的增加而快速下降[111-112]。结构光相机分辨率高、深度信息准确,但需要拍摄多幅条纹图像以求解三维场景的深度信息,存在处理速度较慢的不足。实际应用中,针对不同的拍摄场景,采用的三维采集设备也需要进行适当调整。
建模法和拍摄法面临的困难主要集中在两个方面:首先,难以兼容已有的二维拍摄系统和图像处理方法,系统构建成本高,数据处理难度大;其次,三维内容需要重新创作,无法利用二维内容进行转化,短时间内难以解决三维内容匮乏的问题。二维转三维(2D-to-3D)技术为三维内容的制作提供了新的解决思路。2D-to-3D技术是一种利用二维图像的特征信息计算生成三维内容的技术。Tsai和Cheng等人[113-114]利用图像的边缘信息,实现了深度线索的提取。Lai等人利用图像的纹理信息,确定了二维图像中不同物体的位置关系,实现了基于二维图像的三维显示[115]。Zhang等人利用图像的颜色信息,实现了深度线索的提取,完成了三维模型的实时显示[116]。Gil等人利用视频图像的运动特征,实现了三维视频的实时渲染[117]。利用单一特征的2D-to-3D技术通常只能实现某些特定图像的深度线索提取,适用范围较为有限。He等人提出了一种基于混合特征的2D-to-3D技术,并将其直接应用于全息算法中,获得了良好的全息三维重建效果[118],如图9所示。近年来,随着人工智能技术的发展,深度学习网络也被用来提取二维图像中的深度线索[119-121]。人工智能算法需要使用先验信息进行训练,才能实现二维图像的三维转化,因此对三维数据源有较大依赖性。上述三维内容源生成方法的特点比较如表3所示。考虑到不同应用场景对三维内容源的具体要求存在差异,因此针对不同的应用场景需要采用不同的方法获取三维内容源。
表 3 三维内容源生成方法的特点对比
Table 3. Comparison of generation methods of 3D content
Resolution Depth range Processing time Compatibility Hardware-based methods Camera array High Large Long Medium TOF camera Low Large Short Low Lens-array camera Medium Medium Medium Low Structured illumination camera High Medium Long Low Software-based methods 3D modeling methods High Large Long Low 2D-to-3D methods High Large Medium High
Progress and challenges in dynamic holographic 3D display for the metaverse (Invited)
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摘要: 相比二维显示,三维显示可以提供更接近真实世界的图像内容,是5G、大数据、元宇宙和物联网领域的关键性技术。计算全息三维显示可以提供所有种类的深度线索,被认为是三维显示的终极实现方式,在智能制造、远程教育、异地办公和娱乐社交等领域都具有广阔的应用前景。文中首先介绍了计算全息技术的发展历史和重要技术节点,分析了高质量全息动态三维显示面临的挑战,主要包括计算全息图重建质量不足、波前调制器件和全息显示系统性能受限以及三维内容源匮乏等,进一步介绍了已有的解决方案,比较了各类方案的优势与不足,进而分析了高质量全息动态三维显示的主要研究方向,包括低噪声全息图的获取、像质优化和畸变校正技术的开发以及三维内容源的构建。实现高质量、低噪声、无畸变、高刷新、真三维的动态全息三维显示,是计算全息显示发展的必由之路,也是元宇宙等典型应用对全息三维显示提出的必然要求。Abstract: Compared to 2D display, 3D display presents information with more realistic visual perceptions. It is a vital technology in the fields of 5G, metaverse, big data and internet of things. 3D display based on computer-generated holography can provide various kinds of depth cues. It presents an outstanding 3D effect and is considered as an ultimate form of 3D display. It is believed that holographic 3D display would have broad application prospects in intelligent manufacturing, distance education, telecommuting, entertainment and social networking. In this review, the history and important technical nodes of computer-generated holography were introduced. According to the current status of the technology, main challenges faced by high-quality dynamic holographic 3D display were introduced, including limited accuracy of computer-generated hologram, imperfect performance of wavefront modulator and holographic display system, and shortage of 3D content. Focusing on these challenges, the existing solutions were summarized. Advantages and disadvantages of various solutions were compared. Research directions of high-quality dynamic holographic 3D display were analyzed, including low-noise holographic algorithms, distortion calibration methods, and 3D content generation technologies. Holographic 3D display with low noise, high refresh rate and high precision, which were inevitable in some applications such as meteverse, could be realized by addressing these issues.
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表 1 全息图优化方法的性能对比
Table 1. Comparison of optimization methods for holographic
Speed Quality Training time Generalization Random-phase-based methods Medium Medium NA Medium Iteration-based methods Slow High NA High Learning-based methods Fast High Long High 表 2 全息畸变校正方法的特点对比
Table 2. Comparison of distortion calibration methods in holography
表 3 三维内容源生成方法的特点对比
Table 3. Comparison of generation methods of 3D content
Resolution Depth range Processing time Compatibility Hardware-based methods Camera array High Large Long Medium TOF camera Low Large Short Low Lens-array camera Medium Medium Medium Low Structured illumination camera High Medium Long Low Software-based methods 3D modeling methods High Large Long Low 2D-to-3D methods High Large Medium High -
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