近光源光度立体三维重建误差分析

一般假设的平行光光源,即远光源模型,受安装空间的约束在现实世界中很难实现。针对这一问题,提出了近光源模型解决近光源大尺寸物体的三维重建。近光源模型是一种新型的光源位置假设,需要标定光源位置,计算目标物体每个点上的光线方向,这样即使当物体尺寸较大或距离光源较近时,近光源模型也是对光源较优的一种假设。通过分析上述两种光源模型对噪声、光源到目标物体距离等因素的敏感度,比较发现当光源距离物体较近或物体尺寸较大的情况下,近光源模型的重建结果优于远光源的重建结果。     

图像亮度线索下的单目深度信息提取

目的 深度信息的获取是3维重建、虚拟现实等应用的关键技术,基于单目视觉的深度信息获取是非接触式3维测量技术中成本最低、也是技术难度最大的手段。传统的单目方法多基于线性透视、纹理梯度、运动视差、聚焦散焦等深度线索来对深度信息进行求取,计算量大,对相机精度要求高,应用场景受限,本文基于固定光强的点光源在场景中的移动所带来的物体表面亮度的变化,提出一种简单快捷的单目深度提取方法。方法 首先根据体表面反射模型,得到光源照射下的物体表面的辐亮度,然后结合光度立体学推导物体表面辐亮度与摄像机图像亮度之间的关系,在得到此关系式后,设计实验,依据点光源移动所带来的图像亮度的变化对深度信息进行求解。结果 该算法在简单场景和一些日常场景下均取得了较好的恢复效果,深度估计值与实际深度值之间的误差小于10%。结论 本文方法通过光源移动带来的图像亮度变化估计深度信息,避免了复杂的相机标定过程,计算复杂度小,是一种全新的场景深度信息获取方法。     

基于双视角的可见外壳三维重建研究

针对双目视觉重建方法在三维重建过程中步骤繁琐与重建效果不理想的问题,文中提出了一种基于双视角下可见外壳的三维重建方法。该方法主要借助平面镜成像原理,在不同视角拍摄包含物体与其成像的两幅图像,并根据成像原理确定物体之间的位置关系。通过相机与周围物体的运动关系求得相机参数,实现相机自标定。然后根据阈值分割及区域生长算法处理边缘信息得到目标轮廓,通过可见外壳方法计算侧影轮廓线,连接轮廓线形成重建模型。该方法通过自适应可见外壳种子体素生长在不了解物体先验知识的情况下完成重建。文中对比文献"基于图像轮廓的三维重建方法"中的单视角重建物体方法,提出了双视角重建物体模型方法。实验结果表明,双视角重建方法简单实用,生成的三维模型准确真实。     

利用采样球体提取真实感纹理

提出了一种从真实物体中提取纹理的方法.利用具有复杂纹理的参考球体作为被采样物体,计算其组成材质的BRDF(bidirectional reflectance distribution function)模型参数以及各点由不同材质构成的比例,形成一幅材质权重图.该图作为纹理映射到3D物体上后,配合BRDF模型参数进行渲染,形成一种适用于重光照(relighting)的纹理.被渲染物体可根据自身方位以及光源亮度/方位呈现出自然的光影变化,达到较为逼真的外观效果.     

一种基于双幅图像的物体3维重建

随着虚拟现实技术的发展,对物体进行快速有效建模逐渐成为了研究热点。基于图像建模技术是一种有效的建模方法,其最大的优点是可以利用图像中的信息直接建立物体的几何模型,并且可以迅速构建出具有“照片级”真实感的3维模型。常规的方法是先求解摄像机内外参数,再求解空间中各点在世界坐标系下的位置。提出了一种基于两幅图像利用灭点性质进行建模的新方法。该方法首先利用灭点属性推导出了相机中心在世界坐标系下的坐标位置的解析表达式,再利用两幅图像之间的空间几何约束关系,从空间约束关系中反算出物体各点的空间位置从而重建出物体几何模型,最后进行纹理映射。在物体重建的过程中只利用到了相机内参数,而跳过了对相机外参数的标定,实验结果表明,该方法简单可靠,可用于实现对拥有规则表面的物体3维重建。     

一种基于校正误差的立体相机标定算法

立体相机的标定是一个精确求解各个相机内参数以及相机之间关系参数的过程.它是三维重建的基础,其标定精度的好坏直接影响立体重建的结果.为此提出了一种使用校正误差作为代价函数的立体相机标定算法.该算法首先使用传统的基于重投影误差的方法对单个相机的内参数进行标定,然后利用校正误差完成对相机之间关系参数的标定求解.由于校正误差的计算只与相机内参数以及关系参数有关,可以避免在标定过程中使用难以精确标定的相机外参数.实验结果表明本算法能够有效的提高立体相机标定的精度.     

视线追踪系统中视线估计方法研究

文中提出一种基于瞳孔—角膜反射(PCCR)的视线估计方法(GEMHSSO).针对现有PCCR存在的主要问题:限制使用者头部运动和个体标定问题,提出了一种单相机单光源条件下头部位置的补偿方法,实现了头部位置变化对瞳孔角膜向量影响的解析补偿,并建立一种个体差异的转化模型,进而简化标定过程为单点标定.以此为基础形成一种新的视线估计方法,本方法使精确视线估计的最小硬件要求降低到单相机(未标定)单光源,既不需要繁杂的系统标定,又实现了自然头动视线估计,并且简化用户标定为单点标定.该方法的各个环节都满足实时性要求,为面向人机交互的视线追踪系统提供了有效的解决方案.     

基于高动态范围图像中光晕分析的光照方向测算算法

针对增强现实(AR)系统中复杂场景的光照一致性问题,运用高动态范围(HDR)图像处理技术对标识物图像进行分析,提出一种基于HDR图像中光晕分析的光照方向测算算法。为了提高虚拟物体的沉浸感和真实性,在研究和分析现有的光照恢复算法的基础上,利用二次曲线对的投影不变性原理进行相机标定;为了获得更加详尽的光照信息,使用HDR技术对标识物图像进行处理,提高了测算精确度;参照Lambert光照模型对图像中的光照信息进行分析,将拍摄角度进行分类,实现了对传统测算光源方向算法的改进,可测算出位于摄影球反射范围外的部分光源的方向,扩大了测算范围。设计了针对单一点光源的视角1和视角2测算验证实验,并进行了分析。实验验证表明,所提方法简单易行,具有较强的鲁棒性,能够实现在标识物部分遮挡的情况下,测算出位于摄影球反射范围外部分光源的方向。     

基于PTM模型文物纹理映射算法

针对三维重建物体纹理不真实与不精确问题，提出了基于PTM（Polynomial Texture Maps）模型的文物纹理映射算法，该算法利用了多项式颜色依赖性的特质，采取表征颜色与亮度依赖关系的双二次多项式系数进行存储纹素数据，从而实现在变化的光照条件下重建物体的表面纹理。此外针对原PTM模型中存在的原始采集图像漫反射异常、亮度信息分布不均匀、重建拟合系数不精确，导致的重建图像纹理模糊、存在重影、物体细节纹理缺失等问题，提出了改进多项式的基函数和优化拟合系数的PTM算法以及PTM图像采集设备的优化方法，最后经实验验证，提出的算法使得重建物体纹理的真实性与精确度均得到了有效提高。     

基于线性变换球面分布的实时间接光泽反射

目的 在实时渲染领域中,立即辐射度算法是用于实时模拟间接光泽反射效果的算法之一。基于立即辐射度的GGX SLC(stochastic light culling)算法中使用符合真实物理定律的GGX BRDF(bidirectional reflectance distribution function)光照模型计算间接光泽反射,计算复杂度很高,并且其计算开销会随着虚拟点光源的数量呈明显的线性增长。为解决上述问题,提出一种更高效的实时间接光泽反射渲染算法。方法 基于数学方法中的线性变换球面分布,将计算复杂度很高的GGX BRDF球面分布近似为一种计算复杂度较低的球面分布,并基于该球面分布提出了在单点光源以及多点光源环境下的基于物理的快速光照模型。该光照模型相比GGX BRDF光照模型具有更低的计算开销。然后基于该光照模型,提出实时间接光泽反射渲染算法,计算虚拟点光源对着色点的辐射强度,结合多点光源光照模型对着色点着色,高效地渲染间接光泽反射效果。结果 实验结果表明,改进后的实时间接光泽反射算法能够以更高的渲染效率实现与GGX SLC算法相似的渲染效果,渲染效率提升了20%～40%,并且场...     

Photometric stereo using LCD displays

This paper considers the problem of shape-from-shading using nearby extended light sources. The paper reviews a number of methods that employ nearby illuminants, and describes a new technique that assumes a rectangular planar nearby distributed uniform isotropic illuminant. It is shown that such a light source illuminating a small Lambertian surface patch is equivalent to a single isotropic point light source at infinity, in the absence of shadowing. A closed-form solution is given for the equivalent point light source direction in terms of the illuminant corner locations. Equivalent point light sources can be obtained for distinct illuminant patterns allowing standard photometric stereo algorithms to be used. An extension is given to the case of a rectangular planar illuminant with arbitrary radiance distribution. Experimental results are shown demonstrating the application of the theory to photometric stereo using illumination from a LCD computer monitor. Details on the photometric calibration of the illumination source and image acquisition device are provided.     

Light source position calibration method for photometric stereo in capsule endoscopy

ABSTRACT

For photometric stereo in capsule endoscopy, calibration of light source is crucial for improving the precision of surface normal estimation. Therefore, this paper presents an improved planar-mirror-based light source position calibration method: from captured images of light source and detected poses of planar mirror, light paths are retraced from camera to light source, and position of light source is triangulated with least square method. The contribution of this paper is that a refraction model of the planar mirror is employed in the retracement of light paths, thus the bias of light paths caused by refraction can be compensated and the position of light source can be estimated more precisely. The results of simulation and experiment show that the proposed method provides higher calibration accuracy than the current planar mirror-based calibration method and can improve the precision of subsequent photometric stereo-based 3D reconstruction.     

BRDF invariant stereo using light transport constancy

Nearly all existing methods for stereo reconstruction assume that scene reflectance is Lambertian and make use of brightness constancy as a matching invariant. We introduce a new invariant for stereo reconstruction called light transport constancy (LTC), which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions (BRDFs)). This invariant can be used to formulate a rank constraint on multiview stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies. In addition, we show that this multiview constraint can be used with as few as two cameras and two lighting configurations. Unlike previous methods for BRDF invariant stereo, LTC does not require precisely configured or calibrated light sources or calibration objects in the scene. Importantly, the new constraint can be used to provide BRDF invariance to any existing stereo method whenever appropriate lighting variation is available.     

Photometric Stereo with General,Unknown Lighting

Work on photometric stereo has shown how to recover the shape and reflectance properties of an object using multiple images taken with a fixed viewpoint and variable lighting conditions. This work has primarily relied on known lighting conditions or the presence of a single point source of light in each image. In this paper we show how to perform photometric stereo assuming that all lights in a scene are distant from the object but otherwise unconstrained. Lighting in each image may be an unknown and may include arbitrary combination of diffuse, point and extended sources. Our work is based on recent results showing that for Lambertian objects, general lighting conditions can be represented using low order spherical harmonics. Using this representation we can recover shape by performing a simple optimization in a low-dimensional space. We also analyze the shape ambiguities that arise in such a representation. We demonstrate our method by reconstructing the shape of objects from images obtained under a variety of lightings. We further compare the reconstructed shapes against shapes obtained with a laser scanner.     

LED-Based Photometric Stereo: Modeling,Calibration and Numerical Solution

We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be directional, which is very difficult to achieve in practice. Rather, we use light-emitting diodes to illuminate the scene to be reconstructed. Such point light sources are very convenient to use, yet they yield a more complex photometric stereo model which is arduous to solve. We first derive in a physically sound manner this model, and show how to calibrate its parameters. Then, we discuss two state-of-the-art numerical solutions. The first one alternatingly estimates the albedo and the normals, and then integrates the normals into a depth map. It is shown empirically to be independent from the initialization, but convergence of this sequential approach is not established. The second one directly recovers the depth, by formulating photometric stereo as a system of nonlinear partial differential equations (PDEs), which are linearized using image ratios. Although the sequential approach is avoided, initialization matters a lot and convergence is not established either. Therefore, we introduce a provably convergent alternating reweighted least-squares scheme for solving the original system of nonlinear PDEs. Finally, we extend this study to the case of RGB images.     

Shape Reconstruction of 3D Bilaterally Symmetric Surfaces

The paper presents a new approach for shape recovery based on integrating geometric and photometric information. We consider 3D bilaterally symmetric objects, that is, objects which are symmetric with respect to a plane (e.g., faces), and their reconstruction from a single image. Both the viewpoint and the illumination are not necessarily frontal. Furthermore, no correspondence between symmetric points is required.The basic idea is that an image taken from a general, non frontal viewpoint, under non-frontal illumination can be regarded as a pair of images. Each image of the pair is one half of the object, taken from different viewing positions and with different lighting directions. Thus, one-image-variants of geometric stereo and of photometric stereo can be used. Unlike the separate invocation of these approaches, which require point correspondence between the two images, we show that integrating the photometric and geometric information suffice to yield a dense correspondence between pairs of symmetric points, and as a result, a dense shape recovery of the object. Furthermore, the unknown lighting and viewing parameters, are also recovered in this process.Unknown distant point light source, Lambertian surfaces, unknown constant albedo, and weak perspective projection are assumed. The method has been implemented and tested experimentally on simulated and real data.     

Retrieving shape information from multiple images of a specularsurface

In many remote sensing and machine vision applications, the shape of a specular surface such as water, glass, or polished metal must be determined instantaneously and under natural lighting conditions. Most image analysis techniques, however, assume surface reflectance properties or lighting conditions that are incompatible with these situations. To retrieve the shape of smooth specular surfaces, a technique known as specular surface stereo was developed. The method analyzes multiple images of a surface and finds a surface shape that results in a set of synthetic images that match the observed ones. An image synthesis model is used to predict image irradiance values as a function of the shape and reflectance properties of the surface, camera geometry, and radiance distribution of the illumination. The specular surface stereo technique was tested by processing four numerical simulations-a water surface illuminated by a low- and high-contrast extended light source, and a mirrored surface illuminated by a low- and high-contrast extended light source. Under these controlled circumstances, the recovered surface shape showed good agreement with the known input     

Estimating the surface radiance function from single images

This paper describes a simple method for estimating the surface radiance function from single images of smooth surfaces made of materials whose reflectance function is isotropic and monotonic. The method makes implicit use of the Gauss map between the surface and a unit sphere. We assume that the material brightness is monotonic with respect to the angle between the illuminant direction and the surface normal. Under conditions in which the light source and the viewer directions are identical, we show how a tabular representation of the surface radiance function can be estimated using the cumulative distribution of image gradients. Using this tabular representation of the radiance function, surfaces may be rendered under varying light source direction by rotating the corresponding reflectance map on the Gauss sphere about the specular spike direction. We present a sensitivity study on synthetic and real-world imagery. We also present two applications which make use of the estimated radiance function. The first of these illustrates how the radiance function estimates can be used to render objects when the light and viewer directions are no longer coincident. The second application involves applying corrected Lambertian radiance to rough and shiny surfaces.     

Uncalibrated flatfielding and illumination vector estimationfor photometric stereo face reconstruction

Within the context of photometric stereo reconstruction, flatfielding may be used to compensate for the effect of the inverse-square law of light propagation on the pixel brightness. This would require capturing a set of reference images at an off-line imaging session, which employs a calibrating device that should be captured under the exact conditions as the main session. Similarly, the illumination vectors, on which photometric stereo relies, are typically precomputed based on another dedicated calibration session. In practice, implementing such off-line sessions is inconvenient and often infeasible. This work aims at enabling accurate photometric stereo reconstruction for the case of non-interactive on-line capturing of human faces. We propose unsupervised methodologies, which extract all information that is required for accurate face reconstruction from the images of interest themselves. Specifically, we propose an uncalibrated flatfielding and an uncalibrated illumination vector estimation methodology, and we assess their effect on photometric stereo face reconstruction. Results demonstrate that incorporating our methodologies into the photometric stereo framework halves the reconstruction error, while eliminating the need of off-line calibration.