未知光源位置环境中物体形状恢复的神经网络方法研究

用神经网络方法解决未知光源位置环境中物体三维形状恢复的问题.对漫反射表 面,用神经网络方法由已知表面形状物体及其对应图像的灰度值进行学习,所得权值可视为 环境光源参数.由此可恢复同样光源环境中其它物体的三维形状.实验证明,神经网络方法可 以解决未知光源位置环境(包括多个光源)中漫反射表面物体的三维形状恢复问题.     

一种简易的线结构光投影三维形状获取方法

利用普通的LCD投影仪和数码相机获得物体的三维形状.投影仪作为光源,投射线结构光在物体表面,从数码相机拍摄的照片序列中,恢复物体的形状.提出了根据已经标定好的相机参数,继续对投影仪进行标定的方法;同时,针对物体表面颜色/纹理较为复杂的情况,提出了一次投射三色线结构光的改进方法,克服了不易恢复此类物体形状的困难.实验结果表明,该方法可以获得物体的稠密点云用于进一步的重建,适于组建一个廉价但却保持相对精度的扫描系统.     

基于Phong模型的明暗恢复形状的新算法

针对传统的混合表面形状恢复算法存在较大误差的问题,提出一种透视投影下从单幅图像混合表面明暗信息恢复形状的新算法。采用Phong反射模型来描述物体表面反射特性,假设光源处于相机的光心处,建立透视投影下的图像辐照度方程。然后由辐照度方程构造包含物体深度信息的Hamilton Jacobi偏微分方程,引入局部高阶LLF通量分裂格式和五阶WENO格式逼近微分方程的粘性解,最终得到物体表面三维形状。实验结果表明,与传统算法相比,新算法的恢复高度的最大误差和平均误差均显著降低。     

单像机有源形状恢复方法研究

物体表面三维形状恢复是计算机视觉的一个重要研究内容.给出了一种利用网格结 构光,采用单像机恢复物体表面三维形状的方法.提出了一种新的定标方法和投影模板检测 算法,并构造完成了有源三维重建实验系统.结果表明该方法能快速、准确地恢复物体形状.     

引入三维阴影及镜面反射的两步光源检测算法

现有基于光照模型的增强现实逆向光源检测算法要求校准物体的表面是纯漫反射体,检测出的位置误差较大.针对这一问题,提出一种引入投影阴影三维形状特征及镜面反射分量的两步光源检测算法.算法分为光源位置和强度检测两个部分,在光源位置已知的条件下引入带有镜面反射分量的Cook-Torrance模型检测强度,并采用随机采样分区、误差处理等机制.同时使用投影阴影三维形状特征代替平面阴影角点特征,然后利用光线跟踪检测光照位置.实验表明该算法检测光源位置更加准确,检测光强有效,较好地解决了点光源位置检测不准确和受校准物材质限制问题.     

一种基于多面体模型的从明暗恢复物体形状方法

针对目前“从明暗恢复物体形状方法”存在的问题,提出了以多面体模型为基础的快速算法,根据向量场的分布建立关于物体表面深度信息的超定线性方程组,在最小二乘意义下求得物体表面的深度值。该算法能从已知光照条件下的单幅或多幅图像中恢复物体表面的三维结构,形成以像素为精度的物体表面多面体模型。实际计算表明该算法计算速度快,能适应单一反射系数的任意物体表面的形状恢复。     

点光源照明参数估计的算法研究

针对使用朗伯反射模型描述漫反射表面的形状恢复算法存在较大误差的问题,提出了一种偏角的计算和特定方向的选择相关的点光源参数估计的从明暗恢复形状的算法.首先假定摄像机采用正交投影,方向与光源方向一致,建立适合漫反射表面的图像辐照度方程,然后将方程转化为包含物体深度信息的偏微分方程,使用点光源估计方法应用于SFS技术的线性化方法,进而得到物体表面的三维形状.合成人脸的PGM格式的图像实验结果表明,采用改进的算法成像准确,且深度信息值域比较宽,效果满足要求.     

一种基于深度和颜色的3D表面重建方法

为了快速、有效地对三维物体的表面进行重建，提出了一种基于深度和颜色的3D表面重建方法。该方法利用同一物体在空间稀疏分布的不同视点下的多幅深度图像，根据3D物体表面同一点在不同深度图像上颜色和纹理的一致性，通过逆投影变换，可将图像中像素点映射到三维空间中的正确位置。然后在以物体为中心的单个坐标系下，进行数据融合，可得到三维物体表面各点的三维数据。实验结果表明该方法具有复杂度恒定、获取图像真实感强等优点，适用于形状、结构草杂的物体重建。     

基于形态学的SFS法物体表面三维恢复*

目前采用的从明暗恢复形状（shape from shading,SFS）法存在对物体表面光滑度要求高,对噪声敏感等问题,为此,提出了一种基于数学形态学的SFS法,通过数学形态学提取图像灰度函数的峰、谷、脊、沟,鞍等形状特征,并采用球状点假设法确定物体的表面方向,恢复物体三维表面。实验表明,该方法具有更好的精度和抗噪性能。     

由二维图像对三维弹体定位的方法研究

一、引言用计算机来定位复杂的三维物体图形,一直是计算机图形学研究的一个难题,因为它受各方面因素的影响较大。常用的定位方法是从图像上获取三维数据,然后经过作图,给出物体的空间位置与形状。从图像上获取三维数据的方法通常有两种:一种方法是由透视投影获得三维数据,即将图像上的点看作是空间物体对应点经透视投影变换后的象点(要求从不同的观察点拍摄至少两幅图像),然后经过逆变换,可求出原物体上点的三维坐标数据。如文献[2]中介绍的立体测量系统就是利用此方法处理的。另一种方法是由图象的明暗来获取三维数据。当照明模型满足朗伯定律时,利用光源位置不同的三幅正投影图像(这三个光源不应在同一直线上)可以计算出各点的法向矢量,从而决定出物体的形状。     

Variational Segmentation of Image Sequences Using Region-Based Active Contours and Deformable Shape Priors

In this paper we address the problem of segmentation in image sequences using region-based active contours and level set methods. We propose a novel method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the classical Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It is expected to handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on synthetic and real image sequences.     

A Framework for Automatically Recovering Object Shape,Reflectance and Light Sources from Calibrated Images

In this paper, we present a complete framework for recovering an object shape, estimating its reflectance properties and light sources from a set of images. The whole process is performed automatically. We use the shape from silhouette approach proposed by R. Szeliski (1993) combined with image pixels for reconstructing a triangular mesh according to the marching cubes algorithm. A classification process identifies regions of the object having the same appearance. For each region, a single point or directional light source is detected. Therefore, we use specular lobes, lambertian regions of the surface or specular highlights seen on images. An identification method jointly (i) decides what light sources are actually significant and (ii) estimates diffuse and specular coefficients for a surface represented by the modified Phong model (Lewis, 1994). In order to validate our algorithm efficiency, we present a case study with various objects, light sources and surface properties. As shown in the results, our system proves accurate even for real objects images obtained with an inexpensive acquisition system.     

Face recognition under varying illumination based on a 2D face shape model

This paper proposes a novel illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images in normal lighting conditions. A simple yet effective local contrast enhancement method, namely block-based histogram equalization (BHE), is first proposed. The resulting image processed using BHE is then compared with the original face image processed using histogram equalization (HE) to estimate the category of its light source. In our scheme, we divide the light source for a human face into 65 categories. Based on the category identified, a corresponding lighting compensation model is used to reconstruct an image that will visually be under normal illumination. In order to eliminate the influence of uneven illumination while retaining the shape information about a human face, a 2D face shape model is used. Experimental results show that, with the use of principal component analysis for face recognition, the recognition rate can be improved by 53.3% to 62.6% when our proposed algorithm for lighting compensation is used.     

Shape from Shading with Interreflections Under a Proximal Light Source: Distortion-Free Copying of an Unfolded Book

We address the problem of recovering the 3D shape of an unfolded book surface from the shading information in a scanner image. This shape-from-shading problem in a real world environment is made difficult by a proximal, moving light source, interreflections, specular reflections, and a nonuniform albedo distribution. Taking all these factors into account, we formulate the problem as an iterative, non-linear optimization problem. Piecewise polynomial models of the 3D shape and albedo distribution are introduced to efficiently and stably compute the shape in practice. Finally, we propose a method to restore the distorted scanner image based on the reconstructed 3D shape. The image restoration experiments for real book surfaces demonstrate that much of the geometric and photometric distortions are removed by our method.     

Reconstructing 3D Shape,Albedo and Illumination from a Single Face Image

The morphable model has been employed to efficiently describe 3D face shape and the associated albedo with a reduced set of basis vectors. The spherical harmonics (SH) model provides a compact basis to well approximate the image appearance of a Lambertian object under different illumination conditions. Recently, the SH and morphable models have been integrated for 3D face shape reconstruction. However, the reconstructed 3D shape is either inconsistent with the SH bases or obtained just from landmarks only. In this work, we propose a geometrically consistent algorithm to reconstruct the 3D face shape and the associated albedo from a single face image iteratively by combining the morphable model and the SH model. The reconstructed 3D face geometry can uniquely determine the SH bases, therefore the optimal 3D face model can be obtained by minimizing the error between the input face image and a linear combination of the associated SH bases. In this way, we are able to preserve the consistency between the 3D geometry and the SH model, thus refining the 3D shape reconstruction recursively. Furthermore, we present a novel approach to recover the illumination condition from the estimated weighting vector for the SH bases in a constrained optimization formulation independent of the 3D geometry. Experimental results show the effectiveness and accuracy of the proposed face reconstruction and illumination estimation algorithm under different face poses and multiple‐light‐source illumination conditions.     

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.     

Incorporating illumination constraints in deformable models for shape from shading and light direction estimation

We present a method for the integration of nonlinear holonomic constraints in deformable models and its application to the problems of shape and illuminant direction estimation from shading. Experimental results demonstrate that our method performs better than previous Shape from Shading algorithms applied to images of Lambertian objects under known illumination. It is also more general as it can be applied to non-Lambertian surfaces and it does not require knowledge of the illuminant direction. In this paper, (1) we first develop a theory for the numerically robust integration of nonlinear holonomic constraints within a deformable model framework. In this formulation, we use Lagrange multipliers and a Baumgarte stabilization approach (1972). (2) We also describe a fast new method for the computation of constraint based forces, in the case of high numbers of local parameters. (3) We demonstrate how any type of illumination constraint, from the simple Lambertian model to more complex highly nonlinear models can be incorporated in a deformable model framework. (4) We extend our method to work when the direction of the light source is not known. We couple our shape estimation method with a method for light estimation, in an iterative process, where improved shape estimation results in improved light estimation and vice versa. (5) We perform a series of experiments.     

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     

Light source position and reflectance estimation from a single view without the distant illumination assumption

Several techniques have been developed for recovering reflectance properties of real surfaces under unknown illumination. However, in most cases, those techniques assume that the light sources are located at infinity, which cannot be applied safely to, for example, reflectance modeling of indoor environments. In this paper, we propose two types of methods to estimate the surface reflectance property of an object, as well as the position of a light source from a single view without the distant illumination assumption, thus relaxing the conditions in the previous methods. Given a real image and a 3D geometric model of an object with specular reflection as inputs, the first method estimates the light source position by fitting to the Lambertian diffuse component, while separating the specular and diffuse components by using an iterative relaxation scheme. Our second method extends that first method by using as input a specular component image, which is acquired by analyzing multiple polarization images taken from a single view, thus removing its constraints on the diffuse reflectance property. This method simultaneously recovers the reflectance properties and the light source positions by optimizing the linearity of a log-transformed Torrance-Sparrow model. By estimating the object's reflectance property and the light source position, we can freely generate synthetic images of the target object under arbitrary lighting conditions with not only source direction modification but also source-surface distance modification. Experimental results show the accuracy of our estimation framework.     

Optimal Algorithm for Shape from Shading and Path Planning

An optimal algorithm for the reconstruction of a surface from its shading image is presented. The algorithm solves the 3D reconstruction from a single shading image problem. The shading image is treated as a penalty function and the height of the reconstructed surface is a weighted distance. A consistent numerical scheme based on Sethian's fast marching method is used to compute the reconstructed surface. The surface is a viscosity solution of an Eikonal equation for the vertical light source case. For the oblique light source case, the reconstructed surface is the viscosity solution to a different partial differential equation. A modification of the fast marching method yields a numerically consistent, computationally optimal, and practically fast algorithm for the classical shape from shading problem. Next, the fast marching method coupled with a back tracking via gradient descent along the reconstructed surface is shown to solve the path planning problem in robot navigation.