Vignette and exposure calibration and compensation

We discuss calibration and removal of "vignetting" (radial falloff) and exposure (gain) variations from sequences of images. Even when the response curve is known, spatially varying ambiguities prevent us from recovering the vignetting, exposure, and scene radiances uniquely. However, the vignetting and exposure variations can nonetheless be removed from the images without resolving these ambiguities or the previously known scale and gamma ambiguities. Applications include panoramic image mosaics, photometry for material reconstruction, image-based rendering, and preprocessing for correlation-based vision algorithms.     

Single-Image Vignetting Correction

In this paper, we propose a method for robustly determining the vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for vignetting estimation. Within each image region, our method capitalizes on the frequency characteristics and physical properties of vignetting to distinguish it from other sources of intensity variation. Rejection of outlier pixels is applied to improve the robustness of vignetting estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images with both simulated and natural vignetting effects. Causes of failures using the proposed algorithm are also analyzed.     

Joint radiometric calibration and feature tracking system with an application to stereo

To capture the full brightness range of natural scenes, cameras automatically adjust the exposure value which causes the brightness of scene points to change from frame to frame. Given such a video sequence, we introduce a system for tracking features and estimating the radiometric response function of the camera and the exposure difference between frames simultaneously. We model the global and nonlinear process that is responsible for the changes in image brightness rather than adapting to the changes locally and linearly which makes our tracking more robust to the change in brightness. We apply our system to perform structure-from-motion and stereo to reconstruct a texture-mapped 3D surface from a video taken in a high dynamic range environment.     

Estimating Photometric Properties from Image Collections

We address the problem of jointly estimating the scene illumination, the radiometric camera calibration and the reflectance properties of an object using a set of images from a community photo collection. The highly ill-posed nature of this problem is circumvented by using appropriate representations of illumination, an empirical model for the nonlinear function that relates image irradiance with intensity values and additional assumptions on the surface reflectance properties. Using a 3D model recovered from an unstructured set of images, we estimate the coefficients that represent the illumination for each image using a frequency framework. For each image, we also compute the corresponding camera response function. Additionally, we calculate a simple model for the reflectance properties of the 3D model. A robust non-linear optimization is proposed exploiting the high sparsity present in the problem.     

Generalized Mosaicing: High Dynamic Range in a Wide Field of View

We present an approach that significantly enhances the capabilities of traditional image mosaicking. The key observation is that as a camera moves, it senses each scene point multiple times. We rigidly attach to the camera an optical filter with spatially varying properties, so that multiple measurements are obtained for each scene point under different optical settings. Fusing the data captured in the multiple images yields an image mosaic that includes additional information about the scene. We refer to this approach as generalized mosaicing. In this paper we show that this approach can significantly extend the optical dynamic range of any given imaging system by exploiting vignetting effects. We derive the optimal vignetting configuration and implement it using an external filter with spatially varying transmittance. We also derive efficient scene sampling conditions as well as ways to self calibrate the vignetting effects. Maximum likelihood is used for image registration and fusion. In an experiment we mounted such a filter on a standard 8-bit video camera, to obtain an image panorama with dynamic range comparable to imaging with a 16-bit camera.     

3D遮挡模型引导的光场图像深度获取

目的 光场相机可以通过单次曝光同时从多个视角采样单个场景,在深度估计领域具有独特优势。消除遮挡的影响是光场深度估计的难点之一。现有方法基于2D场景模型检测各视角遮挡状态,但是遮挡取决于所采样场景的3D立体模型,仅利用2D模型无法精确检测,不精确的遮挡检测结果将降低后续深度估计精度。针对这一问题,提出了3D遮挡模型引导的光场图像深度获取方法。方法 向2D模型中的不同物体之间添加前后景关系和深度差信息,得到场景的立体模型,之后在立体模型中根据光线的传输路径推断所有视角的遮挡情况并记录在遮挡图（occlusion map）中。在遮挡图引导下,在遮挡和非遮挡区域分别使用不同成本量进行深度估计。在遮挡区域,通过遮挡图屏蔽被遮挡视角,基于剩余视角的成像一致性计算深度;在非遮挡区域,根据该区域深度连续特性设计了新型离焦网格匹配成本量,相比传统成本量,该成本量能够感知更广范围的色彩纹理,以此估计更平滑的深度图。为了进一步提升深度估计的精度,根据遮挡检测和深度估计的依赖关系设计了基于最大期望（exception maximization,EM）算法的联合优化框架,在该框架下,遮挡图和深度图通过互相引导的方式相继提升彼此精度。结果 实验结果表明,本文方法在大部分实验场景中,对于单遮挡、多遮挡和低对比度遮挡在遮挡检测和深度估计方面均能达到最优结果。均方误差（mean square error,MSE）对比次优结果平均降低约19.75%。结论 针对遮挡场景的深度估计,通过理论分析和实验验证,表明3D遮挡模型相比传统2D遮挡模型在遮挡检测方面具有一定优越性,本文方法更适用于复杂遮挡场景的深度估计。     

Generalized mosaicing: polarization panorama

We present an approach to image the polarization state of object points in a wide field of view, while enhancing the radiometric dynamic range of imaging systems by generalizing image mosaicing. The approach is biologically inspired, as it emulates spatially varying polarization sensitivity of some animals. In our method, a spatially varying polarization and attenuation filter is rigidly attached to a camera. As the system moves, it senses each scene point multiple times, each time filtering it through a different filter polarizing angle, polarizance, and transmittance. Polarization is an additional dimension of the generalized mosaicing paradigm, which has recently yielded high dynamic range images and multispectral images in a wide field of view using other kinds of filters. The image acquisition is as easy as in traditional image mosaics. The computational algorithm can easily handle nonideal polarization filters (partial polarizers), variable exposures, and saturation in a single framework. The resulting mosaic represents the polarization state at each scene point. Using data acquired by this method, we demonstrate attenuation and enhancement of specular reflections and semi reflection separation in an image mosaic.     

Three-dimensional image mosaicking using multiple projection planes for 3-D visualization of roadside standing buildings.

A novel image-mosaicking technique suitable for 3-D visualization of roadside buildings on websites or mobile systems is proposed. Our method was tested on a roadside building scene taken using a side-looking video camera employing a continuous set of vertical-textured planar faces. A vertical plane approximation of the scene geometry for each frame was calculated using sparsely distributed feature points that were assigned 3-D data through bundle adjustments. These vertical planes were concatenated to create an approximate model on which the images could be backprojected as textures and blended together. Additionally, our proposed method includes an expanded crossed-slits projection around far-range areas to reduce the "ghost effect," a phenomenon in which a particular object appears repeatedly in a created image mosaic. The final step was to produce seamless image mosaics using Dijkstra's algorithm to find the optimum seam line to blend overlapping images. We used our algorithm to create efficient image mosaics in 3-D space from a sequence of real images.     

外极面图像的运动遮挡模型和运动纹理方向检测算法

运动遮挡边界处的运动估计是一种困难的问题,外极面图像方法将运动估计转化为转迹线的检测,人造物体的轨迹线容易通过边缘跟踪的方法获得,但对于纹理复杂的自然景物,轨迹跟踪较为困难。     

Revised definition of optical flow: integration of radiometric andgeometric cues for dynamic scene analysis

Optical flow has been commonly defined as the apparent motion of image brightness patterns in an image sequence. In this paper, we propose a revised definition to overcome shortcomings in interpreting optical flow merely as a geometric transformation field. The new definition is a complete representation of geometric and radiometric variations in dynamic imagery. We argue that this is more consistent with the common interpretation of optical flow induced by various scene events. This leads to a general framework for the investigation of problems in dynamic scene analysis, based on the integration and unified treatment of both geometric and radiometric cues in time-varying imagery. We discuss selected models, including the generalized dynamic image model, for the estimation of optical flow. We show how various 3D scene information are encoded in, and thus may be extracted from, the geometric and radiometric components of optical flow. We provide selected examples based on experiments with real images     

Generalized mosaicing: wide field of view multispectral imaging

We present an approach to significantly enhance the spectral resolution of imaging systems by generalizing image mosaicing. A filter transmitting spatially varying spectral bands is rigidly attached to a camera. As the system moves, it senses each scene point multiple times, each time in a different spectral band. This is an additional dimension of the generalized mosaic paradigm, which has demonstrated yielding high radiometric dynamic range images in a wide field of view, using a spatially varying density filter. The resulting mosaic represents the spectrum at each scene point. The image acquisition is as easy as in traditional image mosaics. We derive an efficient scene sampling rate, and use a registration method that accommodates the spatially varying properties of the filter. Using the data acquired by this method, we demonstrate scene rendering under different simulated illumination spectra. We are also able to infer information about the scene illumination. The approach was tested using a standard 8-bit black/white video camera and a fixed spatially varying spectral (interference) filter.     

A Shape‐Aware Model for Discrete Texture Synthesis

We present a novel shape‐aware method for synthesizing 2D and 3D discrete element textures consisting of collections of distinct vector graphics objects. Extending the long‐proven point process framework, we propose a shape process, a novel stochastic model based on spatial measurements that fully take into account the geometry of the elements. We demonstrate that our approach is well‐suited for discrete texture synthesis by example. Our model enables for both robust statistical parameter estimation and reliable output generation by Monte Carlo sampling. Our numerous experiments show that contrary to current state‐of‐the‐art techniques, our algorithm manages to capture anisotropic element distributions and systematically prevents undesirable collisions between objects.     

Robust multiscale stereo matching from fundus images with radiometric differences

A robust multiscale stereo matching algorithm is proposed to find reliable correspondences between low contrast and weakly textured retinal image pairs with radiometric differences. Existing algorithms designed to deal with piecewise planar surfaces with distinct features and Lambertian reflectance do not apply in applications such as 3D reconstruction of medical images including stereo retinal images. In this paper, robust pixel feature vectors are formulated to extract discriminative features in the presence of noise in scale space, through which the response of low-frequency mechanisms alter and interact with the response of high-frequency mechanisms. The deep structures of the scene are represented with the evolution of disparity estimates in scale space, which distributes the matching ambiguity along the scale dimension to obtain globally coherent reconstructions. The performance is verified both qualitatively by face validity and quantitatively on our collection of stereo fundus image sets with ground truth, which have been made publicly available as an extension of standard test images for performance evaluation.     

N-dimensional tensor voting and application to epipolar geometryestimation

We address the problem of epipolar geometry estimation by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two images of a static scene without correspondences, even in the presence of moving objects, our method extracts good matches and rejects outliers. The methodology is novel and unconventional, since, unlike most other methods optimizing certain scalar, objective functions, our approach does not involve initialization or any iterative search in the parameter space. Therefore, it is free of the problem of local optima or poor convergence. Further, since no search is involved, it is unnecessary to impose simplifying assumption to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by a computation scheme, 8D tensor voting, which is an instance of the more general N-dimensional tensor voting framework. In essence, the input set of matches is first transformed into a sparse 8D point set. Dense, 8D tensor kernels are then used to vote for the most salient hyperplane that captures all inliers inherent in the input. With this filtered set of matches, the normalized eight-point algorithm can be used to estimate the fundamental matrix accurately. By making use of efficient data structure and locality, our method is both time and space efficient despite the higher dimensionality. We demonstrate the general usefulness of our method using example image pairs for aerial image analysis, with widely different views, and from nonstatic 3D scenes. Each example contains a considerable number of wrong matches     

A hierarchy of cameras for 3D photography

The view-independent visualization of 3D scenes is most often based on rendering accurate 3D models or utilizes image-based rendering techniques. To compute the 3D structure of a scene from a moving vision sensor or to use image-based rendering approaches, we need to be able to estimate the motion of the sensor from the recorded image information with high accuracy, a problem that has been well-studied. In this work, we investigate the relationship between camera design and our ability to perform accurate 3D photography, by examining the influence of camera design on the estimation of the motion and structure of a scene from video data. By relating the differential structure of the time varying plenoptic function to different known and new camera designs, we can establish a hierarchy of cameras based upon the stability and complexity of the computations necessary to estimate structure and motion. At the low end of this hierarchy is the standard planar pinhole camera for which the structure from motion problem is non-linear and ill-posed. At the high end is a camera, which we call the full field of view polydioptric camera, for which the motion estimation problem can be solved independently of the depth of the scene which leads to fast and robust algorithms for 3D Photography. In between are multiple view cameras with a large field of view which we have built, as well as omni-directional sensors.     

抗高光的光场深度估计方法

目的 光场相机一次成像可以同时获取场景中光线的空间和角度信息,为深度估计提供了条件。然而,光场图像场景中出现高光现象使得深度估计变得困难。为了提高算法处理高光问题的可靠性,本文提出了一种基于光场图像多视角上下文信息的抗高光深度估计方法。方法 本文利用光场子孔径图像的多视角特性,创建多视角输入支路,获取不同视角下图像的特征信息;利用空洞卷积增大网络感受野,获取更大范围的图像上下文信息,通过同一深度平面未发生高光的区域的深度信息,进而恢复高光区域深度信息。同时,本文设计了一种新型的多尺度特征融合方法,串联多膨胀率空洞卷积特征与多卷积核普通卷积特征,进一步提高了估计结果的精度和平滑度。结果 实验在3个数据集上与最新的4种方法进行了比较。实验结果表明,本文方法整体深度估计性能较好,在4D light field benchmark合成数据集上,相比于性能第2的模型,均方误差（mean square error,MSE）降低了20.24%,坏像素率（bad pixel,BP）降低了2.62%,峰值信噪比（peak signal-to-noise ratio,PSNR）提高了4.96%。同时,通过对CVIA （computer vision and image analysis） Konstanz specular dataset合成数据集和Lytro Illum拍摄的真实场景数据集的定性分析,验证了本文算法的有效性和可靠性。消融实验结果表明多尺度特征融合方法改善了深度估计在高光区域的效果。结论 本文提出的深度估计模型能够有效估计图像深度信息。特别地,高光区域深度信息恢复精度高、物体边缘区域平滑,能够较好地保存图像细节信息。     

Stereoscopic Scene Flow Computation for 3D Motion Understanding

Building upon recent developments in optical flow and stereo matching estimation, we propose a variational framework for the estimation of stereoscopic scene flow, i.e., the motion of points in the three-dimensional world from stereo image sequences. The proposed algorithm takes into account image pairs from two consecutive times and computes both depth and a 3D motion vector associated with each point in the image. In contrast to previous works, we partially decouple the depth estimation from the motion estimation, which has many practical advantages. The variational formulation is quite flexible and can handle both sparse or dense disparity maps. The proposed method is very efficient; with the depth map being computed on an FPGA, and the scene flow computed on the GPU, the proposed algorithm runs at frame rates of 20 frames per second on QVGA images (320×240 pixels). Furthermore, we present solutions to two important problems in scene flow estimation: violations of intensity consistency between input images, and the uncertainty measures for the scene flow result.     

Free-form object reconstruction from silhouettes, occluding edges and texture edges: a unified and robust operator based on duality

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, i.e., each tangent plane of the primal surface is represented as a four-dimensional vector which constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, i.e., the dual of the dual goes back to the primal. This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data, by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences.     

Structure from Motion: Beyond the Epipolar Constraint

The classic approach to structure from motion entails a clear separation between motion estimation and structure estimation and between two-dimensional (2D) and three-dimensional (3D) information. For the recovery of the rigid transformation between different views only 2D image measurements are used. To have available enough information, most existing techniques are based on the intermediate computation of optical flow which, however, poses a problem at the locations of depth discontinuities. If we knew where depth discontinuities were, we could (using a multitude of approaches based on smoothness constraints) accurately estimate flow values for image patches corresponding to smooth scene patches; but to know the discontinuities requires solving the structure from motion problem first. This paper introduces a novel approach to structure from motion which addresses the processes of smoothing, 3D motion and structure estimation in a synergistic manner. It provides an algorithm for estimating the transformation between two views obtained by either a calibrated or uncalibrated camera. The results of the estimation are then utilized to perform a reconstruction of the scene from a short sequence of images.The technique is based on constraints on image derivatives which involve the 3D motion and shape of the scene, leading to a geometric and statistical estimation problem. The interaction between 3D motion and shape allows us to estimate the 3D motion while at the same time segmenting the scene. If we use a wrong 3D motion estimate to compute depth, we obtain a distorted version of the depth function. The distortion, however, is such that the worse the motion estimate, the more likely we are to obtain depth estimates that vary locally more than the correct ones. Since local variability of depth is due either to the existence of a discontinuity or to a wrong 3D motion estimate, being able to differentiate between these two cases provides the correct motion, which yields the least varying estimated depth as well as the image locations of scene discontinuities. We analyze the new constraints, show their relationship to the minimization of the epipolar constraint, and present experimental results using real image sequences that indicate the robustness of the method.     

基于在线光度标定的单目直接视觉SLAM

为了减少场景点的光照变化对直接视觉SLAM （simultaneous localization and mapping,SLAM）的影响,在稀疏直接法（direct sparse odometry with loop closure,LDSO）的基础上对其优化,结合光度标定来增强系统的整体性能。直接视觉SLAM,其图像配对的基本假设是建立在灰度一致性之上的,即同一个场景点在多个图像中是以恒定亮度值出现的。为了更好地满足这一假设,采用KLT （Kanade-Lucas-Tracker）来跟踪关键点,利用带光度参数的像素跟踪模型建立优化方程,优化序列的光度参数（曝光时间、晕影和响应函数）,从而实现实时曝光补偿,增强了系统视觉前端的跟踪稳定性。最后,在公开数据集上进行实验。实验结果标明所提方法在部分序列上能有效地提高系统性能。