Omnistereo: panoramic stereo imaging

An omnistereo panorama consists of a pair of panoramic images, where one panorama is for the left eye and another panorama is for the right eye. The panoramic stereo pair provides a stereo sensation up to a full 360 degrees. Omnistereo panoramas can be constructed by mosaicing images from a single rotating camera. This approach also enables the control of stereo disparity, giving larger baselines for faraway scenes, and a smaller baseline for closer scenes. Capturing panoramic omnistereo images with a rotating camera makes it impossible to capture dynamic scenes at video rates and limits omnistereo imaging to stationary scenes. We present two possibilities for capturing omnistereo panoramas using optics without any moving parts. A special mirror is introduced such that viewing the scene through this mirror creates the same rays as those used with the rotating cameras. The lens used for omnistereo panorama is also introduced, together with the design of the mirror. Omnistereo panoramas can also be rendered by computer graphics methods to represent virtual environments     

Handling occlusions in video‐based augmented reality using depth information

Augmented Reality (AR) composes virtual objects with real scenes in a mixed environment where human–computer interaction has more semantic meanings. To seamlessly merge virtual objects with real scenes, correct occlusion handling is a significant challenge. We present an approach to separate occluded objects in multiple layers by utilizing depth, color, and neighborhood information. Scene depth is obtained by stereo cameras and two Gaussian local kernels are used to represent color, spatial smoothness. These three cues are intelligently fused in a probability framework, where the occlusion information can be safely estimated. We apply our method to handle occlusions in video‐based AR where virtual objects are simply overlapped on real scenes. Experiment results show the approach can correctly register virtual and real objects in different depth layers, and provide a spatial‐awareness interaction environment. Copyright © 2009 John Wiley & Sons, Ltd.     

Depth Discontinuities by Pixel-to-Pixel Stereo

An algorithm to detect depth discontinuities from a stereo pair of images is presented. The algorithm matches individual pixels in corresponding scanline pairs, while allowing occluded pixels to remain unmatched, then propagates the information between scanlines by means of a fast postprocessor. The algorithm handles large untextured regions, uses a measure of pixel dissimilarity that is insensitive to image sampling, and prunes bad search nodes to increase the speed of dynamic programming. The computation is relatively fast, taking about 600 nanoseconds per pixel per disparity on a personal computer. Approximate disparity maps and precise depth discontinuities (along both horizontal and vertical boundaries) are shown for several stereo image pairs containing textured, untextured, fronto-parallel, and slanted objects in indoor and outdoor scenes.     

全向立体成像系统场景深度估计

基于单相机双抛物面反射镜设计了一种共轴结构的折反射全向立体成像装置,给出了针对相应展开柱面全景立体图像对深度估计的应点匹配方法,最后通过3D Max构造相应虚拟装置和虚拟场景进行了仿真实验,初步证明了该结构设计和对应深度估计方法的有效性。     

A parallel stereo algorithm that produces dense depth maps and preserves image features

To compute reliable dense depth maps, a stereo algorithm must preserve depth discontinuities and avoid gross errors. In this paper, we show how simple and parallel techniques can be combined to achieve this goal and deal with complex real world scenes. Our algorithm relies on correlation followed by interpolation. During the correlation phase the two images play a symmetric role and we use a validity criterion for the matches that eliminate gross errors: at places where the images cannot be correlated reliably, due to lack of texture of occlusions for example, the algorithm does not produce wrong matches but a very sparse disparity map as opposed to a dense one when the correlation is successful. To generate a dense depth map, the information is then propagated across the featureless areas, but not across discontinuities, by an interpolation scheme that takes image grey levels into account to preserve image features. We show that our algorithm performs very well on difficult images such as faces and cluttered ground level scenes. Because all the algorithms described here are parallel and very regular they could be implemented in hardware and lead to extremely fast stereo systems.This research was supported in part under the Centre National d'Etudes Spatiales VAP contract and in part under a Defence Advanced Research Projects Agency contract at SRI     

A LSS-based registration of stereo thermal–visible videos of multiple people using belief propagation

In this paper, we propose a novel stereo method for registering foreground objects in a pair of thermal and visible videos of close-range scenes. In our stereo matching, we use Local Self-Similarity (LSS) as similarity metric between thermal and visible images. In order to accurately assign disparities to depth discontinuities and occluded Region Of Interest (ROI), we have integrated color and motion cues as soft constraints in an energy minimization framework. The optimal disparity map is approximated for image ROIs using a Belief Propagation (BP) algorithm. We tested our registration method on several challenging close-range indoor video frames of multiple people at different depths, with different clothing, and different poses. We show that our global optimization algorithm significantly outperforms the existing state-of-the art method, especially for disparity assignment of occluded people at different depth in close-range surveillance scenes and for relatively large camera baseline.     

立体画的原理与生成技术

立体画是立体视觉技术与计算机技术相结合的产物。立体画的基本原理就是“墙纸效应”。立体视感是人的左右两眼视图交叠后通过大脑形成的立体虚象。同一幅立体画可以看出多个立体机感，分为交叉眼立体视感和非交叉眼立体视感。立体画分为两大类：基于模式的立体画和随机点立体画。在改进Ｔｙｌｅｒ算法和Ｔｈｉｍｂｌｅｂｙ模型的基础上，本文实现了一个Ｗｉｎｄｏｗｓ环境下的立体画生成系统──ＷｉｎＳｔｅｒｅｏ。在ＷｉｎＳｔｅｒｅｏ系统中，着重解决了“平面分层”现象，并提出了一种新的立体画──双向立体画及其生成算法。     

Reconstructing a 3-D depth map from one or more images

Several algorithms are suggested for recovering depth and orientation maps of a surface from its image intensities. They combine the advantages of stereo vision and shape-from-shading (SFS) methods. These algorithms generate accurate, unambiguous and dense surface depth and orientation maps. Most of the existing SFS algorithms cannot be directly extended to combine stereo images because the recovery of surface depth and that of orientation are separated in these formulations. We first present an SFS algorithm that couples the generation of depth and orientation maps. This formulation also ensures that the reconstructed surface depth and its orientation are consistent. The SFS algorithm for a single image is then extended to utilize stereo images. The correspondence over stereo images is established simultaneously with the generation of surface depth and orientation. An alternative approach is also suggested for combining stereo and SFS techniques. This approach can be used to combine needle maps which are directly available from other sources such as photometric stereo. Finally we present an algorithm to combine sparse depth measurements with an orientation map to reconstruct a surface. The same algorithm can be combined with the above algorithms for solving the SFS problem with sparse depth measurements. Thus various information sources can be used to accurately reconstruct a surface.     

Spacetime stereo: a unifying framework for depth from triangulation

Depth from triangulation has traditionally been investigated in a number of independent threads of research, with methods such as stereo, laser scanning, and coded structured light considered separately. We propose a common framework called spacetime stereo that unifies and generalizes many of these previous methods. To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.     

Time‐critical rendering algorithm with incorporation of LoD,visibility culling and object impostor

Given enough CPU time, present graphics technology can render near‐photorealistic images. However, for real‐time graphics applications such as virtual reality systems, developers must make explicit programming decisions, trading off rendering quality for interactive update rates. In this papar we present a new algorithm for rendering complex 3D models at near‐interactive rates that can be used in virtual environments composed of static or dynamic scenes. The algorithm integretes the techniques of level of details (LoD), visibility computation and object impostor. The method is more suitable for very dynamic scenes with high depth complexity. We introduce a new criterion to identify the occluder and the occludee: the object that can be replaced by its LoD model and the one that can be replaced by its impostor. The efficiency of our algorithm is then illustrated by experimental results. Copyright © 2003 John Wiley & Sons, Ltd.     

近距离3D人机交互技术研究实现

3D人机交互技术是计算机图形学、虚拟现实和模式识别的交叉融合领域,可分为虚拟环境的显示和三维物体识别。该研究将虚拟环境显示和三维物体识别整合成一个完整的解决方案并应用到1∶1模拟虚拟场景的近距离交互。研究了虚拟现实之间的坐标转换;分析了影响虚拟物体立体显示的三个主要因素：OpenGL中摄像机的张角,摄像机间距和立体图像对的产生;并实现了基于Intel Perceptual Computing的三维物体识别。实验结果显示：该方案在1∶1模拟虚拟场景方面具有良好的3D显示效果,同时在手势识别方面有较高的识别率。     

Combinatorial photometric stereo and its application in 3D modeling of melanoma

This article concerns a new type of photometric stereo algorithm for which outliers such as highlights and shadows, including attached and cast shadow, are mixed with Lambertian data. The underlying motivation behind this algorithm is very simple: an axial symmetrical setup in 6-light photometric stereo can be used to offer advantages. We investigate why an axial symmetrical setup is useful and how it can be used to improve standard photometric stereo. The main result is summarized as a combinatorial photometric stereo algorithm which embeds a non-Lambertian detection procedure. To apply this algorithm, it involves three steps. First, it combines a group of reflectance intensities to make five virtual images, whose equivalence is guaranteed due to the axial symmetrical setup of the 6-source photometric stereo system. Second, comparison between these virtual images generates a five by five skew-symmetric matrix. The Frobenius norm of this matrix is then employed as an index to determine whether there is a non-Lambertian pixel present among the six pixels. Finally, after identification of non-Lambertian pixels, standard photometric stereo is performed to realize 3D modeling. Validation of this algorithm has been conducted with both synthetic and real images. The real images were obtained from a newly designed 3D imaging device, the Skin Analyzer, for clinical inspection of melanoma. Experimental study shows that combinatorial photometric stereo gives promising results in suppressing shadows and highlights, while improving 3D reconstruction results. Furthermore, error analysis illustrates how to determine an appropriate threshold value to enable the algorithm to achieve optimal performance.     

Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming

A new algorithm is proposed for efficient stereo and novel view synthesis. Given the video streams acquired by two synchronized cameras the proposed algorithm synthesises images from a virtual camera in arbitrary position near the physical cameras. The new technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel view generation. The two main contributions of this paper are: (i) a new four state matching graph for dense stereo dynamic programming, that supports accurate occlusion labelling; (ii) a compact geometric derivation for novel view synthesis by direct projection of the minimum cost surface. Furthermore, the paper presents an algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly in the three-dimensional matching cost space. The proposed algorithm has been designed to work with input images with large disparity range, a common practical situation. The enhanced occlusion handling capabilities of the new dynamic programming algorithm are evaluated against those of the most powerful state-of-the-art dynamic programming and graph-cut techniques. Four-state DP is also evaluated against the disparity-based Middlebury error metrics and its performance found to be amongst the best of the efficient algorithms. A number of examples demonstrate the robustness of four-state DP to artefacts in stereo video streams. This includes demonstrations of cyclopean view synthesis in extended conversational sequences, synthesis from a freely translating virtual camera and, finally, basic 3D scene editing.     

一种测量高光物体的双目Helmholtz立体视觉方法

提出了一种基于迭代动态规划的双目Helmholtz立体视觉算法,并将其应用于高光物体的测量。算法首先对获取的Helmholtz图像对作图像校正,然后确定扫描线的端点,最后设计了一种迭代动态规划方法建立匹配获取视差图,从而恢复出高光物体的表面深度。实验采用光线跟踪方法获取带有高光的双目Helmholtz图像,视差图结果表明该方法能够有效地恢复出高光物体的深度信息。     

3-D Scene Data Recovery Using Omnidirectional Multibaseline Stereo

A traditional approach to extracting geometric information from a large scene is to compute multiple 3-D depth maps from stereo pairs or direct range finders, and then to merge the 3-D data. However, the resulting merged depth maps may be subject to merging errors if the relative poses between depth maps are not known exactly. In addition, the 3-D data may also have to be resampled before merging, which adds additional complexity and potential sources of errors.This paper provides a means of directly extracting 3-D data covering a very wide field of view, thus by-passing the need for numerous depth map merging. In our work, cylindrical images are first composited from sequences of images taken while the camera is rotated 360° about a vertical axis. By taking such image panoramas at different camera locations, we can recover 3-D data of the scene using a set of simple techniques: feature tracking, an 8-point structure from motion algorithm, and multibaseline stereo. We also investigate the effect of median filtering on the recovered 3-D point distributions, and show the results of our approach applied to both synthetic and real scenes.     

结合SURF算子和极线约束的柑橘立体图像对匹配

提出一种结合SURF算子和极线约束的立体匹配方法。对采集的双目视觉柑橘图像进行R-B分量的计算,在该分量上,采用快速hessian检测子进行特征点检测,并使用SURF描述子对检测到的特征点进行64维的特征描述。采用欧式距离和极线约束进行特征点匹配。实验表明,该方法对一幅图像对的平均处理时间为293ms,在果实被遮挡或光线变化的情况下均能较好地进行特征点提取和匹配。该方法为后续的深度信息计算提供了基础。     

An algorithm for the visualization of stereo images simultaneously captured with different exposures

The visualization of stereo images obtained from two eyepiece cameras of a stereo microscope with different exposures is studied. This problem is solved to improve the quality of resulting images in the case when one image is not sufficient for capturing an object with the desired color reproduction accuracy and high level of detail both in dark and light regions. An approach to solving this problem in which differently exposed images are split between two views is considered. This approach allows us to significantly reduce the capturing time and to enhance the quality of capturing moving objects. The algorithm described in [1] is used as the basic algorithm; the main steps of this algorithm are the stereo matching of two input images and the construction of high dynamic range images. Modifications of the basic algorithm that use different stereo matching techniques are proposed. The application of the algorithm described in [2] for the visualization of stereo images without constructing high dynamic range images is discussed. A database of images captured with different exposures by a stereo microscope is created. The quality of algorithms applied to the images from this database is evaluated in the HDR-VDP-2.2 metric [3].     

立体显示中双目视差模型和深度感知研究

为在平面幕上建立立体视觉,通过模拟真实世界中人眼获取图像过程,介绍计算机绘制双眼视差图像的方法。给出虚拟场景中 2种放置双眼视见体的模型,通过投影分析可以得出内束模型会引入垂直视差,且水平视差不恒定,从而引起同一深度面的物体被感知深度变形。离轴模型符合人眼建立立体视觉的习惯,可以正确反映出物理屏幕前后不同深度的物体,且比单眼图像的视野范围大。     

Realtime Object-aware Monocular Depth Estimation in Onboard Systems

This paper proposes the object depth estimation in real-time, using only a monocular camera in an onboard computer with a low-cost GPU. Our algorithm estimates scene depth from a sparse feature-based visual odometry algorithm and detects/tracks objects’ bounding box by utilizing the existing object detection algorithm in parallel. Both algorithms share their results, i.e., feature, motion, and bounding boxes, to handle static and dynamic objects in the scene. We validate the scene depth accuracy of sparse features with KITTI and its ground-truth depth map made from LiDAR observations quantitatively, and the depth of detected object with the Hyundai driving datasets and satellite maps qualitatively. We compare the depth map of our algorithm with the result of (un-) supervised monocular depth estimation algorithms. The validation shows that our performance is comparable to that of monocular depth estimation algorithms which train depth indirectly (or directly) from stereo image pairs (or depth image), and better than that of algorithms trained with monocular images only, in terms of the error and the accuracy. Also, we confirm that our computational load is much lighter than the learning-based methods, while showing comparable performance.