Advances in computational stereo

Extraction of three-dimensional structure of a scene from stereo images is a problem that has been studied by the computer vision community for decades. Early work focused on the fundamentals of image correspondence and stereo geometry. Stereo research has matured significantly throughout the years and many advances in computational stereo continue to be made, allowing stereo to be applied to new and more demanding problems. We review recent advances in computational stereo, focusing primarily on three important topics: correspondence methods, methods for occlusion, and real-time implementations. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses and we make suggestions for analyses yet to be done.     

A Roadmap to the Integration of Early Visual Modules

By examining the problem of image correspondence (binocular stereo and optical flow) and its relationship with other modules such as segmentation, shape and depth estimation, occlusion detection, and local signal processing, we argue that early visual modules are entangled in chicken-and-egg relationships, and unraveling these necessitates a compositional approach. In this paper, we present compositional algorithms which can match images containing slanted surfaces and images having different contrast, while simultaneously solving other problems as part of the same process. Ultimately, our goal is to motivate the application of the compositional approach to unify many other early visual modules. Experimental results have been presented on a large variety of stereo and motion images, including images with contrast mismatch and images containing untextured slanted surfaces.     

A wavelet-based multiresolution approach to solve the stereo correspondence problem using mutual information.

In this correspondence, we propose a wavelet-based hierarchical approach using mutual information (MI) to solve the correspondence problem in stereo vision. The correspondence problem involves identifying corresponding pixels between images of a given stereo pair. This results in a disparity map, which is required to extract depth information of the relevant scene. Until recently, mostly correlation-based methods have been used to solve the correspondence problem. However, the performance of correlation-based methods degrades significantly when there is a change in illumination between the two images of the stereo pair. Recent studies indicate MI to be a more robust stereo matching metric for images affected by such radiometric distortions. In this short correspondence paper, we compare the performances of MI and correlation-based metrics for different types of illumination changes between stereo images. MI, as a statistical metric, is computationally more expensive. We propose a wavelet-based hierarchical technique to counter the increase in computational cost and show its effectiveness in stereo matching.     

Accumulation of object representations utilising interaction of robot action and perception

We introduce a robotic-vision system which is able to extract object representations autonomously utilising a tight interaction of visual perception and robotic action within a perception action cycle [Ecological Psychology 4 (1992) 121; Algebraic Frames for the Perception and Action Cycle, 1997, 1]. Controlled movement of the object grasped by the robot enables us to compute the transformations of entities which are used to represent aspects of objects and to find correspondences of entities within an image sequence.A general accumulation scheme allows to acquire robust information from partly missing information extracted from single frames of an image sequence. Here we use this scheme with a preprocessing stage in which 3D-line segments are extracted from stereo images. However, the accumulation scheme can be used with any kind of preprocessing as long as the entities used to represent objects can be brought to correspondence by certain equivalence relations such as ‘rigid body motion’.We show that an accumulated representation can be applied within a tracking algorithm. The accumulation scheme is an important module of a vision based robot system on which we are currently working. In this system, objects are planned to be represented by different visual and tactile entities. The object representations are going to be learned autonomously. We discuss the accumulation scheme in the context of this project.     

Map building through pseudo dense scan matching using visual sonar data

This paper presents a novel approach to the vision based grid map building and localization problem that works in a complex indoor environment with a single forward viewing camera. Most existing visual SLAM has been limited to the feature-based method and only a few researchers have proposed visual SLAM methods for building a grid map using a stereo vision system which has not been popular in practical application. In this paper, we estimate the planar depth by applying a simple visual sonar ranging technique to the single camera image and then associating sequential scans through our own pseudo dense adaptive scan matching algorithm reducing the processing time compared to the standard point-to-point correspondence based algorithm and finally produce a grid map. To this end, we construct a Pseudo Dense Scan (PDS) which is an odometry based temporal accumulation of the visual sonar readings emulating omni-directional sensing in order to overcome the sparseness of the visual sonar. Moreover, in order to obtain a much more refined map, we further correct the slight trajectory error incurred in the PDS construction step using Sequential Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that our method can obtain an accurate grid map using a single camera without the need for a high price range sensors or stereo camera.

Selective rendering for efficient ray traced stereoscopic images

Depth-related visual effects are a key feature of many virtual environments. In stereo-based systems, the depth effect can be produced by delivering frames of disparate image pairs, while in monocular environments, the viewer has to extract this depth information from a single image by examining details such as perspective and shadows. This paper investigates via a number of psychophysical experiments, whether we can reduce computational effort and still achieve perceptually high-quality rendering for stereo imagery. We examined selectively rendering the image pairs by exploiting the fusing capability and depth perception underlying human stereo vision. In ray-tracing-based global illumination systems, a higher image resolution introduces more computation to the rendering process since many more rays need to be traced. We first investigated whether we could utilise the human binocular fusing ability and significantly reduce the resolution of one of the image pairs and yet retain a high perceptual quality under stereo viewing condition. Secondly, we evaluated subjects’ performance on a specific visual task that required accurate depth perception. We found that subjects required far fewer rendered depth cues in the stereo viewing environment to perform the task well. Avoiding rendering these detailed cues saved significant computational time. In fact it was possible to achieve a better task performance in the stereo viewing condition at a combined rendering time for the image pairs less than that required for the single monocular image. The outcome of this study suggests that we can produce more efficient stereo images for depth-related visual tasks by selective rendering and exploiting inherent features of human stereo vision.     

Scanning electron microscope-based stereo analysis

In this paper we present a novel technique to analyze stereo images generated from a SEM. The two main features of this technique are that it uses a binary linear programming approach to set up and solve the correspondence problem and that it uses constraints based on the physics of SEM image formation. Binary linear programming is a powerful tool with which to tackle constrained optimization problems, especially in cases that involve matching between one data set and another. We have also analyzed the process of SEM image formation, and present constraints that are useful in solving the stereo correspondence problem. This technique has been tested on many images. Results for a few wafers are included here.     

Robust active stereo vision using Kullback-Leibler divergence

Active stereo vision is a method of 3D surface scanning involving the projecting and capturing of a series of light patterns where depth is derived from correspondences between the observed and projected patterns. In contrast, passive stereo vision reveals depth through correspondences between textured images from two or more cameras. By employing a projector, active stereo vision systems find correspondences between two or more cameras, without ambiguity, independent of object texture. In this paper, we present a hybrid 3D reconstruction framework that supplements projected pattern correspondence matching with texture information. The proposed scheme consists of using projected pattern data to derive initial correspondences across cameras and then using texture data to eliminate ambiguities. Pattern modulation data are then used to estimate error models from which Kullback-Leibler divergence refinement is applied to reduce misregistration errors. Using only a small number of patterns, the presented approach reduces measurement errors versus traditional structured light and phase matching methodologies while being insensitive to gamma distortion, projector flickering, and secondary reflections. Experimental results demonstrate these advantages in terms of enhanced 3D reconstruction performance in the presence of noise, deterministic distortions, and conditions of texture and depth contrast.     

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.     

立体视频传输与再现研究

在分析人的立体视觉原理基础上,设计了一种立体视频传输方案:用两个摄像头代替人的双眼采集远端场景,采用RTP传输两路视频,为了确保两路视频同步,提出了时间戳匹配算法.最后介绍了使用头盔显示器立体显示方法.     

Rectified catadioptric stereo sensors

It has been shown elsewhere how mirrors can be used to capture stereo images with a single camera, an approach termed catadioptric stereo. We present novel catadioptric sensors that use mirrors to produce rectified stereo images. The scanline correspondence of these images benefits real-time stereo by avoiding the computational cost and image degradation due to resampling when rectification is performed after image capture. First, we develop a theory which determines the number of mirrors that must be used and the constraints on those mirrors that must be satisfied to obtain rectified stereo images with a single camera. Then, we discuss in detail the use of both one and three mirrors. In addition, we show how the mirrors should be placed in order to minimize sensor size for a given baseline, an important design consideration. In order to understand the feasibility of building these sensors, we analyze rectification errors due to misplacement of the camera with respect to the mirrors     

HoloTabletop: an anamorphic illusion interactive holographic-like tabletop system

HoloTabletop is a low-cost holographic-like tabletop interactive system. This system analyzes user’s head position and gaze location in a real time setting and computes the corresponding anamorphic illusion image. The anamorphic illusion image is displayed on a 2D horizontally-located monitor, yet offers stereo vision to the user. The user is able to view and interact with the 3D virtual objects without wearing any special glasses or devices. The experimental results and user studies verify that the proposed HoloTabletop system offers excellent stereo vision while no visual fatigue will be caused to human eyes. This system is a great solution for many interactive applications such as 3D board games and stereo map browsing.     

Absolute motion and structure from stereo image sequences without stereo correspondence and analysis of degenerate cases

This paper deals with the estimation of motion and structure with an absolute scale factor from stereo image sequences without stereo correspondence. We show that the absolute motion and structure can be determined using only motion correspondences. This property is very useful in two aspects: first, motion correspondence is easier to solve than stereo correspondence because sequences of images can be taken at short time intervals; second, it is not necessary that the rigid scene be included in the intersection of the field of view of the two cameras. It is also shown that the degenerate cases reported in this paper constitute all of the degenerate cases for the scheme and can be easily avoided.     

Evaluation of correspondence errors for stereo

The computation of a scalar correspondence error is the fundamental step in most stereo correspondence algorithms. The quality of the results obtained by the reconstruction algorithm directly depends on the characteristics of such error. We have developed a procedure to evaluate different methods proposed for the computation of the correspondence error. The evaluation is based on exploring the shape of the error surface generated and testing it for uniqueness, isolation and compatibility. The scheme presented makes it possible to recognise the known characteristics of the tested methods for the computation of a correspondence error from the results of the evaluations. Our results show that, for the tested scenes, the evaluation scheme allows us to identify the most appropriate method to compute the correspondence error.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

A stereo matching approach based on particle filters and scattered control landmarks

In robot localization, particle filtering can estimate the position of a robot in a known environment with the help of sensor data. In this paper, we present an approach based on particle filtering, for accurate stereo matching. The proposed method consists of three parts. First, we utilize multiple disparity maps in order to acquire a very distinctive set of features called landmarks, and then we use segmentation as a grouping technique. Secondly, we apply scan line particle filtering using the corresponding landmarks as a virtual sensor data to estimate the best disparity value. Lastly, we reduce the computational redundancy of particle filtering in our stereo correspondence with a Markov chain model, given the previous scan line values. More precisely, we assist particle filtering convergence by adding a proportional weight in the predicted disparity value estimated by Markov chains. In addition to this, we optimize our results by applying a plane fitting algorithm along with a histogram technique to refine any outliers. This work provides new insights into stereo matching methodologies by taking advantage of global geometrical and spatial information from distinctive landmarks. Experimental results show that our approach is capable of providing high-quality disparity maps comparable to other well-known contemporary techniques.     

基于自适应迭代松弛的立体点对匹配鲁棒算法

图像匹配是立体视觉的重要部分,也是双目立体测量系统必须解决和最难解决的问题。为了对图像进行鲁棒性匹配,提出了一种基于自适应迭代松弛的立体点对匹配方法。该方法首先利用视差梯度约束来构造匹配支持度函数;然后通过松弛方法优化该函数来完成立体点对的匹配。由于利用了动态更新松弛匹配过程参数的方法,因此有效地降低了误匹配率和误剔除率。在此基础上还提出了对松弛过程结束后的匹配结果,再次使用视差梯度约束来进行进一步检验的策略,该策略能够以一定幅度的误剔除率提升为代价,大幅度降低了误匹配率,从而可满足许多要求严格限制误匹配率的应用。实验结果证明,该新算法是有效的,并已经用于一个双目立体测量原型系统当中。     

Multiflash stereopsis: depth-edge-preserving stereo with small baseline illumination

Traditional stereo matching algorithms are limited in their ability to produce accurate results near depth discontinuities, due to partial occlusions and violation of smoothness constraints. In this paper, we use small baseline multi-flash illumination to produce a rich set of feature maps that enable acquisition of discontinuity preserving point correspondences. First, from a single multi-flash camera, we formulate a qualitative depth map using a gradient domain method that encodes object relative distances. Then, in a multiview setup, we exploit shadows created by light sources to compute an occlusion map. Finally, we demonstrate the usefulness of these feature maps by incorporating them into two different dense stereo correspondence algorithms, the first based on local search and the second based on belief propagation. Experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods. We also demonstrate that small baseline illumination can be useful to handle specular reflections in stereo imagery. Different from most existing active illumination techniques, our method is simple, inexpensive, compact, and requires no calibration of light sources.     

Using Multiple-Hypothesis Disparity Maps and Image Velocity for 3-D Motion Estimation

In this paper we explore a multiple hypothesis approach to estimating rigid motion from a moving stereo rig. More precisely, we introduce the use of Gaussian mixtures to model correspondence uncertainties for disparity and image velocity estimation. We show some properties of the disparity space and show how rigid transformations can be represented. An algorithm derived from standard random sampling-based robust estimators, that efficiently estimates rigid transformations from multi-hypothesis disparity maps and velocity fields is given.     

Depth extraction using a single moving camera: an integration of depth from motion and depth from stereo

An integrated approach to extract depth, efficiently and accurately, from a sequence of images is presented in this paper. The method combines the ability of the stereo processing to acquire highly accurate depth measurements and the efficiency of spatial and temporal gradient analysis. As a result of this integration, depth measurements of high quality are obtained at a speed approximately ten times greater than that of stereo processing. Without any a priori information of the locations of the points in the scene, the correspondence problem in stereo processing is computationally expensive. In our approach, we use spatial and temporal gradient (STG) analysis, which has been shown to provide depth with great efficiency, but limited accuracy, to guide the matching process of stereo. The camera motion used in the approach can be either lateral or axial. Extensive experiments on real scenes have shown the ability of the integrated approach to acquire depth with a mean error of less than 3%.