Abstract not found

This paper considers the use of stereo vision in structured environments. Sharp discontinuities and large untextured areas must be anticipated, but complex or natural shapes of objects and fine structures should be handled as well. Additionally, radiometric differences of input images often occur in practice. Finally, computation time is an issue for handling large or many images in acceptable time. The Semi-Global Matching method is chosen as it fulfills already many of the requirements. Remaining problems in structured environments are carefully analyzed and two novel extensions suggested. Firstly, intensity consistent disparity selection is proposed for handling untextured areas. Secondly, discontinuity preserving interpolation is suggested for filling holes in the disparity images that are caused by some filters. It is shown that the performance of the new method on test images with ground truth is comparable to the currently best stereo methods, but the complexity and runtime is much lower. 1.

Stereo matching, tree-based dynamic programming, fast stereo method. This work describes a fast method for computing dense stereo correspondences that is capable of generating results close to the state-of-the-art. We propose running a separate disparity computation process in each image pixel. The idea is to root a tree graph on the pixel whose disparity needs to be reconstructed. The tree thereby forms an individual approximation of the standard four-connected grid for this specific pixel. An exact optimum of a predefined energy function on the applied tree structure is determined via dynamic programming (DP), and the root pixel is assigned to the disparity of optimal costs. We present two simple tree structures that allow for the efficient calculation of all trees ’ optima with only four scanline-based DP passes. These simple trees are designed to capture all pixels of the reference frame and incorporate horizontal and vertical smoothness edges in order to weaken the scanline streaking problem inherent in DP-based approaches. We evaluate our results using the Middlebury test set. Our algorithm currently ranks at the eighth position of approximately 30 algorithms in the Middlebury database. More importantly, it is the currently best-performing method that does not use image segmentation and is significantly faster than most competing algorithms. Our method needs less than a second to determine the disparity map for typical stereo pairs. 1

Abstract. A novel algorithm for obtaining accurate dense disparity measurements and precise border localization from stereo pairs is proposed. The algorithm embodies a very effective variable support approach based on segmentation within a Scanline Optimization framework. The use of a variable support allows for precisely retrieving depth discontinuities while smooth surfaces are well recovered thanks to the minimization of a global function along multiple scanlines. Border localization is further enhanced by symmetrically enforcing the geometry of the scene along depth discontinuities. Experimental results show a significant accuracy improvement with respect to comparable stereo matching approaches. 1 Introduction and Previous Work In the last decades stereo vision has been one of the most studied task of computer vision and many proposals have been made in literature on this topic (see [1] for a review). The problem of stereo correspondence can be formulated as

We present a novel multi-baseline, multi-resolution stereo method, which varies the baseline and resolution proportionally to depth to obtain a reconstruction in which the depth error is constant. This is in contrast to traditional stereo, in which the error grows quadratically with depth, which means that the accuracy in the near range far exceeds that of the far range. This accuracy in the near range is unnecessarily high and comes at significant computational cost. It is, however, non-trivial to reduce this without also reducing the accuracy in the far range. Many datasets, such as video captured from a moving camera, allow the baseline to be selected with significant flexibility. By selecting an appropriate baseline and resolution (realized using an image pyramid), our algorithm computes a depthmap which has these properties: 1) the depth accuracy is constant over the reconstructed volume, 2) the computational effort is spread evenly over the volume, 3) the angle of triangulation is held constant w.r.t. depth. Our approach achieves a given target accuracy with minimal computational effort, and is orders of magnitude faster than traditional stereo. 1.

Abstract. Stereo and motion analysis are potential techniques for providing information for control or assistance systems in various robotics or driver assistance applications. This paper evaluates the performance of several stereo and motion algorithms over a long synthetic sequence (100 stereo pairs). Such an evaluation of low-level computer vision algorithms is necessary, as moving platforms are being used for image analysis in a wide area of applications. In this paper algorithms are evaluated with respect to robustness by modifying the test sequence with various types of realistic noise. The novelty of this paper is comparing top performing algorithms on a long sequence of images, taken from a moving platform. 1

Abstract—In this paper, we introduce a novel stereo-monocular fusion approach to on-road localization and tracking of vehicles. Utilizing a calibrated stereo-vision rig, the proposed approach combines monocular detection with stereo-vision for on-road vehicle localization and tracking for driver assistance. The system initially acquires synchronized monocular frames and calculates depth maps from the stereo rig. The system then detects vehicles in the image plane using an active learning-based monocular vision approach. Using the image coordinates of detected vehicles, the system then localizes the vehicles in real-world coordinates using the calculated depth map. The vehicles are tracked both in the image plane, and in real-world coordinates, fusing information from both the monocular and stereo modalities. Vehicles ’ states are estimated and tracked using Kalman filtering. Quantitative analysis of tracks is provided. The full system takes 46ms to process a single frame.

In this work we present a method to detect overlaps in image sequences, and use this information to integrate overlapping sparse 3D structure from video sequences. The additional temporal information of these images is used to increase robustness over single image pair matching. A scanline optimization problem formulation is used to compute the best sequence alignment using wide-baseline image matching techniques. Compared to a direct dynamic programming approach, the scanline optimization formulation increases the robustness of sequence alignment for general relative motions. The proposed alignment method is employed to integrate sparse 3D models reconstructed from separate video sequences. In addition loop closures are detected. Consequently, the 3D modeling process from sequential image data can be split into fast sequence processing and subsequent global integration steps. 1.

Abstract. Intelligent vehicle systems need to distinguish which objects are moving and which are static. A static concrete wall lying in the path of a vehicle should be treated differently than a truck moving in front of the vehicle. This paper proposes a new algorithm that addresses this problem, by providing dense dynamic depth information, while coping with real-time constraints. The algorithm models disparity and disparity rate pixel-wise for an entire image. This model is integrated over time and tracked by means of many pixel-wise Kalman filters. This provides better depth estimation results over time, and also provides speed information at each pixel without using optical flow. This simple approach leads to good experimental results for real stereo sequences, by showing an improvement over previous methods. 1

Abstract. Ambitious driver assistance for complex urban scenarios demands a complete awareness of the situation, including all moving and stationary objects that limit the free space. Recent progress in real-time dense stereo vision provides precise depth information for nearly every pixel of an image. This rises new questions: How can one efficiently analyze half a million disparity values of next generation imagers? And how can one find all relevant obstacles in this huge amount of data in real-time? In this paper we build a medium-level representation named “stixel-world”. It takes into account that the free space in front of vehicles is limited by objects with almost vertical surfaces. These surfaces are approximated by adjacent rectangular sticks of a certain width and height. The stixel-world turns out to be a compact but flexible representation of the three-dimensional traffic situation that can be used as the common basis for the scene understanding tasks of driver assistance and autonomous systems. 1