Abstract not found

This paper presents a new algorithm for multi-view reconstruction that demonstrates both accuracy and efficiency. Our method is based on robust binocular stereo matching, followed by adaptive point-based filtering of the merged point clouds, and efficient, high-quality mesh generation. All aspects of our method are designed to be highly scalable with the number of views. Our technique produces the most accurate results among current algorithms for a sparse number of viewpoints according to the Middlebury datasets. Additionally, we prove to be the most efficient method among non-GPU algorithms for the same datasets. Finally, our scaled-window matching technique also excels at reconstructing deformable objects with high-curvature surfaces, which we demonstrate with a number of examples.

Abstract. This paper presents a technique for estimating the threedimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. We enforce the scene flow to yield consistent displacement vectors in the left and right images. The decoupling strategy has two main advantages: Firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. The approach provides dense velocity estimates with accurate results at distances up to 50 meters. 1

This paper presents an area-based stereo algorithm suitable to real time applications. The core of the algorithm relies on the uniqueness constraint and on a matching process that allows for rejecting previous matches as soon as more reliable ones are found. The proposed approach is compared with the left-right consistency constraint, being the latter the basic method for detecting unreliable matches in many area-based stereo algorithms. The algorithm has been carefully optimised to obtain a very fast implementation on a Personal Computer. This paper describes the computational optimisation strategy, which is based on a very effective incremental calculation scheme. Finally, we provide experimental results obtained on stereo pairs with ground-truth as well as computation-time measurements

Abstract not found

We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multiwindow scheme using left– right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both accuracy and efficiency.

Figure 1: Our method reconstructs accurate depth from light fields of complex scenes. The images on the left show a 2D slice of a 3D input light field, a so called epipolar-plane image (EPI), and two out of one hundred 21 megapixel images that were used to construct the light field. Our method computes 3D depth information for all visible scene points, illustrated by the depth EPI on the right. From this representation, individual depth maps or segmentation masks for any of the input views can be extracted as well as other representations like 3D point clouds. The horizontal red lines connect corresponding scanlines in the images with their respective position in the EPI. This paper describes a method for scene reconstruction of complex, detailed environments from 3D light fields. Densely sampled light fields in the order of 109 light rays allow us to capture the real world in unparalleled detail, but efficiently processing this amount of data to generate an equally detailed reconstruction represents a significant challenge to existing algorithms. We propose an algorithm that leverages coherence in massive light fields by breaking with a number of established practices in image-based reconstruction. Our algorithm first computes reliable depth estimates specifically

Image retargeting algorithms attempt to adapt the image content to the screen without distorting the important ob-jects in the scene. Existing methods address retargeting of a single image. In this paper we propose a novel method for retargeting a pair of stereo images. Naively retargeting each image independently will distort the geometric struc-ture and make it impossible to perceive the 3D structure of the scene. We show how to extend a single image seam carv-ing to work on a pair of images. Our method minimizes the visual distortion in each of the images as well as the depth distortion. A key property of the proposed method is that it takes into account the visibility relations between pixels in the image pair (occluded and occluding pixels). As a re-sult, our method guarantees, as we formally prove, that the retargeted pair is geometrically consistent with a feasible 3D scene, similar to the original one. Hence, the retargeted stereo pair can be viewed on a stereoscopic display or pro-cessed by any computer vision algorithm. We demonstrate our method on a number of challenging indoor and outdoor stereo images. 1.

This work presents a technique for computing dense disparity maps from a binocular stereo camera system. The methods are applied in an Augmented Reality setting for combining real and virtual worlds with proper occlusions. The proposed stereo correspondence technique is based on area matching and facilitates an efficient strategy by using the concept of a three-dimensional similarity accumulator, whereby occlusions are detected and object boundaries are extracted correctly. The main contribution of this paper is the way we fill the accumulator using absolute differences of images and computing a mean filter on these difference images. This is where the main advantages of the accumulator approach can be exploited, since all entries can be computed in parallel and thus extremely efficient. Additionally, we perform an asymmetric correction step and a post-processing of the disparity maps that maintains object edges.

Image rectification is a process of transforming a set of images into a new set such that the epipolar lines in the transformed images have the same direction as the image rows or columns to enable an efficient and reliable stereo matching. Previous algorithms for stereo image rectification either work for two view uncalibrated or two/three view calibrated situations. In this paper we propose several novel techniques to rectify uncalibrated trinocular images using the trilinear tensor or projective invariants or fundamental matrices obtained from a triplet of images. Our new methods include: a rotation and skew method, an affine transformation method, and a vanishing points method. Real images have been used for testing purposes, and accurate results have been obtained.