We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity values are regressed from the cost volume using a proposed differentiable soft argmin operation, which allows us to train our method end-to-end to sub-pixel accuracy without any additional post-processing or regularization. We evaluate our method on the Scene Flow and KITTI datasets and on KITTI we set a new state-of-the-art benchmark, while being significantly faster than competing approaches.
The matching cost is a measure of pixel dissimilarity for potentially corresponding image locations @cite_22 , of which absolute differences, squared differences, and truncated differences are examples. Local descriptors based on gradients @cite_44 or binary patterns, such as CENSUS @cite_13 or BRIEF @cite_39 @cite_1 , can be employed. Instead of aggregating neighboring pixels equally as patch-based matching costs do, awareness of the image content can more heavily incorporate neighboring pixels possessing similar appearance, under the assumption that they are more likely to come from the same surface and disparity. A survey of these techniques is provided by @cite_27 . Local matching costs may also be optimized within a global framework, usually minimizing an energy function combining a local data term and a pairwise smoothness term. Global optimization can be accomplished using graph cuts @cite_16 or belief propagation @cite_18 , which can be extended to slanted surfaces @cite_45 . A popular and effective approximation to global optimization is the (SGM) of Hirschm "u ller @cite_33 , where dynamic programming optimizes a pathwise form of the energy function in many directions.
@cite_18 @cite_22 @cite_33 @cite_1 @cite_39 @cite_44 @cite_27 @cite_45 @cite_16 @cite_13
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A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a self-adapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique that is more robust to outliers. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. The optimal disparity plane labeling is approximated by applying belief propagation. Experimental results using the Middlebury stereo test bed demonstrate the superior performance of the proposed method Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper we evaluate the insensitivity of different matching costs with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants and measure their performance in the presence of global intensity changes (e.g., due to gain and exposure differences), local intensity changes (e.g., due to vignetting, non-Lambertian surfaces, and varying lighting), and noise. Using existing stereo datasets with ground-truth disparities as well as six new datasets taken under controlled changes of exposure and lighting, we evaluate the different costs with a local, a semi-global, and a global stereo method. The stereo correspondence problem is still a highly active topic of research with many applications in the robotic domain. Still many state of the art algorithms proposed to date are unable to reasonably handle high resolution images due to their run time complexities or memory requirements. In this work we propose a novel stereo correspondence estimation algorithm that employs binary locality sensitive hashing and is well suited to implementation on the GPU. Our proposed method is capable of processing very high-resolution stereo images at near real-time rates. An evaluation on the new Middlebury and Disney high-resolution stereo benchmarks demonstrates that our proposed method performs well compared to existing state of the art algorithms. We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the Hamming distance, which is very efficient to compute, instead of the L2 norm as is usually done. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and U-SURF on standard benchmarks and show that it yields a similar or better recognition performance, while running in a fraction of the time required by either. In this paper we propose a novel approach to binocular stereo for fast matching of high-resolution images. Our approach builds a prior on the disparities by forming a triangulation on a set of support points which can be robustly matched, reducing the matching ambiguities of the remaining points. This allows for efficient exploitation of the disparity search space, yielding accurate dense reconstruction without the need for global optimization. Moreover, our method automatically determines the disparity range and can be easily parallelized. We demonstrate the effectiveness of our approach on the large-scale Middlebury benchmark, and show that state-of-the-art performance can be achieved with significant speedups. Computing the left and right disparity maps for a one Megapixel image pair takes about one second on a single CPU core. We propose a new approach to the correspondence problem that makes use of non-parametric local transforms as the basis for correlation. Non-parametric local transforms rely on the relative ordering of local intensity values, and not on the intensity values themselves. Correlation using such transforms can tolerate a significant number of outliers. This can result in improved performance near object boundaries when compared with conventional methods such as normalized correlation. We introduce two non-parametric local transforms: the rank transform, which measures local intensity, and the census transform, which summarizes local image structure. We describe some properties of these transforms, and demonstrate their utility on both synthetic and real data.