This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

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Literature Survey on Stereo Vision Disparity Map Algorithms

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and RS400.

Intel(R) RealSense(TM) Stereoscopic Depth Cameras

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition Our discussion covers the Intel RealSense R200 and the Intel RealSense D400 (formally RS400)

Intel RealSense Stereoscopic Depth Cameras

Systems and methods for calibrating a volume dimensioner are provided. In one embodiment, a calibrating system comprises a dimensioner and a reference object. The dimensioner is configured to remotely sense characteristics of an object and calculate physical dimensions of the object from the sensed characteristics. The reference object has predefined physical dimensions and an outside surface that exhibits a pattern of reference markings. The dimensioner is configured to be calibrated using the reference object as a basis for comparison.

Calibrating a volume dimensioner

Stereo matching is a challenging problem, and high-accuracy stereo matching is still required in various computer vision applications, e.g., 3-D scanning, autonomous navigation, and 3-D reconstruction. Therefore, we present a novel image-guided stereo matching algorithm, which employs the efficient combined matching cost and multistep disparity refinement, to improve the accuracy of existing local stereo matching algorithms. Different from all the other methods, we introduce a guidance image for the whole algorithm. This filter-based guidance image is generated by extracting the enhanced information from the raw stereo image. The combined matching cost consists of the novel double-RGB gradient, the improved lightweight census transform, and the image color. This cost measurement is robust against image noise and textureless regions in computing the matching cost. Furthermore, a new systemic multistep refinement process, which includes outlier classification, four-direction propagation, leftmost propagation, and an exponential step filter, is proposed to remove the outliers in the raw disparity map. Experiments on the Middlebury benchmark demonstrate our algorithm’s superior performance that it ranks first among the 158 submitted algorithms. Moreover, the proposed method is also robust on the 30 Middlebury data sets and the real-world Karlsruhe Institute of Technology and Toyota Technological Institute benchmark.

Accurate Image-Guided Stereo Matching With Efficient Matching Cost and Disparity Refinement

Fast stereo matching using adaptive guided filtering

A novel full-image guided filtering method is proposed. Different with many existing neighborhood filters, all input elements are employed during the proposed filtering approach. In addition, a novel scheme called weight propagation is proposed to compute support weights. It fulfills the requirements of edge preserving and low complexity. It is applied to the cost-volume filtering in the local stereo matching framework. The algorithm utilizing the proposed filtering method is currently one of the best local algorithms on the Middlebury stereo testbed in terms of both speed and accuracy.

Full-Image Guided Filtering for Fast Stereo Matching

The invention discloses a method for converting 2D video into 3D video in a three-dimensional television system, which comprises the following steps: (1) obtaining a 2D video file to be converted, which is compressed by a video coding standard; (2) decoding to obtain original video, extracting the motion vector information of each 4*4 pixel block from a video decoder and converting the motion vector information into the motion amplitude; (3) describing an initial depth figure of each frame image by the motion amplitude; (4) iterating an edge correction filter wave to obtain a clear, accurate and dense depth figure of an object edge and finishing the expression of the 3D video. The invention obtains the initial depth figure by a middle result of the video decoder and well saves the processing time of signals and the cost of a final product; and an edge correction wave filter is designed so that the problem of obtaining the clear, accurate and dense depth figure of the object edge in analgorithm for obtaining depth based on an image is solved.

Method for converting 2D video into 3D video in three-dimensional television system

A depth-image-based rendering (DIBR) method with spatial and temporal texture synthesis is presented in this article. Theoretically, the DIBR algorithm can be used to generate arbitrary virtual views of the same scene in a three-dimensional television system. But the disoccluded area, which is occluded in the original views and becomes visible in the virtual views, makes it very difficult to obtain high image quality in the extrapolated views. The proposed view synthesis method combines the temporally stationary scene information extracted from the input video and spatial texture in the current frame to fill the disoccluded areas in the virtual views. Firstly, the current texture image and a stationary scene image, which is extracted from the input video, are warped to the same virtual perspective position by the DIBR method. Then, the two virtual images are merged together to reduce the hole regions and maintain the temporal consistency of these areas. Finally, an oriented exemplar-based inpainting method is utilized to eliminate the remaining holes. Experimental results are shown to demonstrate the performance and advantage of the proposed method compared with other view synthesis methods.

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Depth-image-based rendering with spatial and temporal texture synthesis for 3DTV

A novel cost volume regularization algorithm is proposed It successfully combines weighted cost aggregation in local stereo methods and smoothness constraints in global stereo methods By enforcing local smoothness during the weighted cost aggregation on the horizontal tree structure, cost distribution can be well regularized Accurate disparity results with sharp boundary can be generated while keeping extremely low computational amount A new post-processing method is also proposed The whole stereo correspondence algorithm achieves competitive performance on the Middlebury stereo testbed in terms of both accuracy and speed

Qingqing Yang

Papers

Fast stereo matching using adaptive guided filtering

Full-Image Guided Filtering for Fast Stereo Matching

Method for converting 2D video into 3D video in three-dimensional television system

Depth-image-based rendering with spatial and temporal texture synthesis for 3DTV

Local Smoothness Enforced Cost Volume Regularization for Fast Stereo Correspondence