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A common practice among implementations of RANSAC is to take a few samples extra than the minimum required for estimation problem, but the implications of this heuristic is lacking in literature. RANSAC is an iterative algorithm for robust model parameter estimation. The same mechanism can be used to predict depth in stereo disparity estimation problem.įor robust tracking of the motion of camera array, we have used the Random Sample Consensus (RANSAC) algorithm. 3D-warping along with the camera tracking can then be used to generate reference frames to improve compression efficiency of motion vectors. Using the depth information, the RGB-D device motion can be accurately tracked. In this work, we have leveraged depth for better RGB-D data compression and efficient depth estimation. The availability of depth modality opens up several possibilities for efficient MVD data compression. Traditionally, it has been assumed that devices are static but for smartphones such an assumption is not valid. The number of views required depends on the application.
#KINECT RANDOM DOT PATTERN PLUS#
3D-communication require synchronous capture of the scene from multiple viewpoints along with depth for each view, known as Multiview Plus Depth (MVD) data. Depth sensors are being incorporated into smartphones for large-scale deployment of AR applications. With the development of technologies such as head mounted displays and Augmented Reality (AR) the need for efficient 3D scene communication is becoming vital. In recent years communication of the scene geometry is gaining importance. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. Experiments on depth captured from a noisy sensor (Microsoft Kinect) shows superior Rate-Distortion performance over the 3D extension of HEVC codec. Also presented is a unique method to encode depth based on its segmented planar representation. While all prior works based on this approach remain restricted to images only and under noise-free conditions, this paper presents an efficient solution to planar segmentation in noisy depth videos. The segmentation algorithm is based on Markov Random Field assumptions on depth data and solved using Graph Cuts. This paper presents a method for coding depth videos, captured from mobile RGB-D sensors, by planar segmentation. While there exist Video Coding Standards such as HEVC and H.264/AVC for compression of RGB/texture component, the coding of depth data is still an area of active research. Given the large number of smartphone users, efficient storage and transmission of RGB-D data is of paramount interest to the research community.
#KINECT RANDOM DOT PATTERN WINDOWS#
The intrinsics of the Kinect color/depth cameras can either be obtained from Kinect Windows SDK or calibrated using a printed checkerboard.Augmented Reality applications are set to revolutionize the smartphone industry due to the integration of RGB-D sensors into mobile devices. In the rest of the article, we focus on calibrating the intrinsic parameters of the projector and the extrinsic parameters between the projector and the Kinect depth camera. Instead, we project a checkerboard pattern to a white flat wall, then move the bound Kinect-projector pair to capture mages from at least three different poses, as shown in the teaser image. In this article, we show that the system can be calibrated using Zhang’s method without a printed checkerboard pattern or a large room. As shown below, we bind them such that their FOVs overlap. In most simple AR applications, the relative rotation and translation between the Kinect and the projector are fixed. Existing methods, such as RGBDdemo and KinectProjectorToolkit either requires printed checkerboard patterns or a large room to calibrate Kinect depth/color cameras and a projector. We want to combine Microsoft Kinect and a projector to create cool Augmented Reality (AR) applications, one prerequisite is system calibration. Projector and Kinect depth camera extrinsics.Geometric interpretation of eigenvectors and Singular Value Decomposition (SVD).Getting the 3D-2D coordinates of the checkerboard corners.
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