We introduce a novel workflow for stereoscopic 2D to 3D conversion in which the user “paints” depth onto a 2D image via sparse scribbles, instantaneously receiving intuitive 3D feedback.
This paper focuses on reducing the computational complexity of a direct Cholesky-decomposition-based solver.
In this paper, we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation.
This paper addresses the problem of globally balancing colors between images. The input to our algorithm is a sparse set of desired color correspondences between a source and a target image.
In this paper, we propose a reinterpretation of the brush and the fill tools for digital image painting.
We propose a method for estimating the 3D structure and the dense 3D motion (scene flow) of a dynamic nonrigid 3D scene, using a camera array.
In our paper, we provide an overview on existing technologies for stereoscopic content editing, identify current challenges, and present a number of recent research results which provide novel solutions to problems such as disparity correction and depth authoring, display adaptation, and 2D-to-3D conversion.
In this paper, we introduce a framework to analyze the bandwidth of such display devices.
This paper addresses the problem of remapping the disparity range of stereoscopic images and video. Such operations are highly important for a variety of issues arising from the production, live broadcast, and consumption of 3D content.
We present an efficient and simple method for introducing temporal consistency to a large class of optimization driven image-based computer graphics problems.
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