4 research outputs found

    View generation for three-dimensional scenes from video sequences

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    Adquisici贸n y visualizaci贸n de v铆deo 3D

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    La visualizaci贸n de im谩genes en 3D es posible gracias a los sistemas estereosc贸picos, que nos permiten capturar diferentes vistas de una misma escena y mediante el procesado de estas se consigue extraer informaci贸n de profundidad que nos permite realizar el efecto. El sistema estereosc贸pico est谩 formado por dos c谩maras convencionales situadas a una distancia de unos 65mm con el fin de simular la vista humana. El objetivo de este proyecto es realizar un sistema estereosc贸pico y procesar las im谩genes obtenidas por este sistema, para finalmente lograr el efecto 3D. Esto lo logramos por medio de un proceso de calibraci贸n mediante los par谩metros intr铆nsecos (internos de la c谩mara) y extr铆nsecos (informan de la rotaci贸n y traslaci贸n de los ejes de referencia de las c谩maras respecto a los de la escena), conseguidos a trav茅s de im谩genes controladas. Este proceso es conocido como rectificaci贸n del par est茅reo y consiste en alinear los puntos de las dos vistas de modo que consigamos tener una correspondencia entre las dos vistas y la escena. Una vez calibrado el sistema estereosc贸pico se procesan las im谩genes para visualizarlas en diferentes modos: anagl铆fico (m茅todo de visualizaci贸n directa) y Side by Side (m茅todo que requiere de procesado y dispositivos necesarios para visualizar mediante la t茅cnica de secuencia de frames alternados). Por tanto, se obtienen diferentes modos de visualizaci贸n para su posterior transmisi贸n, objetivo de otros TFC. El sistema estereosc贸pico es la fase inicial de un sistema completo de transmisi贸n de im谩genes, el cual dar谩 el flujo de entrada con las posibilidades anteriormente expuestas. El sistema completo est谩 compuesto por las siguientes fases: adquisici贸n, codificaci贸n, transmisi贸n, decodificaci贸n y visualizaci贸n de im谩genes en tres dimensiones

    Rendering from unstructured collections of images

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 157-163).Computer graphics researchers recently have turned to image-based rendering to achieve the goal of photorealistic graphics. Instead of constructing a scene with millions of polygons, the scene is represented by a collection of photographs along with a greatly simplified geometric model. This simple representation allows traditional light transport simulations to be replaced with basic image-processing routines that combine multiple images together to produce never-before-seen images from new vantage points. This thesis presents a new image-based rendering algorithm called unstructured lumigraph rendering (ULR). ULR is an image-based rendering algorithm that is specifically designed to work with unstructured (i.e., irregularly arranged) collections of images. The algorithm is unique in that it is capable of using any amount of geometric or image information that is available about a scene. Specifically, the research in this thesis makes the following contributions: * An enumeration of image-based rendering properties that an ideal algorithm should attempt to satisfy. An algorithm that satisfies these properties should work as well as possible with any configuration of input images or geometric knowledge. * An optimal formulation of the basic image-based rendering problem, the solution to which is designed to satisfy the aforementioned properties. * The unstructured lumigraph rendering algorithm, which is an efficient approximation to the optimal image-based rendering solution. * A non-metric ULR algorithm, which generalizes the basic ULR algorithm to work with uncalibrated images. * A time-dependent ULR algorithm, which generalizes the basic ULR algorithm to work with time-dependent data.by Christopher James Buehler.Ph.D

    View generation for three-dimensional scenes from video sequences

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    Abstract鈥擳his paper focuses on the representation and view generation of three-dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence. Furthermore, instead of using multiple calibrated stationary cameras or range scanners, we derive our depth maps from image sequences captured by an uncalibrated camera with only approximately known motion. We propose an adaptive matching algorithm that assigns various confidence levels to different regions in the depth maps. Nonuniform bicubic spline interpolation is then used to fill in low confidence regions in the depth maps. Once the depth maps are computed at preselected viewpoints, the intensity and depth at these locations are used to reconstruct arbitrary views of the 3-D scene. Specifically, the depth maps are regarded as vertices of a deformable 2-D mesh, which are transformed in 3-D, projected to 2-D, and rendered to generate the desired view. Experimental results are presented to verify our approach. I
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