Adquisición y visualización de vídeo 3D

La visualización de imágenes en 3D es posible gracias a los sistemas estereoscópicos, que nos permiten capturar diferentes vistas de una misma escena y mediante el procesado de estas se consigue extraer información de profundidad que nos permite realizar el efecto. El sistema estereoscópico está formado por dos cámaras 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 estereoscópico y procesar las imágenes obtenidas por este sistema, para finalmente lograr el efecto 3D. Esto lo logramos por medio de un proceso de calibración mediante los parámetros intrínsecos (internos de la cámara) y extrínsecos (informan de la rotación y traslación de los ejes de referencia de las cámaras respecto a los de la escena), conseguidos a través de imágenes controladas. Este proceso es conocido como rectificación del par estéreo 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 estereoscópico se procesan las imágenes para visualizarlas en diferentes modos: anaglífico (método de visualización directa) y Side by Side (método que requiere de procesado y dispositivos necesarios para visualizar mediante la técnica de secuencia de frames alternados). Por tanto, se obtienen diferentes modos de visualización para su posterior transmisión, objetivo de otros TFC. El sistema estereoscópico es la fase inicial de un sistema completo de transmisión de imágenes, el cual dará el flujo de entrada con las posibilidades anteriormente expuestas. El sistema completo está compuesto por las siguientes fases: adquisición, codificación, transmisión, decodificación y visualización de imágenes en tres dimensiones

Rendering from unstructured collections of images

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

Abstract—This 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