User:S.darukumalli

= Image-based modeling and rendering = In [null computer graphics] and [null computer vision], image-based modeling and rendering (IBMR) is a collection of techniques that relies directly on a set of input two-dimensional images of a 3D scene to achieve photorealistic results rather than utilizing conventional geometry-based methods. Using images enables the rendering of high-quality novel views, while traditional approaches generally use geometry as the main primitives.

Image-based modeling refers to the use of images to drive the reconstruction of 3D models. In the case of image-based rendering, the goal is to achieve more realistic and faster renderings and to simplify the modeling task by using images, as opposed to polygons, as both modeling and rendering primitives [1].

Depth image-based rendering (DIBR) is used to create corresponding depth maps as a guidance for generating novel views. Computer vision is mostly focused on detecting, grouping, and extracting features (edges, faces, etc.) present in a given picture and then trying to interpret them as three-dimensional indicators. Conversely, image-based modeling and rendering allows the use of multiple two-dimensional images in order to generate directly novel two-dimensional images, skipping the manual modeling stage.

Contents

 * 1 Light modeling
 * 2 IBMR    methods and algorithms
 * 3 See also
 * 4 External links

Light modeling
Instead of considering only the physical model of a solid, IBMR methods usually focus more on light modeling. The fundamental concept behind IBMR is the [null plenoptic illumination function] which is a parameterisation of the [null light field]. The plenoptic function describes the light rays contained in a given volume. It can be represented with seven dimensions: a ray is defined by its position, its orientation , its wavelength  and its time. IBMR methods try to approximate the plenoptic function to render a novel set of two-dimensional images from another. Given the high dimensionality of this function, practical methods place constraints on the parameters in order to reduce this number (typically to 2 to 4).

Caption. Image-Based Modeling and Rendering techniques in the image-geometry spectrum.
·     Image-based modeling (IBM)

·     Projective methods

These techniques exploit projective properties of the scene to reconstruct geometric models directly from a set of photographs (Photo3D [2], PhotoModeler [3], PhotoBuilder [4]).

·     Tour into the picture

Tour into the picture, the simplest image-based modeling technique, recovers, from a single picture, an extremely simplified scene model consisting of just a few texture-mapped polygons [5].

·     Façade

The Façade system uses a non-linear optimization algorithm to reconstruct 3D textured models of architectural elements from photographs [6].

·     Voxel coloring

The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene able to cope with large changes in visibility and its modeling of intrinsic scene color and texture information, fully consistent with the input images [7].

·     Multi-view geometry

It is a set of intricate geometric relations between multiple view of a 3D scene, applied to recover 3D models from images [8].

·     Image-based rendering (IBR)

·     Light-field rendering

It is a method for generating new views from arbitrary camera positions without depth information or feature matching, simply by combining and resampling the available images. [9].

·     Plenoptic stitching

It gives the viewer the ability to explore unobstructed environments of arbitrary sizes and shapes, using appropriate sampling for most viewpoints in the environment by moving omnidirectional video camera over the grid [10].

·     Cylindrical panoramas

It provides horizontal orientation independence when exploring an environment from a single point. Cylindrical panoramas can be created using specialized panoramic cameras [11, 12, 13].

·     Concentric mosaics

It is a generalization of cylindrical panoramas that allows the viewer to explore a circular region and experience horizontal parallax and lighting effects. In this case, instead of using a single cylindrical image, slit cameras are rotated along planar concentric circles. A series of concentric manifold mosaics are created by composing the slit images acquired by each camera along their circular paths. Unlike light field and lumigraph where cameras are placed on a two-dimensional grid, the concentric mosaics representation reduces the amount of data by capturing a sequence of images along a circle path [14, 15].

·     Lumigraph

It is similar to light field rendering, but it applies approximated geometry to compensate for non-uniform sampling, in order to improve rendering performance [16].

·     Transfer methods

They are characterized by the use of a relatively small number of images with the application of geometric constraints (either recovered at some stage or known a priori) to reproject image pixels appropriately at a given virtual camera viewpoint [Laveau and Faugeras [17, 18].

·     Relief texture

To improve the rendering speed of 3D warping, the warping process is factored into a relatively simple pre-warping step and a traditional texture mapping step [19].

·     Image-based objects

They provide a compact image-based representation for 3D objects that can be rendered in occlusion-compatible order. An image-based object is constructed by acquiring multiple views of the object, registering and resampling them from every center of projection onto the faces of a parallelepiped. The use of a parallelepiped allows such a representation to be decomposed into parameterized planar regions for which a warper can be efficiently implemented [20].

·     Image-based visual hulls

It is based on efficient computation and shading visual hulls from silhouette image data. The algorithm takes advantage of epipolar geometry and incremental computation to achieve a constant rendering cost per rendered pixel [21].

·     3D Warping

With available depth information for every point in one or more images, 3D warping techniques can be used to render from any nearby point of view by projecting the pixels of the original image to their proper 3D locations and re-projecting them onto the new picture [22]

·     Layered depth images

To deal with the disocclusion artifacts in 3D warping, Layered Depth Image is proposed to store not only what is visible in the input image, but also what is behind the visible surface. Each pixel in the input image contains a list of depth and color values where the ray from the pixel intersects with the scene [23].

·     View-dependent texture maps

View-dependent texture mapping is used to render novel views, by warping and compositing several input images. A three-step view-dependent texture mapping method is considered to further reduce the computational cost and provide smoother blending. This method employs visibility preprocessing, polygon-view maps, and projective texture mapping [24, 25].

·     Surface light field

It is a function that assigns a color to each ray originating on a surface. Surface light fields are well suited to constructing virtual images of shiny objects under complex lighting conditions [26].

·     Light field mapping

This method is a representation and interactive visualization of surface light fields, by partitioning the radiance data over elementary surface primitives and by approximating each partitioned data by a small set of lower-dimensional discrete functions. The rendering algorithm decodes directly from this compact representation at interactive frame rates [27].