Image-based modeling and rendering

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WIKIPEDIA ARTICLE

In computer graphics and computer vision, image-based modeling and rendering (IBMR) methods rely on a set of two-dimensional images of a scene to generate a three-dimensional model and then render some novel views of this scene.

The traditional approach of computer graphics has been used to create a geometric model in 3D and try to reproject it onto a two-dimensional image. Computer vision, conversely, 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 clues. 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.

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 plenoptic illumination function which is a parametrisation of the 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 ${\displaystyle (x,y,z)}$, its orientation ${\displaystyle (\theta ,\phi )}$, its wavelength ${\displaystyle (\lambda )}$ and its time ${\displaystyle (t)}$: ${\displaystyle P(x,y,z,\theta ,\phi ,\lambda ,t)}$. 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).

IBMR methods and algorithms

• View morphing generates a transition between images
• Panoramic imaging renders panoramas using image mosaics of individual still images
• Lumigraph relies on a dense sampling of a scene
• Space carving generates a 3D model based on a photo-consistency check

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]. For an exhaustive overview of the currently available methods and algorithms in this topic, see the following surveys [1, 28]

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