We investigate the feasibility of reconstructing an arbitrarily-shaped specular scene (refractive or mirror-like) from one or more viewpoints. By reducing shape recovery to the problem of reconstructing individual 3D light paths that cross the image plane, we obtain three key results. First, we show how to compute the depth map of a specular scene from a single viewpoint, when the scene redirects incoming light just once. Second, for scenes where incoming light undergoes two refractions or reflections, we show that three viewpoints are sufficient to enable reconstruction in the general case. Third, we show that it is impossible to reconstruct individual light paths when light is redirected more than twice. Our analysis assumes that, for every point on the image plane, we know at least one 3D point on its light path. This leads to reconstruction algorithms that rely on an “environment matting” procedure to establish pixel-to-point correspondences along a light path. Preliminary results for a variety of scenes (mirror, glass, etc) are also presented.

of the 3D gradient field tomographically reconstructed from 16 cameras. Far right: volume rendering of the final refractive index field after

We present a recording scheme, image formation model and reconstruction method that enables image-based modeling of flowing bodies of water from multi-video input data. The recorded water is dyed with a fluorescent chemical to measure the thickness of a column of water, which leads to an image formation model based on integrated emissivities along a viewing ray. This model allows for a photoconsistency based error measure for a weighted minimal surface, which is recovered using a PDE obtained from the Euler-Lagrangian formulation of the problem. The resulting equation is solved using the level set method. 1.

This state of the art report covers reconstruction methods for transparent and specular objects or phenomena. While the 3D acquisition of opaque surfaces with lambertian reflectance is a well-studied problem, transparent, refractive, specular and potentially dynamic scenes pose challenging problems for acquisition systems. This report reviews and categorizes the literature in this field. Despite tremendous interest in object digitization, the acquisition of digital models of transparent or specular objects is far from being a solved problem. On the other hand, real-world data is in high demand for applications such as object modeling, preservation of historic artifacts and as input to data driven modeling techniques. With this report we aim at providing a reference for and an introduction to the field of transparent and specular object reconstruction. We describe acquisition approaches for different classes of objects. Transparent objects/phenomena that do not change the straight ray geometry can be found foremost in natural phenomena. Refraction effects are usually small and can be considered negligible for these objects. Phenomena as diverse as fire, smoke, and interstellar nebulae can be modeled using a straight ray model of image formation. Refractive and specular surfaces on the other hand change the straight rays into usually piecewise linear ray paths, adding additional complexity to the reconstruction problem. Translucent objects exhibit significant sub-surface scattering effects rendering traditional acquisition approaches unstable. Different classes of techniques have been developed to deal with these problems and good reconstruction results can be achieved with current state-of-the-art techniques. However, the approaches are still specialized and targeted at very specific object classes. We classify the existing literature and hope to provide an entry point to this exiting field.

In the last few years, new view synthesis has emerged as an important application of 3D stereo reconstruction. While the quality of stereo has improved, it is still imperfect, and a unique depth is typically assigned to every pixel. This is problematic at object boundaries, where the pixel colors are mixtures of foreground and background colors. Interpolating views without explicitly accounting for this effect results in objects with a "cut-out" appearance.

Lately, new methods for the acquisition of time-varying, volumetric data for photo-realistic rendering of semitransparent, volumetric phenomena like fire and smoke have been developed. This paper presents a wavelet-coding and rendering approach for these volumetric sequences that exploits spatial as well as temporal coherence in the data. A space partitioning tree allows for efficient storage and real-time rendering of dynamic, volumetric data on common PC hardware.

We propose a new approach to capture the volumetric density of dynamic scattering media instantaneously with a single image. The volume is probed with a set of laser lines and the scattered intensity is recorded by a conventional camera. We then determine the density along the laser lines taking the scattering properties of the media into account. A specialized approximation technique reconstructs the full density field in the volume. We apply the technique to capture the volumetric density of participating media such as smoke.

Generating realistic animations of passive dynamic systems such as rigid bodies, cloth and fluids is an important problem in computer graphics. Although several techniques for animating these phenomena have been developed, achieving the level of realism exhibited by objects in the real world has proved to be incredibly hard. We argue, therefore, that an effective method for increasing the realism of these types of computer animations is to infer the behavior of real phenomena from video. This thesis presents two techniques for using video to create realistic animations of phenomena such as rigid bodies, cloth, waterfalls, streams, smoke and fire. The first technique, inverse simulation, estimates the parameters of physical simulations from video. Physical simulation techniques are widely used in computer graphics to generate animations of passive phenomenon using physical laws and numerical integration techniques. The behavior of a physical simulator is governed by a set of parameters, typically specified by the animator. However, directly tuning the physical parameters of complex simulations like rigid bodies or cloth to achieve a desired motion is often cumbersome and nonintuitive. The inverse simulation framework uses optimization to automatically estimate the simulation