Bidirectional Rendering of Vector Light Transport

On the foundations of many rendering algorithms it is the symmetry between the path traversed by light and its adjoint path starting from the camera. However, several effects, including polarization or ¿uorescence, break that symmetry, and are de¿ned only on the direction of light propagation. This reduces the applicability of bidirectional methods that exploit this symmetry for simulating effectively light transport. In this work, we focus on how to include these non-symmetric effects within a bidirectional rendering algorithm. We generalize the path integral to support the constraints imposed by non-symmetric light transport. Based on this theoretical framework, we propose modi¿cations on two bidirectional methods, namely bidirectional path tracing and photon mapping, extending them to support polarization and ¿uorescence, in both steady and transient stat

Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The large range of issues related to these interactions, and in particular to three-dimensional interactions, motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local and directional observables, to integrated energetic quantities. In the continuity of this community effort, we propose here an open-source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of path-tracing in complex data, typically high-resolution large-domain grounds and clouds. The main algorithmic advances embedded in the library are those related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null-collision (maximum cross-section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitivive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Two other examples are given as illustrations, that are respectively used to analyse the transmission of solar radiation under a cloud together with its sensitivity to an optical parameter, and to assess a parametrization of 3D radiative effects of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2

Modelling polarized light for computer graphics

The quality of visual realism in computer generated images is largely determined by the accuracy of the reflection model. Advances in global illumination techniques have removed to a large extent, some of the limitations on the physical correctness achievable by reflection models. While models currently used by researchers are physically based, most approaches have ignored the polarization of light. The few previous efforts addressing the polarization of light were hampered by inherently unphysical light transport algorithms. This paper, besides taking polarization of light into account in the reflection computation, also provides a basis for modelling polarization as an inherent attribute of light, using the Stokes parameters. A reflection model is developed within this framework and the implementation within a global illumination algorithm called Photon is presented

The Impact of Surface Normals on Appearance

The appearance of an object is the result of complex light interaction with the object. Beyond the basic interplay between incident light and the object\u27s material, a multitude of physical events occur between this illumination and the microgeometry at the point of incidence, and also beneath the surface. A given object, made as smooth and opaque as possible, will have a completely different appearance if either one of these attributes - amount of surface mesostructure (small-scale surface orientation) or translucency - is altered. Indeed, while they are not always readily perceptible, the small-scale features of an object are as important to its appearance as its material properties. Moreover, surface mesostructure and translucency are inextricably linked in an overall effect on appearance. In this dissertation, we present several studies examining the importance of surface mesostructure (small-scale surface orientation) and translucency on an object\u27s appearance. First, we present an empirical study that establishes how poorly a mesostructure estimation technique can perform when translucent objects are used as input. We investigate the two major factors in determining an object\u27s translucency: mean free path and scattering albedo. We exhaustively vary the settings of these parameters within realistic bounds, examining the subsequent blurring effect on the output of a common shape estimation technique, photometric stereo. Based on our findings, we identify a dramatic effect that the input of a translucent material has on the quality of the resultant estimated mesostructure. In the next project, we discuss an optimization technique for both refining estimated surface orientation of translucent objects and determining the reflectance characteristics of the underlying material. For a globally planar object, we use simulation and real measurements to show that the blurring effect on normals that was observed in the previous study can be recovered. The key to this is the observation that the normalization factor for recovered normals is proportional to the error on the accuracy of the blur kernel created from estimated translucency parameters. Finally, we frame the study of the impact of surface normals in a practical, image-based context. We discuss our low-overhead, editing tool for natural images that enables the user to edit surface mesostructure while the system automatically updates the appearance in the natural image. Because a single photograph captures an instant of the incredibly complex interaction of light and an object, there is a wealth of information to extract from a photograph. Given a photograph of an object in natural lighting, we allow mesostructure edits and infer any missing reflectance information in a realistically plausible way

Sparse ellipsometry: portable acquisition of polarimetric SVBRDF and shape with unstructured flash photography

Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry, a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as single scattering, and devise a novel polarimetric inverse rendering algorithm with data augmentation of specular reflection samples via generative modeling. Our results show a strong agreement with a recent ground-truth dataset of captured polarimetric BRDFs of real-world objects

Capturing and Reconstructing the Appearance of Complex {3D} Scenes

In this thesis, we present our research on new acquisition methods for reflectance properties of real-world objects. Specifically, we first show a method for acquiring spatially varying densities in volumes of translucent, gaseous material with just a single image. This makes the method applicable to constantly changing phenomena like smoke without the use of high-speed camera equipment. Furthermore, we investigated how two well known techniques -- synthetic aperture confocal imaging and algorithmic descattering -- can be combined to help looking through a translucent medium like fog or murky water. We show that the depth at which we can still see an object embedded in the scattering medium is increased. In a related publication, we show how polarization and descattering based on phase-shifting can be combined for efficient 3D~scanning of translucent objects. Normally, subsurface scattering hinders the range estimation by offsetting the peak intensity beneath the surface away from the point of incidence. With our method, the subsurface scattering is reduced to a minimum and therefore reliable 3D~scanning is made possible. Finally, we present a system which recovers surface geometry, reflectance properties of opaque objects, and prevailing lighting conditions at the time of image capture from just a small number of input photographs. While there exist previous approaches to recover reflectance properties, our system is the first to work on images taken under almost arbitrary, changing lighting conditions. This enables us to use images we took from a community photo collection website

Single view reflectance capture using multiplexed scattering and time-of-flight imaging

This paper introduces the concept of time-of-flight reflectance estimation, and demonstrates a new technique that allows a camera to rapidly acquire reflectance properties of objects from a single view-point, over relatively long distances and without encircling equipment. We measure material properties by indirectly illuminating an object by a laser source, and observing its reflected light indirectly using a time-of-flight camera. The configuration collectively acquires dense angular, but low spatial sampling, within a limited solid angle range - all from a single viewpoint. Our ultra-fast imaging approach captures space-time "streak images" that can separate out different bounces of light based on path length. Entanglements arise in the streak images mixing signals from multiple paths if they have the same total path length. We show how reflectances can be recovered by solving for a linear system of equations and assuming parametric material models; fitting to lower dimensional reflectance models enables us to disentangle measurements. We demonstrate proof-of-concept results of parametric reflectance models for homogeneous and discretized heterogeneous patches, both using simulation and experimental hardware. As compared to lengthy or highly calibrated BRDF acquisition techniques, we demonstrate a device that can rapidly, on the order of seconds, capture meaningful reflectance information. We expect hardware advances to improve the portability and speed of this device.National Science Foundation (U.S.) (Award CCF-0644175)National Science Foundation (U.S.) (Award CCF-0811680)National Science Foundation (U.S.) (Award IIS-1011919)Intel Corporation (PhD Fellowship)Alfred P. Sloan Foundation (Research Fellowship