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Illumination is one of the key components in the creation of realistic renderings of scenes containing virtual objects. In this paper, we present a set of novel algorithms and data structures for visualization, processing and rendering with real world lighting conditions captured using High Dynamic Range (HDR) video. The presented algorithms enable rapid construction of general and editable representations of the lighting environment, as well as extraction and fitting of sampled reflectance to parametric BRDF models. For efficient representation and rendering of the sampled lighting environment function, we consider an adaptive (2D/4D) data structure for storage of light field data on proxy geometry describing the scene. To demonstrate the usefulness of the algorithms, they are presented in the context of a fully integrated framework for spatially varying image based lighting. We show reconstructions of example scenes and resulting production quality renderings of virtual furniture with spatially varying real world illumination including occlusions.

In this paper, we propose a method for estimating the camera pose for an environment in which the intrinsic camera parameters change dynamically. In video see-through augmented reality (AR) technology, image-based methods for estimating the camera pose are used to superimpose virtual objects onto the real environment. In general, video see-through-based AR cannot change the image magnification that results from a change in the camera's field-of-view because of the difficulty of dealing with changes in the intrinsic camera parameters. To remove this limitation, we propose a novel method for simultaneously estimating the intrinsic and extrinsic camera parameters based on an energy minimization framework. Our method is composed of both online and offline stages. An intrinsic camera parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic camera parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based method to estimate the camera parameters: reprojection errors based on the epipolar constraint and the constraint of the continuity of zoom values. By using a novel energy function, our method can accurately estimate intrinsic and extrinsic camera parameters. We confirmed experimentally that the proposed method can achieve accurate camera parameter estimation during camera zooming. Graphical abstractDisplay Omitted HighlightsWe propose an intrinsic and extrinsic camera parameter estimation method.We extended a conventional marker-based method by adding two energy terms.Camera parameters are estimated accurately using new energy function.The effectiveness of our method is shown in simulated and real environments.

There are hundreds of distinct 3D, CAD and engineering file formats. As engineering design and analysis has become increasingly digital, the proliferation of file formats has created many problems for data preservation, data exchange, and interoperability. In some situations, physical file objects exist on legacy media and must be identified and interpreted for reuse. In other cases, file objects may have varying representational expressiveness.We introduce the problem of automated file recognition and classification in emerging digital engineering environments, where all design, manufacturing and production activities are "born digital." The result is that massive quantities and varieties of data objects are created during the product lifecycle.This paper presents an approach to automated identification of engineering file formats. This work operates independent of any modeling tools and can identify families of related file objects as well as variations in versions. This problem is challenging as it cannot assume any a priori knowledge about the nature of the physical file object. Applications for these methods include support for a number of emerging applications in areas such as forensic analysis, data translation, as well as digital curation and long-term data management. Graphical abstractDisplay Omitted HighlightsProvides support for emerging applications in long-term data management.Compression-based classification enables specification-free format identification.Classification accuracy best when NCD distance and the first 16KB of files used.Classifier is highly effective at distinguishing among very similar formats.Computational time is comparable or better than approaches based on known signatures.

Dehydrated core/shell fruits, such as jujubes, raisins and plums, show very complex buckles and wrinkles on their exocarp. It is a challenging task to model such complicated patterns and their evolution in a virtual environment even for professional animators. This paper presents a unified physically-based approach to simulate the morphological transformation for the core/shell fruits in the dehydration process. A finite element method (FEM), which is based on the multiplicative decomposition of the deformation gradient into an elastic part and a dehydrated part, is adopted to model the morphological evolution. In the method, the dehydration pattern can be conveniently controlled through physically prescribed parameters according to the geometry and material of the real fruits. The effects of the parameters on the final dehydrated surface patterns are investigated and summarized in detail. Experiments on jujubes, wolfberries, raisins and plums are given, which demonstrate the efficacy of the method. Display Omitted A unified physically-based approach for modeling the deformation in a general dehydration process.A nonlinear finite element method to simulate the dehydration-induced morphological transformation.Simple parameters based on the geometry and material of the fruits to control the evolution of surface patterns.A looking-up table for efficient simulation.