This paper provides a comprehensive overview of urban reconstruction. While there exists a considerable body of literature, this topic is still under active research. The work reviewed in this survey stems from the following three research communities: computer graphics, computer vision and photogrammetry and remote sensing. Our goal is to provide a survey that will help researchers to better position their own work in the context of existing solutions, and to help newcomers and practitioners in computer graphics to quickly gain an overview of this vast field. Further, we would like to bring the mentioned research communities to even more interdisciplinary work, since the reconstruction problem itself is by far not solved.

This paper provides a comprehensive overview of urban reconstruction. While there exists a considerable body of literature, this topic is still under active research. The work reviewed in this survey stems from the following three research communities: computer graphics, computer vision and photogrammetry and remote sensing. Our goal is to provide a survey that will help researchers to better position their own work in the context of existing solutions, and to help newcomers and practitioners in computer graphics to quickly gain an overview of this vast field. Further, we would like to bring the mentioned research communities to even more interdisciplinary work, since the reconstruction problem itself is by far not solved.

In this work we propose a new discretization method for the Laplace–Beltrami operator defined on point-based surfaces. In contrast to the existing point-based discretization techniques, our approach does not rely on any triangle mesh structure, turning out truly mesh-free. Based on a combination of Smoothed Particle Hydrodynamics and an optimization procedure to estimate area elements, our discretization method results in accurate solutions while still being robust when facing abrupt changes in the density of points. Moreover, the proposed scheme results in numerically stable discrete operators. The effectiveness of the proposed technique is brought to bear in many practical applications. In particular, we use the eigenstructure of the discrete operator for filtering and shape segmentation. Point-based surface deformation is another application that can be easily carried out from the proposed discretization method.

In this work we propose a new discretization method for the Laplace–Beltrami operator defined on point-based surfaces. In contrast to the existing point-based discretization techniques, our approach does not rely on any triangle mesh structure, turning out truly meshfree. Based on a combination of Smoothed Particle Hydrodynamics and an optimization procedure to estimate area elements, our discretization method results in accurate solutions while still being robust when facing abrupt changes in the density of points. Moreover, the proposed scheme results in numerically stable discrete operators. The effectiveness of the proposed technique is brought to bear in many practical applications.

Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g. deferred shading, isosurface extraction and surface voxelization in computer graphics and visualization. We present a novel In-Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray-tracing-based visualization of volumetric data. We demonstrate that the proposed In-Kernel compaction outperforms the standard out-of-kernel Thrust parallel-scan method for performing stream compaction in this real-world application. For the data visualization, we also propose a novel multi-kernel ray-tracing pipeline for increased thread coherency and show that it outperforms a conventional single-kernel approach.

Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing, and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g., deferred shading, isosurface extraction, and surface voxelization in computer graphics and visualization. We present a novel In-Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel.

We present a method for accelerating the convergence of continuous non-linear shape optimization algorithms. We start with a general method for constructing gradient vector fields on a manifold, and we analyse this method from a signal processing viewpoint. This analysis reveals that we can construct various filters using the Laplace–Beltrami operator of the shape that can effectively separate the components of the gradient at different scales. We use this idea to adaptively change the scale of features being optimized to arrive at a solution that is optimal across multiple scales. This is in contrast to traditional descent-based methods, for which the rate of convergence often stalls early once the high frequency components have been optimized. We demonstrate how our method can be easily integrated into existing non-linear optimization frameworks such as gradient descent, Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the non-linear conjugate gradient method. We show significant performance improvement for shape optimization in variational shape modelling and parameterization, and we also demonstrate the use of our method for efficient physical simulation.

We present a method for accelerating the convergence of continuous nonlinear shape optimization algorithms. We start with a general method for constructing gradient vector fields on a manifold, and we analyze this method from a signal processing viewpoint. This analysis reveals that we can construct various filters using the Laplace-Beltrami operator of the shape that can effectively separate the components of the gradient at different scales. We use this idea to adaptively change the scale of features being optimized in order to arrive at a solution that is optimal across multiple scales.

Soft shadows play an important role in photo-realistic rendering. Although there are many efficient soft shadow algorithms, most of them focus on the one-side light source situation, where a planar light source is on the outside of the scene. In fact, in many situations, such as games, light sources are omnidirectional. They may be surrounded by a number of 3D objects. This paper proposes a soft shadow algorithm for the omnidirectional situation. We develop a concentric spherical representation to model the behaviour of omnidirectional light sources. To provide better rendering results, a novel summed area table based filtering scheme for spherical functions is proposed. In addition, we utilize unicube mapping, which samples the spherical space more uniformly, to further improve the filtering quality.

Soft shadows play an important role in photo-realistic rendering. Although there are many efficient soft shadow algorithms, most of them focus on the one-side light source situation, where a planar light source is on the outside of the scene. In fact, in many situations, such as games, light sources are omnidirectional. They may be surrounded by a number of 3D objects. This paper proposes a soft shadow algorithm for the omnidirectional situation.

Feature-driven analysis forms the basis of many shape processing tasks, where detected feature points are characterized by local shape descriptors. Such descriptors have so far been defined to capture regions of interest centred at individual points. Using such regions to compare feature points can be problematic when performing partial shape matching, because the region of interest is typically defined as an isotropic neighbourhood around a point, which does not adapt to the geometry of the shape parts. We introduce thebilateral map, a local shape descriptor whose region of interest is defined by two feature points. Compared to the classical descriptor definition using a single point, the bilateral approach exploits the use of a second point to place more constraints on the selection of the spatial context for feature analysis. This leads to a descriptor where the shape of the region of interest adapts to the context of the two points, making it more refined for shape matching. In particular, we show that our new descriptor is more effective for partial matching, because potentially extraneous regions of the models are selectively ignored owing to the adaptive nature of the bilateral map. This property also renders the bilateral map partially insensitive to topological changes. We demonstrate the effectiveness of the bilateral map for partial matching via several correspondence and retrieval experiments and evaluate the results both qualitatively and quantitatively.

Feature-driven analysis forms the basis of many shape processing tasks, where detected feature points are characterized by local shape descriptors. Such descriptors have so far been defined to capture regions of interest centered at individual points. Using such regions to compare feature points can be problematic when performing partial shape matching, since the region of interest is typically defined as an isotropic neighborhood around a point, which does not adapt to the geometry of the shape parts. We introduce the bilateral map, a local shape descriptor whose region of interest is defined by two feature points. Compared to the classical descriptor definition using a single point, the bilateral approach exploits the use of a second point to place more constraints on the selection of the spatial context for feature analysis.

Two-dimensional (2D) parametric colour functions are widely used in Image-Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intensive acquisition process resulting in dense and uniform sampling is not always possible. Conversely, a simpler acquisition process results in sparse, scattered and noisy data on which parametric functions can hardly be fitted without introducing artefacts. Within this context, we present two contributions. The first one is a robust least-squares-based method for fitting 2D parametric colour functions on sparse and scattered data. Our method works for any amount and distribution of acquired data, as well as for any function expressed as a linear combination of basis functions. We tested our fitting for both image-based rendering (surface light fields) and image relighting using polynomials and spherical harmonics. The second one is a statistical analysis to measure the robustness of any fitting method. This measure assesses a trade-off between precision of the fitting and stability with respect to input sampling conditions. This analysis along with visual results confirm that our fitting method is robust and reduces reconstruction artefacts for poorly sampled data while preserving the precision for a dense and uniform sampling.

Generating surface light fields from real acquisition campaigns' data often leads to robustness issues that are due to irregular distribution and sparsity of the photographic sampling. Within this context, we present a robust least-squares-based method for fitting 2D parametric colour functions on sparse and scattered data. Moreover, we provide a statistical analysis to measure the robustness of such fitting approaches. The proposed method allows, on one hand, for high-quality reconstructions in good sampling conditions and, on the other hand, for robust and predictable reconstructions in poor sampling conditions.

This paper presents a GPU-based rendering algorithm for real-time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: (1) adaptive discrete geometric level of detail (LOD), made popping-free by blending visibility samples across the two adjacent geometric levels; (2) adaptive visibility/shading sampling via sample reuse; (3) visibility supersampling via height-field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute-force accumulation buffering.

This paper presents a GPU-based rendering algorithm for real-time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: (1) adaptive discrete geometric level of detail (LOD), made popping-free by blending visibility samples across the two adjacent geometric levels; (2) adaptive visibility/shading sampling via sample reuse; (3) visibility supersampling via height-field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute-force accumulation buffering.

The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called and . only solves for k by marginalizing the image prior, while recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyses the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported failure. Then we show that the leading methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. , although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size. To overcome these limitations, we introduce an inter-scale prior prediction scheme and a principled mechanism for integrating the sharpening filter into . Both qualitative results and extensive quantitative comparisons demonstrate that our algorithm outperforms state-of-the-art methods.

The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called MAP(k) and MAP(x,k). MAP(k) only solves for k by marginalizing the image prior, while MAP(x,k) recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyzes the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported MAP(x,k) failure. Then we show that the leading MAP(x,k) methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. MAP(k), although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size.

Triangle meshes have been nearly ubiquitous in computer graphics, and a large body of data structures and geometry processing algorithms based on them has been developed in the literature. At the same time, quadrilateral meshes, especially semi-regular ones, have advantages for many applications, and significant progress was made in quadrilateral mesh generation and processing during the last several years. In this survey we discuss the advantages and problems of techniques operating on quadrilateral meshes, including surface analysis and mesh quality, simplification, adaptive refinement, alignment with features, parametrisation and remeshing.

Triangle meshes have been nearly ubiquitous in computer graphics, and a large body of data structures and geometry processing algorithms based on them has been developed in the literature. At the same time, quadrilateral meshes, especially semi-regular ones, have advantages for many applications, and significant progress was made in quadrilateral mesh generation and processing during the last several years. In this survey we discuss the advantages and problems of techniques operating on quadrilateral meshes, including surface analysis and mesh quality, simplification, adaptive refinement, alignment with features, parametrization, and remeshing.

The word ‘style’ can be interpreted in so many different ways in so many different contexts. To provide a general analysis and understanding of styles is a highly challenging problem. We pose the open question ‘how to extract styles from geometric shapes?’ and address one instance of the problem. Specifically, we present an unsupervised algorithm for identifying curve styles in a set of shapes. In our setting, a curve style is explicitly represented by a mode of curve features appearing along the 2D silhouettes of the shapes in the set. Unlike previous attempts, we do not rely on any preconceived conceptual characterisations, for example, via specific shape descriptors, to define what is or is not a style. Our definition of styles is data-dependent; it depends on the input set but we do not require computing a shape correspondence across the set. We provide an operational definition of curve styles which focuses on separating curve features that represent styles from curve features that are content revealing. To this end, we develop a novel formulation and associated algorithm for style-content separation. The analysis is based on a feature-shape association matrix (FSM) whose rows correspond to modes of curve features, columns to shapes in the set, and each entry expresses the extent a feature mode is present in a shape. We make several assumptions to drive style-content separation which only involve properties of, and relations between, rows of the FSM. Computationally, our algorithm only requires row-wise correlation analysis in the FSM and a heuristic solution of an instance of the set cover problem. Results are demonstrated on several data sets showing the identification of curve styles. We also develop and demonstrate several style-related applications including style exaggeration, removal, blending, and style transfer for 2D shape synthesis.

The word “style” can be interpreted in so many different ways in so many different contexts. To provide a general analysis and understanding of styles is a highly challenging problem. We pose the open question “how to extract styles from geometric shapes?” and address one instance of the problem. Specifically, we present an unsupervised algorithm for identifying curve styles in a set of shapes. In our setting, a curve style is explicitly represented by a mode of curve features appearing along the 2D silhouettes of the shapes in the set. Unlike previous attempts, we do not rely on any preconceived conceptual characterizations, e.g., via specific shape descriptors, to define what is or is not a style. Our definition of styles is data-dependent; it depends on the input set but we do not require computing a shape correspondence across the set. We provide an operational definition of curve styles which focuses on separating curve features that represent styles from curve features that are content-revealing.

We present a new model for creating composite visualizations of multidimensional data sets using simple visual representations such as point charts, scatterplots and parallel coordinates as components. Each visual representation is contained in a tile, and the tiles are arranged in a mosaic of views using a space-filling slice-and-dice layout. Tiles can be created, resized, split or merged using a versatile set of interaction techniques, and the visual representation of individual tiles can also be dynamically changed to another representation. Because each tile is self-contained and independent, it can be implemented in any programming language, on any platform and using any visual representation. We also propose a formalism for expressing visualization mosaics. A Web-based implementation called MosaicJS supporting multidimensional visual exploration showcases the versatility of the concept and illustrates how it can be used to integrate visualization components provided by different toolkits.

We present a new model for creating composite visualizations of multidimensional datasets using simple visual representations such as point charts, scatterplots, and parallel coordinates as components. Each visual representation is contained in a tile, and the tiles are arranged in a mosaic of views using a space-filling slice-and-dice layout. Tiles can be created, resized, split, or merged using a versatile set of interaction techniques, and the visual representation of individual tiles can also be dynamically changed to another representation. Because each tile is self-contained and independent, it can be implemented in any programming language, on any platform, and using any visual representation.

The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find, extract, encode and exploit geometric symmetries and high-level structural information for a wide variety of geometry processing tasks. This report surveys and classifies recent developments in symmetry detection. We focus on elucidating the key similarities and differences between existing methods to gain a better understanding of a fundamental problem in digital geometry processing and shape understanding in general. We discuss a variety of applications in computer graphics and geometry processing that benefit from symmetry information for more effective processing. An analysis of the strengths and limitations of existing algorithms highlights the plenitude of opportunities for future research both in terms of theory and applications.

The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find, extract, encode, and exploit geometric symmetries and high-level structural information for a wide variety of geometry processing tasks. This report surveys and classifies recent developments in symmetry detection. We focus on elucidating the key similarities and differences between existing methods to gain a better understanding of a fundamental problem in digital geometry processing and shape understanding in general.

The propagation and reflection of electromagnetic waves in a three-dimensional environment is simulated, and realistic images are produced using the resulting light distributions and reflectance functions. A finite difference time domain method is employed to advance the electric and magnetic fields in a scene. Surfaces containing wavelength scaled structures are created, the interaction of the electromagnetic waves with these nano-structured materials is calculated, and the sub-surface interference and diffraction effects are modelled. The result is a reflectance function with wavelength composition and spatial distribution properties that could not have been predicted using classic computer graphic ray tracing approaches. The techniques are employed to reproduce demonstrations of simple interference and diffraction effects, and to create computer-generated pictures of a Morpho butterfly.

The propagation and reflection of electromagnetic waves in a three-dimensional environment is simulated, and realistic images are produced using the resulting light distributions and reflectance functions. A finite difference time domain method is employed to advance the electric and magnetic fields in a scene. Surfaces containing wavelength scaled structures are created, the interaction of the electromagnetic waves with these nano-structured materials is calculated, and the sub-surface interference and diffraction effects are modeled. The result is a reflectance function with wavelength composition and spatial distribution properties that could not have been predicted using classic computer graphic ray tracing approaches.

Smoke simulation on a large grid is quite time consuming and most of the computation time is spent on the projection step. We present a novel projection method which produces quite similar visual results as those produced with the traditional projection method, but uses much less computation time. Our method includes two steps: detail-capture and shape-correction. The first step preserves most of the smoke details using an efficient DST (Discrete Sine Transformation) Poisson Solver with auxiliary boundary sweeping. The second step maintains the overall flow shape by solving a correcting Poisson equation on a coarse grid. Our algorithm is very fast and quite easy to implement. Experiments show that our projection is approximately 10–30 times faster than the traditional projection with PCG(Preconditioned Conjugate Gradient), while convincingly preserving both the flow details and the overall shape of the smoke.

Ray tracing has become a viable alternative to rasterization for interactive applications and also forms the basis of most global illumination methods. However, even today's fastest ray-tracers offer only a tight budget of rays per pixel per frame. Rendering performance can be improved by increasing this budget, or by developing methods that use it more efficiently. In this paper we propose a global visibility caching algorithm that reduces the number of shadow rays required for shading to a fraction of less than 2% in some cases. We quantize the visibility function's domain while ensuring a minimal degradation of the final image quality. To control the introduced error, we adapt the quantization locally, accounting for variations in geometry, sampling densities on both endpoints of the visibility queries, and the light signal itself. Compared to previous approaches for approximating visibility, e.g. shadow mapping, our method has several advantages: (1) it allows caching of arbitrary visibility queries between surface points and is thus applicable to all ray tracing based methods; (2) the approximation error is uniform over the entire image and can be bounded by a user-specified parameter; (3) the cache is created on-the-fly and does not waste any resources on queries that will never be used. We demonstrate the benefits of our method on Whitted-style ray tracing combined with instant radiosity, as well as an integration with bidirectional path tracing.

This paper presents a parallel, implementation-friendly analytic visibility method for triangular meshes. Together with an analytic filter convolution, it allows for a fully analytic solution to anti-aliased 3D mesh rendering on parallel hardware. Building on recent works in computational geometry, we present a new edge-triangle intersection algorithm and a novel method to complete the boundaries of all visible triangle regions after a hidden line elimination step. All stages of the method are embarrassingly parallel and easily implementable on parallel hardware. A GPU implementation is discussed and performance characteristics of the method are shown and compared to traditional sampling-based rendering methods.

We propose the primitive tree, a novel and compact space-partition method that samples and reconstructs a distance field with high accuracy, even for regions far from the surfaces. The primitive tree is based on the octree and stores the indices of the nearest primitives in its leaf nodes. Most previous approaches have involved a trade-off between accuracy and speed in distance queries, but our method can improve both aspects simultaneously. In addition, our method can sample unsigned distance fields effectively, even for self-intersecting and nonmanifold models. We present test results showing that our method can sample and represent large scenes, with more than ten million triangles, rapidly and accurately.

We argue that full surface correspondence (registration) and optimal deformations (geodesics) are two related problems and propose a framework that solves them simultaneously. We build on the Riemannian shape analysis of anatomical and star-shaped surfaces of Kurtek et al. and focus on articulated complex shapes that undergo elastic deformations and that may contain missing parts. Our core contribution is the re-formulation of Kurtek et al.'s approach as a constrained optimization over all possible re-parameterizations of the surfaces, using a sparse set of corresponding landmarks. We introduce a landmark-constrained basis, which we use to numerically solve this optimization and therefore establish full surface registration and geodesic deformation between two surfaces. The length of the geodesic provides a measure of dissimilarity between surfaces. The advantages of this approach are: (1) simultaneous computation of full correspondence and geodesic between two surfaces, given a sparse set of matching landmarks (2) ability to handle more comprehensive deformations than nearly isometric, and (3) the geodesics and the geodesic lengths can be further used for symmetrizing 3D shapes and for computing their statistical averages. We validate the framework on challenging cases of large isometric and elastic deformations, and on surfaces with missing parts. We also provide multiple examples of averaging and symmetrizing 3D models.

The use of Laplacian eigenbases has been shown to be fruitful in many computer graphics applications. Today, state-of-the-art approaches to shape analysis, synthesis, and correspondence rely on these natural harmonic bases that allow using classical tools from harmonic analysis on manifolds. However, many applications involving multiple shapes are obstacled by the fact that Laplacian eigenbases computed independently on different shapes are often incompatible with each other. In this paper, we propose the construction of common approximate eigenbases for multiple shapes using approximate joint diagonalization algorithms, taking as input a set of corresponding functions (e.g. indicator functions of stable regions) on the two shapes. We illustrate the benefits of the proposed approach on tasks from shape editing, pose transfer, correspondence, and similarity.

Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper.

We present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; no descriptors are provided so the correspondence between the regions is not know, nor we know how many regions correspond in the two shapes. We show that even with such scarce information, it is possible to establish very accurate correspondence between the shapes by using methods from the field of sparse modeling, being this, the first non-trivial use of sparse models in shape correspondence. We formulate the problem of permuted sparse coding, in which we solve simultaneously for an unknown permutation ordering the regions on two shapes and for an unknown correspondence in functional representation. We also propose a robust variant capable of handling incomplete matches. Numerically, the problem is solved efficiently by alternating the solution of a linear assignment and a sparse coding problem. The proposed methods are evaluated qualitatively and quantitatively on standard benchmarks containing both synthetic and scanned objects.

We present a caching framework with a novel probability-based prefetching and eviction strategy applied to atomic cache units that enables interactive rendering of gigaray light fields. Further, we describe two new use cases that are supported by our framework: panoramic light fields, including a robust imaging technique and an appropriate parameterization scheme for real-time rendering and caching; and light-field-cached volume rendering, which supports interactive exploration of large volumetric datasets using light-field rendering. We consider applications such as light-field photography and the visualization of large image stacks from modern scanning microscopes.

We present an algorithm for realtime rendering of large-scale city models with procedurally generated facades. By using highly detailed assets like windows, doors, and decoration such city models can provide an extremely high geometric level of detail but on the downside they also consist of billions of polygons which makes it infeasible to even store them as explicit polygonal meshes. Moreover, when rendering urban scenes usually only a very small fraction of the city is actually visible which calls for effective culling mechanisms. For procedural textures there are efficient screen space techniques that evaluate, e.g., a split grammar on a per-pixel basis in the fragment shader and thus render a textured facade in a view dependent manner. We take this idea further by introducing 3D geometric detail in addition to flat textures. Our approach is a two-pass procedure that first renders a flat procedural facade. During rasterization the fragment shader triggers the instantiation of a detailed asset whenever a geometric facade element is potentially visible. The set of instantiated detail models are then rendered in a second pass. The major challenges arise from the fact that geometric details belonging to a facade can be visible even if the base polygon of the facade itself is not visible. Hence we propose measures to conservatively estimate visibility without introducing excessive redundancy. We further extend our technique by a simple level of detail mechanism that switches to baked textures (of the assets) depending on the distance to the camera. We demonstrate that our technique achieves realtime frame rates for large-scale city models with massive detail on current commodity graphics hardware.

Focus misses are common in image capture, such as when the camera or the subject moves rapidly in sports and macro photography. One option to sharpen focus-missed photographs is through single image deconvolution, but high-frequency data cannot be fully recovered; therefore, artifacts such as ringing and amplified noise become apparent. We propose a new method that uses assisting, similar but different, sharp image(s) provided by the user (such as multiple images of the same subject in different positions captured using a burst of photographs). Our first contribution is to theoretically analyze the errors in three sources of data—a slightly sharpened original input image that we call the target, single image deconvolution with an aggressive inverse filter, and warped assisting image(s) registered using optical flow. We show that these three sources have different error characteristics, depending on image location and frequency band (for example, aggressive deconvolution is more accurate in high-frequency regions like edges). Next, we describe a practical method to compute these errors, given we have no ground truth and cannot easily work in the Fourier domain. Finally, we select the best source of data for a given pixel and scale in the Laplacian pyramid. We accurately transfer high-frequency data to the input, while minimizing artifacts. We demonstrate sharpened results on out-of-focus images in macro, sports, portrait and wildlife photography.

We present a method of analytically rasterizing shapes that have curved boundaries and linear color gradients using piecewise polynomial prefilters. By transforming the convolution of filters with the image from an integral over area into a boundary integral, we find closed-form expressions for rasterizing shapes. We show that a polynomial expression can be used to rasterize any combination of polynomial curves and filters. Our rasterizer also handles rational quadratic boundaries, which allows us to evaluate circles and ellipses. We apply our technique to rasterizing vector graphics and show that our derivation gives an efficient implementation as a scanline rasterizer.

We introduce the Sifted Disk technique for locally resampling a point cloud in order to reduce the number of points. Two neighboring points are removed and we attempt to find a single random point that is sufficient to replace them both. The resampling respects the original sizing function; In that sense it is not a coarsening. The angle and edge length guarantees of a Delaunay triangulation of the points are preserved. The sifted point cloud is still suitable for texture synthesis because the Fourier spectrum is largely unchanged. We provide an efficient algorithm, and demonstrate that sifting uniform Maximal Poisson-disk Sampling (MPS) and Delaunay Refinement (DR) points reduces the number of points by about 25%, and achieves a density about 1/3 more than the theoretical minimum. We show two-dimensional stippling and meshing applications to demonstrate the significance of the concept.