Spherical Harmonics (SH) are fundamental mathematical tools for compactly representing low-frequency spherical functions in computer graphics. Evaluating products of SH-represented functions is a core operation in many rendering algorithms and often becomes a major computational bottleneck. Existing SH product methods face two key challenges: insufficient computational efficiency, and the inflexible requirement that all input functions share the same SH order. To overcome these limitations, we introduce the Generalized Spherical Harmonics Product , a novel formulation that supports inputs with different SH orders and allows flexible truncation of the output. We further propose an efficient and practical computation approach based on Spherical Grids (SphGrid). We establish explicit bidirectional conversions between SphGrid representations and SH coefficients, thus enabling SphGrid to serve as an intermediate representation. Under this framework, SH product computation is reduced to simple point-wise multiplications on the grid. We then derive sufficient conditions on the grid resolution to guarantee exact computation. Using lower resolutions yields an approximate variant that provides a favorable trade-off between accuracy and efficiency. Our approach is not only more general but also demonstrates superior performance even for the specialized case of identical input orders. In particular, our accurate method achieves a 3.5–6.0× speedup over the state-of-the-art method in a practical setting for graphics, while our approximate variant yields lower errors and a 2.5–6.5× speedup over the state-of-the-art approximate method. We rigorously analyze the correctness and efficiency of our method and demonstrate its effectiveness in real-time rendering applications.

Grid-free Monte Carlo methods are capable of solving Poisson equations on highly complex domains. However, existing methods operate solely in the primal domain and can converge slowly due to high variance. Inspired by gradient-domain rendering, we introduce a gradient-domain framework for Poisson problems. Specifically, we devise a new Monte Carlo estimator that directly targets differences of the solution between spatially varying query locations. Further, we adopt state-of-the-art reconstruction techniques originated in gradient-domain rendering to allow efficient reconstruction of the solutions without incurring additional bias. We demonstrate the effectiveness of our technique by comparing solutions obtained using our method and several state-of-the-art baselines.

Color grading, as a crucial step in film post-production, plays an important role in emotional expression and artistic enhancement. Recently, a geometric palette-based approach to video recoloring has been introduced with impressive results. It offers an intuitive interface that allows users to alter the color of a video by manipulating a limited set of representative colors. However, this method has two primary limitations. Firstly, palette extraction is computationally expensive, often taking more than one hour to generate palettes even for medium-length videos, which significantly limits the practical application of color editing for longer videos. Secondly, the palette colors are less representative, and some primary colors may be omitted from the resulting palettes during topological simplification, making it less intuitive in color editing. To overcome these limitations, in this paper, we propose a novel approach to video recoloring. The core of our method is a set of Bézier curves that connect the dominant colors throughout the input video. By slicing these Bézier curves in RGBT space, per-frame palette can be naturally derived. During recoloring, users can select several frames of interest and modify their corresponding palettes to change the color of the video. Our method is simple and intuitive, enabling compelling time-varying recoloring results. Compared to existing methods, our approach is more efficient in palette extraction and can effectively capture the dominant colors of the video. Extensive experiments demonstrate the effectiveness of our method.

Gradient-domain rendering estimates image-space gradients using correlated sampling, which can be combined with color information to reconstruct smoother and less noisy images. While simple ℒ 2 reconstruction is unbiased, it often leads to visible artifacts. In contrast, most recent reconstruction methods based on learned or handcrafted techniques improve visual quality but introduce bias, leaving the development of practically unbiased reconstruction approaches relatively underexplored. In this work, we propose a generalized framework for unbiased reconstruction in gradient-domain rendering. We first derive the unbiasedness condition under a general formulation that linearly combines pixel colors and gradients. Based on this unbiasedness condition, we design a practical algorithm 1 that minimizes image variance while strictly satisfying unbiasedness. Experimental results demonstrate that our method not only guarantees unbiasedness but also achieves superior quality compared to existing unbiased and slightly biased reconstruction methods.

Rendering realistic appearances of homogeneous translucent materials, such as milk and marble, poses challenges due to the complexity of subsurface scattering. In this paper, we present a neural method for real-time rendering of homogeneous translucent objects. Based on the observation that light propagation inside a highly scattered media is like a diffusion process (Stam 1995), we propose a neural data structure named diffusion block to mimic the behavior of the diffusion process. The diffusion block is built upon a recent network structure named DiffusionNet (Sharp et al. 2022) with a few modifications to adapt to our problem of translucent rendering. Our network is lightweight and efficient, leading to a real-time rendering method. Furthermore, our method supports dynamic material properties and diverse lighting conditions. Comparisons with state-of-the-art real-time translucent rendering methods demonstrate the superiority of our method in rendering quality.

Photon mapping is a fundamental and practical Monte Carlo rendering technique for efficiently simulating global illumination effects, especially for caustics and specular-diffuse-specular (SDS) paths. In this paper, we present the first differentiable rendering method for photon mapping. The core of our method is a newly introduced concept named generalized path gradients. Based on the extended path space manifolds (EPSMs) [Xing et al. 2023], the generalized path gradients define the derivatives of the vertex positions and color contributions of a path with respect to scene parameters under given geometric constraints. By formalizing photon mapping as a path sampling technique through vertex merging [Georgiev et al. 2012] and incorporating a smooth differentiable density estimation kernel, we enable the differentiation of the photon mapping algorithms based on the theoretical results of generalized path gradients. Experiments demonstrate that our method is more effective than state-of-the-art physics-based differentiable rendering methods in inverse rendering applications involving difficult illumination paths, especially SDS paths.

We introduce coroutines into GPU kernel programming, providing an automated solution for flexible splitting and scheduling of rendering tasks. This approach addresses a prevalent challenge in harnessing the power of modern GPUs for complex, imbalanced graphics workloads like path tracing. Usually, to accommodate the SIMT execution model and latency-hiding architecture, developers have to decompose a monolithic mega-kernel into smaller sub-tasks for improved thread coherence and reduced register pressure. However, involving the handling of intricate nested control flows and numerous interdependent program states, this process can be exceedingly tedious and error-prone when performed manually. Coroutines, a building block for asynchronous programming in many high-level CPU languages, exhibit untapped potential for restructuring GPU kernels due to their versatility in control representation. By extending Luisa [Zheng et al. 2022], we implement an asymmetric, stackless coroutine model with programming language support and multiple built-in schedulers for modern GPUs. To showcase the effectiveness of our model and implementation, we examine them in different application scenarios, including path tracing, SDF rendering, and incorporation with custom passes.

Physically based differentiable rendering has become an increasingly important topic in recent years. A common pipeline computes local color derivatives of light paths or pixels with respect to arbitrary scene parameters, and enables optimizing or recovering the scene parameters through iterative gradient descent by minimizing the difference between rendered and target images. However, existing approaches cannot robustly handle complex illumination effects including reflections, refractions, caustics, shadows, and highlights, especially when the initial and target locations of such illumination effects are not close to each other in the image space.

A key advantage of vector graphics over raster graphics is their editability. For example, linear gradients define a spatially varying color fill with a few intuitive parameters, which are ubiquitously supported in standard vector graphics formats and libraries. By layering regions filled with linear gradients, complex appearances can be created. We propose an automatic method to convert a raster image into layered regions of linear gradients. Given an input raster image segmented into regions, our approach decomposes the resulting regions into opaque and semi-transparent linear gradient fills. Our approach is fully automatic (e.g., users do not identify a background as in previous approaches) and exhaustively considers all possible decompositions that satisfy perceptual cues. Experiments on a variety of images demonstrate that our method is robust and effective.

We propose neural global illumination, a novel method for fast rendering full global illumination in static scenes with dynamic viewpoint and area lighting. The key idea of our method is to utilize a deep rendering network to model the complex mapping from each shading point to global illumination. To efficiently learn the mapping, we propose a neural-network-friendly input representation including attributes of each shading point, viewpoint information, and a combinational lighting representation that enables high-quality fitting with a compact neural network. To synthesize high-frequency global illumination effects, we transform the low-dimension input to higher-dimension space by positional encoding and model the rendering network as a deep fully-connected network. Besides, we feed a screen-space neural buffer to our rendering network to share global information between objects in the screen-space to each shading point. We have demonstrated our neural global illumination method in rendering a wide variety of scenes exhibiting complex and all-frequency global illumination effects such as multiple-bounce glossy interreflection, color bleeding, and caustics.

Recent years have witnessed the increasing popularity of learning-based methods to enhance the color and tone of images. Although these methods achieve satisfying performance on static images, it is non-trivial to extend such image-to-image methods to handle videos. A straight extension would easily lead to computation inefficiency or distracting flickering effects. In this paper, we propose a novel image-to-video model enforcing the temporal stability for real-time video enhancement, which is trained using only static images. Specifically, we first propose a lightweight image enhancer via learnable flexible 2-dimensional lookup tables (F2D LUTs), which can consider scenario information adaptively. To impose temporal constancy, we further propose to infer the motion fields via a virtual camera motion engine, which can be utilized to stabilize the image-to-video model with temporal consistency loss. Experimental results show that our image-to-video model not only achieves the state-of-the-art performance on the image enhancement task, but also performs favorably against baselines on the video enhancement task. Our source code is available at https://github.com/shedy-pub/I2VEnhance.

Caustics are interesting patterns caused by the light being focused when reflecting off glossy materials. Rendering them in computer graphics is still challenging: they correspond to high luminous intensity focused over a small area. Finding the paths that contribute to this small area is difficult, and even more difficult when using camera-based path tracing instead of bidirectional approaches. Recent improvements in path guiding are still unable to compute efficiently the light paths that contribute to a caustic. In this paper, we present a novel path guiding approach to enable reliable rendering of caustics. Our approach relies on computing representative specular paths, then extending them using a chain of spherical Gaussians. We use these extended paths to estimate the incident radiance distribution and guide path tracing. We combine this approach with several practical strategies, such as spatial reusing and parallax-aware representation for arbitrarily curved reflectors. Our path-guided algorithm using extended specular paths outperforms current state-of-the-art methods and handles multiple bounces of light and a variety of scenes.

Once deployed, a decentralized blockchain system ensures that it will operate faithfully so that no one can interfere with or manipulate its predefined regulations, such as block size and block creation interval investigated in this paper. However, fixed regulations prevent that system from adapting to the change of the environment, such as increasing the underlying network capacity, and result in sub-optimal performance. For example, Bitcoin remains at 7 TPS (transactions per second), even operating over the current Internet. In this paper, we propose a new paradigm for defining the behavior of a consensus system, named as Meta-Regulation, which allows autonomous evolution of the system behavior. A meta-regulation adjusts the actual behavior of a consensus system in response to the changing capacity of the underlying infrastructure and the community of participants. We demonstrate the effectiveness of the proposed meta-regulation by achieving significantly improved throughput and latency for Bitcoin, adapted to the current capacity of the Internet. Our experimental results show that Meta-Regulation can achieve at least$7\times $performance improvement over Bitcoin network deployed in 2009, resulting in 49.7 TPS or 68% reduction confirmation latency by fully utilizing the bandwidth and the computing power of average network nodes.

Traditional differentiable rendering approaches are usually hard to converge in inverse rendering optimizations, especially when initial and target object locations are not so close. Inspired by Lagrangian fluid simulation, we present a novel differentiable rendering method to address this problem. We associate each screen-space pixel with the visible 3D geometric point covered by the center of the pixel and compute derivatives on geometric points rather than on pixels. We refer to the associated geometric points as point proxies of pixels. For each point proxy, we compute its 5D RGBXY derivatives which measures how its 3D RGB color and 2D projected screen-space position change with respect to scene parameters. Furthermore, in order to capture global and long-range object motions, we utilize optimal transport based pixel matching to design a more sophisticated loss function. We have conducted experiments to evaluate the effectiveness of our proposed method on various inverse rendering applications and have demonstrated superior convergence behavior compared to state-of-the-art baselines.

The advancements in hardware have drawn more attention than ever to high-quality offline rendering with modern stream processors, both in the industry and in research fields. However, the graphics APIs are fragmented and existing shading languages lack high-level constructs such as polymorphism, which adds complexity to developing and maintaining cross-platform high-performance renderers. We present LuisaRender 1 , a high-performance rendering framework for modern stream-architecture hardware. Our main contribution is an expressive C++-embedded DSL for kernel programming with JIT code generation and compilation. We also implement a unified runtime layer with resource wrappers and an optimized Monte Carlo renderer. Experiments on test scenes show that LuisaRender achieves much higher performance than existing research renderers on modern graphics hardware, e.g., 5--11× faster than PBRT-v4 and 4--16× faster than Mitsuba 3.

Accurate camera pose estimation is essential and challenging for real world dynamic 3D reconstruction and augmented reality applications. In this article, we present a novel RGB-D SLAM approach for accurate camera pose tracking in dynamic environments. Previous methods detect dynamic components only across a short time-span of consecutive frames. Instead, we provide a more accurate dynamic 3D landmark detection method, followed by the use of long-term consistency via conditional random fields, which leverages long-term observations from multiple frames. Specifically, we first introduce an efficient initial camera pose estimation method based on distinguishing dynamic from static points using graph-cut RANSAC. These static/dynamic labels are used as priors for the unary potential in the conditional random fields, which further improves the accuracy of dynamic 3D landmark detection. Evaluation using the TUM and Bonn RGB-D dynamic datasets shows that our approach significantly outperforms state-of-the-art methods, providing much more accurate camera trajectory estimation in a variety of highly dynamic environments. We also show that dynamic 3D reconstruction can benefit from the camera poses estimated by our RGB-D SLAM approach.

Physically correct, noise-free global illumination is crucial in physically-based rendering, but often takes a long time to compute. Recent approaches have exploited sparse sampling and filtering to accelerate this process but still cannot achieve interactive performance. It is partly due to the time-consuming ray sampling even at 1 sample per pixel, and partly because of the complexity of deep neural networks. To address this problem, we propose a novel method to generate plausible single-bounce indirect illumination for dynamic scenes in interactive framerates. In our method, we first compute direct illumination and then use a lightweight neural network to predict screen space indirect illumination. Our neural network is designed explicitly with bilateral convolution layers and takes only essential information as input (direct illumination, surface normals, and 3D positions). Also, our network maintains the coherence between adjacent image frames efficiently without heavy recurrent connections. Compared to state-of-the-art works, our method produces single-bounce indirect illumination of dynamic scenes with higher quality and better temporal coherence and runs at interactive framerates.

Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a strong correlation between image layouts and perceived image quality. While previous state-of-the-art methods attempt to learn holistic information using deep Convolutional Neural Networks (CNNs), our approach is motivated by the fact that Graph Convolutional Network (GCN) architecture is conceivably more suited for modeling complex relations among image regions than vanilla convolutional layers. Specifically, we present a Hierarchical Layout-Aware Graph Convolutional Network (HLA-GCN) to capture layout information. It is a dedicated double-subnet neural network consisting of two LA-GCN modules. The first LA-GCN module constructs an aesthetics-related graph in the coordinate space and performs reasoning over spatial nodes. The second LA-GCN module performs graph reasoning after aggregating significant regions in a latent space. The model output is a hierarchical representation with layout-aware features from both spatial and aggregated nodes for unified aesthetics assessment. Extensive evaluations show that our proposed model outperforms the state-of-the-art on the AVA and AADB datasets across three different tasks. The code is available at http://github.com/days1011/HLAGCN.

Color correction and color grading are important steps in film production. Recent palette-based approaches to image recoloring have shown that a small set of representative colors provide an intuitive set of handles for color adjustment. However, a single, static palette cannot represent the time-varying colors in a video. We introduce a spatial-temporal geometry-based approach to video recoloring. Specifically, its core is a 4D skew polytope with a few vertices that approximately encloses the video pixels in color and time, which implicitly defines time-varying palettes through slicing of the 4D skew polytope at specific time values. Our geometric palette is compact, descriptive, and provides a correspondence between colors throughout the video, including topological changes when colors merge or split. Experiments show that our method produces natural, artifact-free recoloring.

Spherical Harmonics (SH) have been proven as a powerful tool for rendering, especially in real-time applications such as Precomputed Radiance Transfer (PRT). Spherical harmonics are orthonormal basis functions and are efficient in computing dot products. However, computations of triple product and multiple product operations are often the bottlenecks that prevent moderately high-frequency use of spherical harmonics. Specifically state-of-the-art methods for accurate SH triple products of order n have a time complexity of O ( n 5 ), which is a heavy burden for most real-time applications. Even worse, a brute-force way to compute k -multiple products would take O ( n 2 k ) time. In this paper, we propose a fast and accurate method for spherical harmonics triple products with the time complexity of only O ( n 3 ), and further extend it for computing k -multiple products with the time complexity of O ( kn 3 + k 2 n 2 log ( kn )). Our key insight is to conduct the triple and multiple products in the Fourier space, in which the multiplications can be performed much more efficiently. To our knowledge, our method is theoretically the fastest for accurate spherical harmonics triple and multiple products. And in practice, we demonstrate the efficiency of our method in rendering applications including mid-frequency relighting and shadow fields.

Various denoising methods have been proposed to clean up the noise in Monte Carlo (MC) renderings, each having different advantages, disadvantages, and applicable scenarios. In this paper, we present Ensemble Denoising , an optimization-based technique that combines multiple individual MC denoisers. The combined image is modeled as a per-pixel weighted sum of output images from the individual denoisers. Computation of the optimal weights is formulated as a constrained quadratic programming problem, where we apply a dual-buffer strategy to estimate the overall MSE. We further propose an iterative solver to overcome practical issues involved in the optimization. Besides nice theoretical properties, our ensemble denoiser is demonstrated to be effective and robust, and outperforms any individual denoiser across dozens of scenes and different levels of sample rates. We also perform a comprehensive analysis on the selection of individual denoisers to be combined, providing important and practical guides for users.

We present deferred neural lighting, a novel method for free-viewpoint relighting from unstructured photographs of a scene captured with handheld devices. Our method leverages a scene-dependent neural rendering network for relighting a rough geometric proxy with learnable neural textures. Key to making the rendering network lighting aware are radiance cues: global illumination renderings of a rough proxy geometry of the scene for a small set of basis materials and lit by the target lighting. As such, the light transport through the scene is never explicitely modeled, but resolved at rendering time by a neural rendering network. We demonstrate that the neural textures and neural renderer can be trained end-to-end from unstructured photographs captured with a double hand-held camera setup that concurrently captures the scene while being lit by only one of the cameras' flash lights. In addition, we propose a novel augmentation refinement strategy that exploits the linearity of light transport to extend the relighting capabilities of the neural rendering network to support other lighting types (e.g., environment lighting) beyond the lighting used during acquisition (i.e., flash lighting). We demonstrate our deferred neural lighting solution on a variety of real-world and synthetic scenes exhibiting a wide range of material properties, light transport effects, and geometrical complexity.

Abstract Many‐light rendering is becoming more common and important as rendering goes into the next level of complexity. However, to calculate the illumination under many lights, state of the art algorithms are still far from efficient, due to the separate consideration of light sampling and BRDF sampling. To deal with the inefficiency of many‐light rendering, we present a novel light sampling method named BRDF‐oriented light sampling, which selects lights based on importance values estimated using the BRDF's contributions. Our BRDF‐oriented light sampling method works naturally with MIS, and allows us to dynamically determine the number of samples allocated for different sampling techniques. With our method, we can achieve a significantly faster convergence to the ground truth results, both perceptually and numerically, as compared to previous many‐light rendering algorithms.

A quick-response code (QR code) is a two-dimensional code akin to a barcode that encodes a message of limited length. In this paper, we present a variant of QR code, a two-layer QR code. Its two-layer structure can display two alternative messages when scanned from two different directions. We propose a method to generate such two-layer QR codes encoding two given messages in a few seconds. We also demonstrate the robustness of our method on both synthetic and fabricated examples. All source code will be made publicly available (https://github.com/yuantailing/two-layer-qrcode).

In this paper we present a unified deep inverse rendering framework for estimating the spatially-varying appearance properties of a planar exemplar from an arbitrary number of input photographs, ranging from just a single photograph to many photographs. The precision of the estimated appearance scales from plausible when the input photographs fails to capture all the reflectance information, to accurate for large input sets. A key distinguishing feature of our framework is that it directly optimizes for the appearance parameters in a latent embedded space of spatially-varying appearance, such that no handcrafted heuristics are needed to regularize the optimization. This latent embedding is learned through a fully convolutional auto-encoder that has been designed to regularize the optimization. Our framework not only supports an arbitrary number of input photographs, but also at high resolution. We demonstrate and evaluate our deep inverse rendering solution on a wide variety of publicly available datasets.

Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to achieve better rendering quality with less computational cost. However, most of these methods rely on handcrafted optimization objectives, which lead to artifacts such as blurs and unfaithful details. In this paper, we present an adversarial approach for denoising Monte Carlo rendering. Our key insight is that generative adversarial networks can help denoiser networks to produce more realistic high-frequency details and global illumination by learning the distribution from a set of high-quality Monte Carlo path tracing images. We also adapt a novel feature modulation method to utilize auxiliary features better, including normal, albedo and depth. Compared to previous state-of-the-art methods, our approach produces a better reconstruction of the Monte Carlo integral from a few samples, performs more robustly at different sample rates, and takes only a second for megapixel images.

We present the first computational tool to help ordinary users create transforming pop-up books. In each transforming pop-up, when the user pulls a tab, an initial flat two-dimensional (2D) pattern, i.e., a 2D shape with a superimposed picture, such as an airplane, turns into a new 2D pattern, such as a robot. Given the two 2D patterns, our approach automatically computes a 3D pop-up mechanism that transforms one pattern into the other; it also outputs a design blueprint, allowing the user to easily make the final model. We also present a theoretical analysis of basic transformation mechanisms; combining these basic mechanisms allows more flexibility of final designs. Using our approach, inexperienced users can create models in a short time; previously, even experienced artists often took weeks to manually create them. We demonstrate our method on a variety of real-world examples.

Surface flow phenomena, such as rain water flowing down a tree trunk and progressive water front in a shower room, are common in real life. However, compared with the 3D spatial fluid flow, these surface flow problems have been much less studied in the graphics community. To tackle this research gap, we present an efficient, robust and high-fidelity simulation approach based on the shallow-water equations. Specifically, the standard shallow-water flow model is extended to general triangle meshes with a feature-based bottom friction model, and a series of coherent mathematical formulations are derived to represent the full range of physical effects that are important for real-world surface flow phenomena. In addition, by achieving compatibility with existing 3D fluid simulators and by supporting physically realistic interactions with multiple fluids and solid surfaces, the new model is flexible and readily extensible for coupled phenomena. A wide range of simulation examples are presented to demonstrate the performance of the new approach.

In this paper, we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function, which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on the opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm, which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified median-cut method. Compared with previous approaches, our method can produce high-quality results with a fewer visual artifacts but also requires significantly less computational effort.

Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images.

Photon mapping is a widely used technique for global illumination rendering. In the density estimation step of photon mapping, the indirect radiance at a shading point is estimated through a filtering process using nearby stored photons; an isotropic filtering kernel is usually used. However, using an isotropic kernel is not always the optimal choice, especially for cases when eye paths intersect with surfaces with anisotropic BRDFs. In this paper, we propose an anisotropic filtering kernel for density estimation to handle such anisotropic eye paths. The anisotropic filtering kernel is derived from the recently introduced anisotropic spherical Gaussian representation of BRDFs. Compared to conventional photon mapping, our method is able to reduce rendering errors with negligible additional cost when rendering scenes containing anisotropic BRDFs.

Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator , a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules , which describe typical material patterns for a small group of objects or parts, and global aesthetic rules , which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.

Algorithms for rendering interreflection (or indirect illumination) effects often make assumptions about the frequency range of the materials' reflectance properties. For example, methods based on Virtual Point Lights (VPLs) perform well for diffuse and semi-glossy materials but not so for highly glossy or specular materials; the situation is reversed for methods based on ray tracing. In this article, we present a practical algorithm for rendering interreflection effects with all-frequency BRDFs. Our method builds upon a spherical Gaussian representation of the BRDF, based on which a novel mathematical development of the interreflection equation is made. This allows us to efficiently compute one-bounce interreflection from a triangle to a shading point, by using an analytic formula combined with a piecewise linear approximation. We show through evaluation that this method is accurate for a wide range of BRDFs. We further introduce a hierarchical integration method to handle complex scenes (i.e., many triangles) with bounded errors. Finally, we have implemented the present algorithm on the GPU, achieving rendering performance ranging from near interactive to a few seconds per frame for various scenes with different complexity.

We study the problem of inverse image editing , which recovers a semantically-meaningful editing history from a source image and an edited copy. Our approach supports a wide range of commonly-used editing operations such as cropping, object insertion and removal, linear and non-linear color transformations, and spatially-varying adjustment brushes. Given an input image pair, we first apply a dense correspondence method between them to match edited image regions with their sources. For each edited region, we determine geometric and semantic appearance operations that have been applied. Finally, we compute an optimal editing path from the region-level editing operations, based on predefined semantic constraints. The recovered history can be used in various applications such as image re-editing, edit transfer, and image revision control. A user study suggests that the editing histories generated from our system are semantically comparable to the ones generated by artists.

This work presents Sketch2Scene , a framework that automatically turns a freehand sketch drawing inferring multiple scene objects to semantically valid, well arranged scenes of 3D models. Unlike the existing works on sketch-based search and composition of 3D models, which typically process individual sketched objects one by one, our technique performs co-retrieval and co-placement of 3D relevant models by jointly processing the sketched objects. This is enabled by summarizing functional and spatial relationships among models in a large collection of 3D scenes as structural groups . Our technique greatly reduces the amount of user intervention needed for sketch-based modeling of 3D scenes and fits well into the traditional production pipeline involving concept design followed by 3D modeling. A pilot study indicates that it is promising to use our technique as an alternative but more efficient tool of standard 3D modeling for 3D scene construction.

We present a novel anisotropic Spherical Gaussian (ASG) function, built upon the Bingham distribution [Bingham 1974], which is much more effective and efficient in representing anisotropic spherical functions than Spherical Gaussians (SGs). In addition to retaining many desired properties of SGs, ASGs are also rotationally invariant and capable of representing all-frequency signals. To further strengthen the properties of ASGs, we have derived approximate closed-form solutions for their integral, product and convolution operators, whose errors are nearly negligible, as validated by quantitative analysis. Supported by all these operators, ASGs can be adapted in existing SG-based applications to enhance their scalability in handling anisotropic effects. To demonstrate the accuracy and efficiency of ASGs in practice, we have applied ASGs in two important SG-based rendering applications and the experimental results clearly reveal the merits of ASGs.

Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.

Abstract In this paper we present a new algorithm for accurate rendering of translucent materials under Spherical Gaussian (SG) lights. Our algorithm builds upon the quantized‐diffusion BSSRDF model recently introduced in [ dI11 ]. Our main contribution is an efficient algorithm for computing the integral of the BSSRDF with an SG light. We incorporate both single and multiple scattering components. Our model improves upon previous work by accounting for the incident angle of each individual SG light. This leads to more accurate rendering results, notably elliptical profiles from oblique illumination. In contrast, most existing models only consider the total irradiance received from all lights, hence can only generate circular profiles. Experimental results show that our method is suitable for rendering of translucent materials under finite‐area lights or environment lights that can be approximated by a small number of SGs.

We present an interactive algorithm for hair rendering and appearance editing under complex environment lighting represented as spherical radial basis functions (SRBFs). Our main contribution is to derive a compact 1D circular Gaussian representation that can accurately model the hair scattering function introduced by [Marschner et al. 2003]. The primary benefit of this representation is that it enables us to evaluate, at run-time, closed-form integrals of the scattering function with each SRBF light, resulting in efficient computation of both single and multiple scatterings. In contrast to previous work, our algorithm computes the rendering integrals entirely on the fly and does not depend on expensive pre-computation. Thus we allow the user to dynamically change the hair scattering parameters, which can vary spatially. Analyses show that our 1D circular Gaussian representation is both accurate and concise. In addition, our algorithm incorporates the eccentricity of the hair. We implement our algorithm on the GPU, achieving interactive hair rendering and simultaneous appearance editing under complex environment maps for the first time.

Abstract We propose an efficient method for editing bidirectional texture functions (BTFs) based on edit propagation scheme. In our approach, users specify sparse edits on a certain slice of BTF. An edit propagation scheme is then applied to propagate edits to the whole BTF data. The consistency of the BTF data is maintained by propagating similar edits to points with similar underlying geometry/reflectance. For this purpose, we propose to use view independent features including normals and reflectance features reconstructed from each view to guide the propagation process. We also propose an adaptive sampling scheme for speeding up the propagation process. Since our method needn't any accurate geometry and reflectance information, it allows users to edit complex BTFs with interactive feedback.

Image/video editing by strokes has become increasingly popular due to the ease of interaction. Propagating the user inputs to the rest of the image/video, however, is often time and memory consuming especially for large data. We propose here an efficient scheme that allows affinity-based edit propagation to be computed on data containing tens of millions of pixels at interactive rate (in matter of seconds). The key in our scheme is a novel means for approximately solving the optimization problem involved in edit propagation, using adaptive clustering in a high-dimensional, affinity space. Our approximation significantly reduces the cost of existing affinity-based propagation methods while maintaining visual fidelity, and enables interactive stroke-based editing even on high resolution images and long video sequences using commodity computers.

This paper presents a novel basis function, called spherical piecewise constant basis function (SPCBF), for precomputed radiance transfer. SPCBFs have several desirable properties: rotatability, ability to represent all-frequency signals, and support for efficient multiple product. By smartly partitioning the illumination sphere into a set of subregions, and associating each subregion with an SPCBF valued 1 inside the region and 0 elsewhere, we precompute the light coefficients using the resulting SPCBFs. Efficient rotation of the light representation in SPCBFs is achieved by rotating the domain of SPCBFs. We run-time approximate the BRDF and visibility coefficients using the set of SPCBFs for light, possibly rotated, through fast lookup of summed-area-table (SAT) and visibility distance table (VDT), respectively. SPCBFs enable new effects such as object rotation in all-frequency rendering of dynamic scenes and on-the-fly BRDF editing under rotating environment lighting. With graphics hardware acceleration, our method achieves real-time frame rates.

Abstract This paper presents a novel method for real‐time homogenous translucent material editing under fixed illumination. We consider the complete analytic BSSRDF model proposed by Jensen et al. [ JMLH01 ], including both multiple scattering and single scattering. Our method allows the user to adjust the analytic parameters of BSSRDF and provides high‐quality, real‐time rendering feedback. Inspired by recently developed Precomputed Radiance Transfer (PRT) techniques, we approximate both the multiple scattering diffuse reflectance function and the single scattering exponential attenuation function in the analytic model using basis functions, so that re‐computing the outgoing radiance at each vertex as parameters change reduces to simple dot products. In addition, using a non‐uniform piecewise polynomial basis, we are able to achieve smaller approximation error than using bases adopted in previous PRT‐based works, such as spherical harmonics and wavelets. Using hardware acceleration, we demonstrate that our system generates images comparable to [ JMLH01 ]at real‐time frame‐rates.