Jiaping Wang

We present a practical and unbiased sampling technique for volumetric light sources defined by signed distance functions (SDFs). SDFs can compactly represent complex procedural shapes and support Boolean operations for flexible composition. However, their use as emitters remains underexplored in photorealistic rendering due to the absence of efficient sampling strategies. Our key insight is to model the interior of an SDF as a uniform volume and to project volumetric samples onto the unit sphere centered at the shading point, yielding a solid-angle distribution that captures the emitter's relative spatial layout. We derive the directional probability densities for analytic primitives and generalize the formulation to arbitrary SDFs via Monte Carlo volume estimation. Leveraging robust sphere tracing, our method enables accurate and efficient sampling of SDF emitters without requiring explicit surface parameterization, voxelization, or precomputed tables. Compared to baseline approaches such as uniform directional sampling or surface approximations, our technique achieves significantly lower variance and runtime in next-event estimation, while broadening the expressive power of light sources in rendering.

We introduce a guided lens sampling method for efficient rendering of circles of confusion (CoCs). While traditional Monte Carlo techniques simulate depth-of-field (DoF) effects by perturbing camera rays at the lens, uniform lens sampling often results in significant variance by failing to prioritize rays toward highlight regions in the scene. Although path guiding has proven effective for global illumination by learning importance distributions for incoming radiance, no comparable guiding technique for CoCs exists, primarily due to the strong parallax between adjacent pixels. We model highlight spots in world space using a globally shared radiance field, which is then transformed into lens space using a bipolar-cone projection to guide camera ray generation. We implement this theory using 3D Gaussians, achieving fast, robust guiding with minimal computational and storage overhead, making it suitable for production rendering. We also propose two extensions to further enhance local adaptation. Our experiments show that this approach significantly improves the sampling efficiency for CoC rendering.

Scaling blockchain performance through parallel smart contract execution has gained significant attention, as traditional methods remain constrained by the performance of a single virtual machine (VM), even in multi-chain or Layer-2 systems. Parallel VMs offer a compelling solution by enabling concurrent transaction execution within a single smart contract, using multiple CPU cores. However, Ethereum's sequential, shared-everything model limits the efficiency of existing parallel mechanisms, resulting in frequent rollbacks with optimistic methods and high overhead with pessimistic methods due to state dependency analysis and locking.

This paper introduces Crystality, a programming model for smart contracts on parallel Ethereum Virtual Machines (EVMs) that enables developers to express and leverage the parallelism inherent in smart contracts. Crystality introduces Programmable Contract Scopes to partition contract states into non-overlapping, parallelizable segments and decompose a smart contract function into finer-grained components. Crystality also features Asynchronous Functional Relay to manage execution flow across EVMs. These features simplify parallelism expression and enable asynchronous execution for commutative contract operations.

Crystality extends Solidity with directives, transpiling Crystality code into standard Solidity code for EVM compatibility. The system supports two execution modes: an asynchronous mode for transactions involving commutative operations and an optimistic-based fallback to ensure block-defined transaction order. Our experiments demonstrated Crystality's superior performance compared to Ethereum, Aptos, and Sui on a 64-core machine.

Cryptocurrencies have provided a promising infrastructure for pseudonymous online payments. However, low throughput has significantly hindered the scalability and usability of cryptocurrency systems for increasing numbers of users and transactions. Another obstacle to achieving scalability is that every node is required to duplicate the communication, storage, and state representation of the entire network.

In this paper, we introduce the Asynchronous Consensus Zones, which scales blockchain system linearly without compromising decentralization or security. We achieve this by running multiple independent and parallel instances of single-chain consensus (zones). The consensus happens independently within each zone with minimized communication, which partitions the workload of the entire network and ensures moderate burden for each individual node as network grows. We propose eventual atomicity to ensure transaction atomicity across zones, which guarantees the efficient completion of transaction without the overhead of two-phase commit protocol. We also propose Chu-ko-nu mining to ensure the effective mining power in each zone is at the same level of the entire network, and makes an attack on any individual zone as hard as that on the entire network. Our experimental results show the effectiveness of our work. On a test-bed including 1,200 virtual machines worldwide to support 48,000 nodes, our system deliver 1,000× throughput and 2,000× capacity over Bitcoin and Ethereum network.

We propose a novel framework that automatically learns the lighting patterns for efficient reflectance acquisition, as well as how to faithfully reconstruct spatially varying anisotropic BRDFs and local frames from measurements under such patterns. The core of our framework is an asymmetric deep autoencoder, consisting of a nonnegative, linear encoder which directly corresponds to the lighting patterns used in physical acquisition, and a stacked, nonlinear decoder which computationally recovers the BRDF information from captured photographs. The autoencoder is trained with a large amount of synthetic reflectance data, and can adapt to various factors, including the geometry of the setup and the properties of appearance. We demonstrate the effectiveness of our framework on a wide range of physical materials, using as few as 16 ~ 32 lighting patterns, which correspond to 12 ~ 25 seconds of acquisition time. We also validate our results with the ground truth data and captured photographs. Our framework is useful for increasing the efficiency in both novel and existing acquisition setups.

Raster images are the standard format for texture mapping, but they suffer from limited resolution. Vector graphics are resolution-independent but are less general and more difficult to implement on a GPU. We propose a hybrid representation called vector regression functions (VRFs), which compactly approximate any point-sampled image and support GPU texture mapping, including random access and filtering operations. Unlike standard GPU texture compression, (VRFs) provide a variable-rate encoding in which piecewise smooth regions compress to the square root of the original size. Our key idea is to represent images using the multilayer perceptron, allowing general encoding via regression and efficient decoding via a simple GPU pixel shader. We also propose a content-aware spatial partitioning scheme to reduce the complexity of the neural network model. We demonstrate benefits of our method including its quality, size, and runtime speed.

We present radiance regression functions for fast rendering of global illumination in scenes with dynamic local light sources. A radiance regression function (RRF) represents a non-linear mapping from local and contextual attributes of surface points, such as position, viewing direction, and lighting condition, to their indirect illumination values. The RRF is obtained from precomputed shading samples through regression analysis, which determines a function that best fits the shading data. For a given scene, the shading samples are precomputed by an offline renderer.

The key idea behind our approach is to exploit the nonlinear coherence of the indirect illumination data to make the RRF both compact and fast to evaluate. We model the RRF as a multilayer acyclic feed-forward neural network, which provides a close functional approximation of the indirect illumination and can be effi- ciently evaluated at run time. To effectively model scenes with spatially variant material properties, we utilize an augmented set of attributes as input to the neural network RRF to reduce the amount of inference that the network needs to perform. To handle scenes with greater geometric complexity, we partition the input space of the RRF model and represent the subspaces with separate, smaller RRFs that can be evaluated more rapidly. As a result, the RRF model scales well to increasingly complex scene geometry and material variation. Because of its compactness and ease of evaluation, the RRF model enables real-time rendering with full global illumination effects, including changing caustics and multiple-bounce high-frequency glossy interreflections.

Multispectral images record detailed color spectra at each image pixel. To display a multispectral image on conventional output devices, a chromaticity mapping function is needed to map the spectral vector of each pixel to the displayable three dimensional color space. In this paper, we present an interactive method for locally adjusting the chromaticity mapping of a multispectral image. The user specifies edits to the chromaticity mapping via a sparse set of strokes at selected image locations and wavelengths, then our method automatically propagates the edits to the rest of the multispectral image. The key idea of our approach is to factorize the multispectral image into a component that indicates spatial coherence between different pixels, and one that describes spectral coherence between different wavelengths. Based on this factorized representation, a two-step algorithm is developed to efficiently propagate the edits in the spatial and spectral domains separately. The method presented provides photographers with efficient control over color appearance and scene details in a manner not possible with conventional color image editing. We demonstrate the use of interactive chromaticity mapping in the applications of color stylization to emulate the appearance of photographic films, enhancement of image details, and manipulation of different light transport effects.

We present a simple, fast solution for reflectance acquisition using tools that fit into a pocket. Our method captures video of a flat target surface from a fixed video camera lit by a hand-held, moving, linear light source. After processing, we obtain an SVBRDF. We introduce a BRDF chart, analogous to a color "checker" chart, which arranges a set of known-BRDF reference tiles over a small card. A sequence of light responses from the chart tiles as well as from points on the target is captured and matched to reconstruct the target's appearance.

We develop a new algorithm for BRDF reconstruction which works directly on these LDR responses, without knowing the light or camera position, or acquiring HDR lighting. It compensates for spatial variation caused by the local (finite distance) camera and light position by warping responses over time to align them to a specular reference. After alignment, we find an optimal linear combination of the Lambertian and purely specular reference responses to match each target point's response. The same weights are then applied to the corresponding (known) reference BRDFs to reconstruct the target point's BRDF. We extend the basic algorithm to also recover varying surface normals by adding two spherical caps for diffuse and specular references to the BRDF chart.

We present a technique for rapid capture of high quality bidirectional reflection distribution functions(BRDFs) of surface points. Our method represents the BRDF at each point by a generalized microfacet model with tabulated normal distribution function (NDF) and assumes that the BRDF is symmetrical. A compact and light-weight reflectometry apparatus is developed for capturing reflectance data from each surface point within one second.

The device consists of a pair of condenser lenses, a video camera, and six LED light sources. During capture, the reflected rays from a surface point lit by a LED lighting are refracted by a condenser lenses and efficiently collected by the camera CCD. Taking advantage of BRDF symmetry, our reflectometry apparatus provides an efficient optical design to improve the measurement quality. We also propose a model fitting algorithm for reconstructing the generalized microfacet model from the sparse BRDF slices captured from a material surface point. Our new algorithm addresses the measurement errors and generates more accurate results than previous work.

Our technique provides a practical and efficient solution for BRDF acquisition, especially for materials with anisotropic reflectance. We test the accuracy of our approach by comparing our results with ground truth. We demonstrate the efficiency of our reflectometry by measuring materials with different degrees of specularity, values of Fresnel factor, and angular variation.

Manifold bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance, based on the idea that reflectance over a given material sample forms a low-dimensional manifold. It provides a high-resolution result in both the spatial and angular domains by decomposing reflectance measurement into two lower-dimensional phases. The first acquires representatives of high angular dimension but sampled sparsely over the surface, while the second acquires keys of low angular dimension but sampled densely over the surface.

We develop a hand-held, high-speed BRDF capturing device for phase one measurements. A condenser-based optical setup collects a dense hemisphere of rays emanating from a single point on the target sample as it is manually scanned over it, yielding 10 BRDF point measurements per second. Lighting directions from 6 LEDs are applied at each measurement; these are amplified to a full 4D BRDF using the general (NDF-tabulated) microfacet model. The second phase captures N=20-200 images of the entire sample from a fixed view and lit by a varying area source. We show that the resulting N-dimensional keys capture much of the distance information in the original BRDF space, so that they effectively discriminate among representatives, though they lack sufficient angular detail to reconstruct the SVBRDF by themselves. At each surface position, a local linear combination of a small number of neighboring representatives is computed to match each key, yielding a highresolution SVBRDF. A quick capture session (10-20 minutes) on simple devices yields results showing sharp and anisotropic specularity and rich spatial detail.

Many real world surfaces exhibit translucent appearance due to subsurface scattering. Although various methods exists to measure, edit and render subsurface scattering effects, no solution exists for manufacturing physical objects with desired translucent appearance. In this paper, we present a complete solution for fabricating a material volume with a desired surface BSSRDF. We stack layers from a fixed set of manufacturing materials whose thickness is varied spatially to reproduce the heterogeneity of the input BSSRDF. Given an input BSSRDF and the optical properties of the manufacturing

materials, our system efficiently determines the optimal order and thickness of the layers. We demonstrate our approach

by printing a variety of homogenous and heterogenous BSSRDFs using two hardware setups: a milling machine and a 3D printer.

We present a real-time algorithm for rendering translucent objects of arbitrary shapes. We approximate the scattering

of light inside the objects using the diffusion equation, which we solve on-the-fly using the GPU. Our algorithm is general enough to handle arbitrary geometry, heterogeneous materials, deformable objects and modifications of lighting, all in real-time. In a pre-processing step, we discretize the object into a regular 4-connected structure (QuadGraph). Due to its regular connectivity, this structure is easily packed into a texture and stored on the GPU. At runtime, we use the QuadGraph stored on the GPU to solve the diffusion equation, in real-time, taking into account the varying input conditions: Incoming light, object material and geometry. We handle deformable objects, provided the deformation does not change the topological structure of the objects.

We describe a technique for real-time rendering of dynamic, spatially-varying BRDFs in static scenes with all-frequency shadows from environmental and point lights. The 6D SVBRDF is represented with a general microfacet model and spherical lobes fit to its 4D spatially-varying normal distribution function (SVNDF). A sum of spherical Gaussians (SGs) provides an accurate approximation with a small number of lobes. Parametric BRDFs are fit on-the-fly using simple analytic expressions; measured BRDFs are fit as a preprocess using nonlinear optimization. Our BRDF representation is compact, allows detailed textures, is closed under products and rotations, and supports reflectance of arbitrarily high specularity. At run-time, SGs representing the NDF are warped to align the half-angle vector to the lighting direction and multiplied by the microfacet shadowing and Fresnel factors. This yields the relevant 2D view slice on-the-fly at each pixel, still represented in the SG basis. We account for macro-scale shadowing using a new, nonlinear visibility representation based on spherical signed distance functions (SSDFs). SSDFs allow per-pixel interpolation of high-frequency visibility without ghosting and can be multiplied by the BRDF and lighting efficiently on the GPU.

We present a new model, called the dual-microfacet, for those materials such as paper and plastic formed by a thin, transparent slab lying between two surfaces of spatially varying roughness. Light transmission through the slab is represented by a microfacet-based BTDF which tabulates the microfacet’s normal distribution (NDF) as a function of surface location. Though the material is bounded by two surfaces of different roughness, we approximate light transmission through it by a virtual slab determined by a single spatially-varying NDF. This enables efficient capturing of spatially variant transparent slices. We describe a device for measuring this model over a flat sample by shining light from a CRT behind it and capturing a sequence of images from a single view. Our method captures both angular and spatial variation in the BTDF and provides a good match to measured materials.

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 re?ectance 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 re?ectance information, it allows users to edit complex BTFs with interactive feedback.

We propose a new texture editing operation called texture splicing. For this operation, we regard a texture as having repetitive elements (textons) seamlessly distributed in a particular pattern. Taking two textures as input, texture splicing generates a new texture by selecting the texton appearance from one texture and distribution from the other. Texture splicing involves self-similarity search to extract the distribution, distribution warping, contextdependent warping, and finally, texture refinement to preserve overall appearance. We show a variety of results to illustrate this operation.

We propose a kernel Nystrom method for reconstructing the light transport matrix from a relatively small number of acquired images. Our work is based on the generalized Nystrom method for low rank matrices. We introduce the light transport kernel and incorporate it into the Nystrom method to exploit the nonlinear coherence of the light transport matrix. We also develop an adaptive scheme for efficiently capturing the sparsely sampled images from the scene. Our experiments indicate that the kernel Nystrom method can achieve good reconstruction of the light transport matrix with a few hundred images and produce high quality relighting results. The kernel Nystrom method is effective for modeling scenes with complex lighting effects and occlusions which have been challenging for existing techniques.

We present a new technique for the visual modeling of spatially varying anisotropic reflectance using data captured from a single view. Reflectance is represented using a microfacet-based BRDF which tabulates the facets' normal distribution (NDF) as a function of surface location. Data from a single view provides a 2D slice of the 4D BRDF at each surface point from which we fit a partial NDF. The fitted NDF is partial because the single view direction coupled with the set of light directions covers only a portion of the "half-angle" hemisphere. We complete the NDF at each point by applying a novel variant of texture synthesis using similar, overlapping partial NDFs from other points. Our similarity measure allows azimuthal rotation of partial NDFs, under the assumption that reflectance is spatially redundant but the local frame may be arbitrarily oriented. Our system includes a simple acquisition device that collects images over a 2D set of light directions by scanning a linear array of LEDs over a flat sample. Results demonstrate that our approach preserves spatial and directional BRDF details and generates a visually compelling match to measured materials.

Light Emitting Diodes (LEDs) can be used as light detectors and as light emitters. In this paper, we present a novel BRDF measurement device consisting exclusively of LEDs. Our design can acquire BRDFs over a full hemisphere, or even a full sphere (for the bidirectional transmittance distribution function BTDF) , and can also measure a (partial) multi-spectral BRDF. Because we use no cameras, projectors, or even mirrors, our design does not suffer from occlusion problems. It is fast, significantly simpler, and more compact than existing BRDF measurement designs.

We present a technique for modeling and editing the weathering effects of an object in a single image with appearance manifolds. In our approach, we formulate the input image as the product of reflectance and illuminance. An iterative method is then developed to construct the appearance manifold in color space (i.e., Lab space) for modeling the reflectance variations caused by weathering. Based on the appearance manifold, we propose a statistical method to robustly decompose reflectance and illuminance for each pixel. For editing, we introduce a "pixel-walking" scheme to modify the pixel reflectance according to its position on the manifold, by which the detailed reflectance variations are well preserved. We illustrate our technique in various applications, including weathering transfer between two images that is first enabled by our technique.

We propose techniques for modeling and rendering of heterogeneous translucent materials that enable acquisition from measured samples, interactive editing of material attributes, and real-time rendering. The materials are assumed to be optically dense such that multiple scattering can be approximated by a diffusion process described by the diffusion equation. For modeling heterogeneous materials, we present an algorithm for acquiring material properties from appearance measurements by solving an inverse diffusion problem. Our modeling algorithm incorporates a regularizer to handle the ill-conditioned inverse problem, an adjoint method to dramatically reduce the computational cost, and a hierarchical GPU implementation for further speedup. To display an object with known material properties, we present an algorithm that performs rendering by solving the diffusion equation with the boundary condition defined by the given illumination environment. This algorithm is centered around object representation by a polygrid, a grid with regular connectivity and an irregular shape, which facilitates the solution of the diffusion equation in arbitrary volumes. Because of the regular connectivity, our rendering algorithm can be implemented on the GPU for real-time performance. We demonstrate our techniques by capturing materials from physical samples and performing real-time rendering and editing with these materials.

We present an new SH operation, called spherical harmonics scaling, to shrink or expand a spherical function in frequency domain. We show that this problem can be elegantly formulated as a linear transformation of SH projections, which is efficient to compute and easy to implement on a GPU. Spherical harmonics scaling is particularly useful for extrapolating visibility and radiance functions at a sample point to points closer to or farther from an occluder or light source. With SH scaling, we present applications to lowfrequency shadowing for general deformable object, and to efficient approximation of spherical irradiance functions within a mid-range illumination environment.

We present a visual simulation technique called appearance manifolds for modeling the time-variant surface appearance of a material from data captured at a single instant in time. In modeling timevariant appearance, our method takes advantage of the key observation that concurrent variations in appearance over a surface represent different degrees of weathering. By reorganizing these various appearances in a manner that reveals their relative order with respect to weathering degree, our method infers spatial and temporal appearance properties of the material’s weathering process that can be used to convincingly generate its weathered appearance at different points in time. Results with natural non-linear reflectance variations are demonstrated in applications such as visual simulation of weathering on 3D models, increasing and decreasing the weathering of real objects, and material transfer with weathering effects.

Bidirectional texture functions or BTFs accurately model reflectance variation at a fine (meso-) scale as a function of lighting and viewing direction. BTFs also capture view-dependent visibility variation, also called masking or parallax, but only within surface contours. Mesostructure detail is neglected at silhouettes, so BTF-mapped objects retain the coarse shape of the underlying model.

We augment BTF rendering to obtain approximate mesoscale silhouettes. Our new representation, the 4D mesostructure distance function (MDF), tabulates the displacement from a reference frame where a ray first intersects the meso-scale geometry beneath, as a function of ray direction and ray position along that reference plane. Given an MDF, the mesostructure silhouette can be rendered with a per-pixel depth peeling process on graphics hardware, while shading and local parallax is handled by the BTF. Our approach allows realtime rendering, handles complex, non-height-field mesostructure, requires that no additional geometry to be sent to the rasterizer other than the mesh triangles, is more compact than textured visibility representations used previously, and for the first time can be easily measured from physical samples. We also adapt the algorithm to capture detailed shadows cast both by and onto BTF-mapped surfaces.We demonstrate the efficiency of our algorithm on a variety of BTF data, including real data acquired using our BTF-MDF measurement

Many translucent materials consist of evenly-distributed heterogeneous elements which produce a complex appearance under different lighting and viewing directions. For these quasi-homogeneous materials, existing techniques do not address how to acquire their material representations from physical samples in a way that allows arbitrary geometry models to be rendered with these materials. We propose a model for such materials that can be readily acquired from physical samples. This material model can be applied to geometric models of arbitrary shapes, and the resulting objects can be efficiently rendered without expensive subsurface light transport simulation.

In developing a material model with these attributes, we capitalize on a key observation about the subsurface scattering characteristics of quasi-homogeneous materials at different scales. Locally, the non-uniformity of these materials leads to inhomogeneous subsurface scattering. For subsurface scattering on a global scale, we show that a lengthy photon path through an even distribution of heterogeneous elements statistically resembles scattering in a homogeneous medium. This observation allows us to represent and measure the global light transport within quasi-homogeneous materials as well as the transfer of light into and out of a material volume through surface mesostructures. We demonstrate our technique with results for several challenging materials that exhibit sophisticated appearance features such as transmission of back illumination through surface mesostructures.

We propose a texture function for realistic modeling and efficient rendering of materials that exhibit surface mesostructures, translucency and volumetric texture variations. The appearance of such complex materials for dynamic lighting and viewing directions is expensive to calculate and requires an impractical amount of storage to precompute. To handle this problem, our method models an object as a shell layer, formed by texture synthesis of a volumetric material sample, and a homogeneous inner core. To facilitate computation of surface radiance from the shell layer, we introduce the shell texture function (STF) which describes voxel irradiance fields based on precomputed fine-level light interactions such as shadowing by surface Mesostructures and scattering of photons inside the object. Together with a diffusion approximation of homogeneous inner core radiance, the STF leads to fast and detailed renderings of complex materials by raytracing.