Spatially-varying Reflectance Research Articles

OpenSVBRDF: A Database of Measured Spatially-Varying Reflectance

We present the first large-scale database of measured spatially-varying anisotropic reflectance, consisting of 1,000 high-quality near-planar SVBRDFs, spanning 9 material categories such as wood, fabric and metal. Each sample is captured in 15 minutes, and represented as a set of high-resolution texture maps that correspond to spatially-varying BRDF parameters and local frames. To build this database, we develop a novel integrated system for robust, high-quality and -efficiency reflectance acquisition and reconstruction. Our setup consists of 2 cameras and 16,384 LEDs. We train 64 lighting patterns for efficient acquisition, in conjunction with a network that predicts per-point reflectance in a neural representation from carefully aligned two-view measurements captured under the patterns. The intermediate results are further fine-tuned with respect to the photographs acquired under 63 effective linear lights, and finally fitted to a BRDF model. We report various statistics of the database, and demonstrate its value in the applications of material generation, classification as well as sampling. All related data, including future additions to the database, can be downloaded from https://opensvbrdf.github.io/.

Sparse ellipsometry

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

NeRFactor

We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties. The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and environment lighting. Specifically, NeRFactor recovers 3D neural fields of surface normals, light visibility, albedo, and Bidirectional Reflectance Distribution Functions (BRDFs) without any supervision, using only a re-rendering loss, simple smoothness priors, and a data-driven BRDF prior learned from real-world BRDF measurements. By explicitly modeling light visibility, NeRFactor is able to separate shadows from albedo and synthesize realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor is able to recover convincing 3D models for free-viewpoint relighting in this challenging and underconstrained capture setup for both synthetic and real scenes. Qualitative and quantitative experiments show that NeRFactor outperforms classic and deep learning-based state of the art across various tasks. Our videos, code, and data are available at people.csail.mit.edu/xiuming/projects/nerfactor/.

Highlight-aware two-stream network for single-image SVBRDF acquisition

This paper addresses the task of estimating spatially-varying reflectance (i.e., SVBRDF) from a single, casually captured image. Central to our method is a highlight-aware (HA) convolution operation and a two-stream neural network equipped with proper training losses. Our HA convolution, as a novel variant of standard (ST) convolution, directly modulates convolution kernels under the guidance of automatically learned masks representing potentially overexposed highlight regions. It helps to reduce the impact of strong specular highlights on diffuse components and at the same time, hallucinates plausible contents in saturated regions. Considering that variation of saturated pixels also contains important cues for inferring surface bumpiness and specular components, we design a two-stream network to extract features from two different branches stacked by HA convolutions and ST convolutions, respectively. These two groups of features are further fused in an attention-based manner to facilitate feature selection of each SVBRDF map. The whole network is trained end to end with a new perceptual adversarial loss which is particularly useful for enhancing the texture details. Such a design also allows the recovered material maps to be disentangled. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to recover clear SVBRDFs from a single casually captured image, and performs favorably against state-of-the-arts. Since we impose very few constraints on the capture process, even a non-expert user can create high-quality SVBRDFs that cater to many graphical applications.

Towards spatially varying gloss reproduction for 3D printing

3D printing technology is a powerful tool for manufacturing complex shapes with high-quality textures. Gloss, next to color and shape, is one of the most salient visual aspects of an object. Unfortunately, printing a wide range of spatially-varying gloss properties using state-of-the-art 3D printers is challenging as it relies on geometrical modifications to achieve the desired appearance. A common post-processing step is to apply off-the-shelf varnishes that modify the final gloss. The main difficulty in automating this process lies in the physical properties of the varnishes which owe their appearance to a high concentration of large particles and as such, they cannot be easily deposited with current 3D color printers. As a result, fine-grained control of gloss properties using today's 3D printing technologies is limited in terms of both spatial resolution and the range of achievable gloss. We address the above limitations and propose new printing hardware based on piezo-actuated needle valves capable of jetting highly viscous varnishes. Based on the new hardware setup, we present the complete pipeline for controlling the gloss of a given 2.5 D object, from printer calibration, through material selection, to the manufacturing of models with spatially-varying reflectance. Furthermore, we discuss the potential integration with current 3D printing technology. Apart from being a viable solution for 3D printing, our method offers an additional and essential benefit of separating color and gloss fabrication which makes the process more flexible and enables high-quality color and gloss reproduction.

Learning to reconstruct shape and spatially-varying reflectance from a single image

Reconstructing shape and reflectance properties from images is a highly under-constrained problem, and has previously been addressed by using specialized hardware to capture calibrated data or by assuming known (or highly constrained) shape or reflectance. In contrast, we demonstrate that we can recover non-Lambertian, spatially-varying BRDFs and complex geometry belonging to any arbitrary shape class, from a single RGB image captured under a combination of unknown environment illumination and flash lighting. We achieve this by training a deep neural network to regress shape and reflectance from the image. Our network is able to address this problem because of three novel contributions: first, we build a large-scale dataset of procedurally generated shapes and real-world complex SVBRDFs that approximate real world appearance well. Second, single image inverse rendering requires reasoning at multiple scales, and we propose a cascade network structure that allows this in a tractable manner. Finally, we incorporate an in-network rendering layer that aids the reconstruction task by handling global illumination effects that are important for real-world scenes. Together, these contributions allow us to tackle the entire inverse rendering problem in a holistic manner and produce state-of-the-art results on both synthetic and real data.

Shape and Spatially-Varying Reflectance Estimation from Virtual Exemplars.

This paper addresses the problem of estimating the shape of objects that exhibit spatially-varying reflectance. We assume that multiple images of the object are obtained under a fixed view-point and varying illumination, i.e., the setting of photometric stereo. At the core of our techniques is the assumption that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary. This assumption enables a per-pixel surface normal and BRDF estimation framework that is computationally tractable and requires no initialization in spite of the underlying problem being non-convex. Our estimation framework first solves for the surface normal at each pixel using a variant of example-based photometric stereo. We design an efficient multi-scale search strategy for estimating the surface normal and subsequently, refine this estimate using a gradient descent procedure. Given the surface normal estimate, we solve for the spatially-varying BRDF by constraining the BRDF at each pixel to be in the span of the BRDF dictionary; here, we use additional priors to further regularize the solution. A hallmark of our approach is that it does not require iterative optimization techniques nor the need for careful initialization, both of which are endemic to most state-of-the-art techniques. We showcase the performance of our technique on a wide range of simulated and real scenes where we outperform competing methods.

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

Practical SVBRDF capture in the frequency domain

Spatially-varying reflectance and small geometric variations play a vital role in the appearance of real-world surfaces. Consequently, robust, automatic capture of such models is highly desirable; however, current systems require either specialized hardware, long capture times, user intervention, or rely heavily on heuristics. We describe an acquisition setup that utilizes only portable commodity hardware (an LCD display, an SLR camera) and contains no moving parts. In particular, a laptop screen can be used for illumination. Our setup, aided by a carefully constructed image formation model, automatically produces realistic spatially-varying reflectance parameters over a wide range of materials from diffuse to almost mirror-like specular surfaces, while requiring relatively few photographs. We believe our system is the first to offer such generality, while requiring only standard office equipment and no user intervention or parameter tuning. Our results exhibit a good qualitative match to photographs taken under novel viewing and lighting conditions for a range of materials.

Bi-scale appearance fabrication

Surfaces in the real world exhibit complex appearance due to spatial variations in both their reflectance and local shading frames (i.e. the local coordinate system defined by the normal and tangent direction). For opaque surfaces, existing fabrication solutions can reproduce well only the spatial variations of isotropic reflectance. In this paper, we present a system for fabricating surfaces with desired spatially-varying reflectance, including anisotropic ones, and local shading frames. We approximate each input reflectance, rotated by its local frame, as a small patch of oriented facets coated with isotropic glossy inks. By assigning different ink combinations to facets with different orientations, this bi-scale material can reproduce a wider variety of reflectance than the printer gamut, including anisotropic materials. By orienting the facets appropriately, we control the local shading frame. We propose an algorithm to automatically determine the optimal facets orientations and ink combinations that best approximate a given input appearance, while obeying manufacturing constraints on both geometry and ink gamut. We fabricate the resulting surface with commercially available hardware, a 3D printer to fabricate the facets and a flatbed UV printer to coat them with inks. We validate our method by fabricating a variety of isotropic and anisotropic materials with rich variations in normals and tangents.

Acquiring reflectance and shape from continuous spherical harmonic illumination

We present a novel technique for acquiring the geometry and spatially-varying reflectance properties of 3D objects by observing them under continuous spherical harmonic illumination conditions. The technique is general enough to characterize either entirely specular or entirely diffuse materials, or any varying combination across the surface of the object. We employ a novel computational illumination setup consisting of a rotating arc of controllable LEDs which sweep out programmable spheres of incident illumination during 1-second exposures. We illuminate the object with a succession of spherical harmonic illumination conditions, as well as photographed environmental lighting for validation. From the response of the object to the harmonics, we can separate diffuse and specular reflections, estimate world-space diffuse and specular normals, and compute anisotropic roughness parameters for each view of the object. We then use the maps of both diffuse and specular reflectance to form correspondences in a multiview stereo algorithm, which allows even highly specular surfaces to be corresponded across views. The algorithm yields a complete 3D model and a set of merged reflectance maps. We use this technique to digitize the shape and reflectance of a variety of objects difficult to acquire with other techniques and present validation renderings which match well to photographs in similar lighting.

AppGen

We present AppGen , an interactive system for modeling materials from a single image. Given a texture image of a nearly planar surface lit with directional lighting, our system models the detailed spatially-varying reflectance properties (diffuse, specular and roughness) and surface normal variations with minimal user interaction. We ask users to indicate global shading and reflectance information by roughly marking the image with a few user strokes, while our system assigns reflectance properties and normals to each pixel. We first interactively decompose the input image into the product of a diffuse albedo map and a shading map. A two-scale normal reconstruction algorithm is then introduced to recover the normal variations from the shading map and preserve the geometric features at different scales. We finally assign the specular parameters to each pixel guided by user strokes and the diffuse albedo. Our system generates convincing results within minutes of interaction and works well for a variety of material types that exhibit different reflectance and normal variations, including natural surfaces and man-made ones.

Manifold bootstrapping for SVBRDF capture

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 bootstrapping for SVBRDF capture

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 high-resolution SVBRDF. A quick capture session (10-20 minutes) on simple devices yields results showing sharp and anisotropic specularity and rich spatial detail.

All-frequency rendering of dynamic, spatially-varying reflectance

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.

Printing spatially-varying reflectance

Although real-world surfaces can exhibit significant variation in materials --- glossy, diffuse, metallic, etc. --- printers are usually used to reproduce color or gray-scale images. We propose a complete system that uses appropriate inks and foils to print documents with a variety of material properties. Given a set of inks with known Bidirectional Reflectance Distribution Functions (BRDFs), our system automatically finds the optimal linear combinations to approximate the BRDFs of the target documents. Novel gamut-mapping algorithms preserve the relative glossiness between different BRDFs, and halftoning is used to produce patterns to be sent to the printer. We demonstrate the effectiveness of this approach with printed samples of a number of measured spatially-varying BRDFs.

Linear light source reflectometry

This paper presents a technique for estimating the spatially-varying reflectance properties of a surface based on its appearance during a single pass of a linear light source. By using a linear light rather than a point light source as the illuminant, we are able to reliably observe and estimate the diffuse color, specular color, and specular roughness of each point of the surface. The reflectometry apparatus we use is simple and inexpensive to build, requiring a single direction of motion for the light source and a fixed camera viewpoint. Our model fitting technique first renders a reflectance table of how diffuse and specular reflectance lobes would appear under moving linear light source illumination. Then, for each pixel we compare its series of intensity values to the tabulated reflectance lobes to determine which reflectance model parameters most closely produce the observed reflectance values. Using two passes of the linear light source at different angles, we can also estimate per-pixel surface normals as well as the reflectance parameters. Additionally our system records a per-pixel height map for the object and estimates its per-pixel translucency. We produce real-time renderings of the captured objects using a custom hardware shading algorithm. We apply the technique to a test object exhibiting a variety of materials as well as to an illuminated manuscript with gold lettering. To demonstrate the technique's accuracy, we compare renderings of the captured models to real photographs of the original objects.