Kavita Bala

Lightcuts: a scalable approach to illumination

Lightcuts is a scalable framework for computing realistic illumination. It handles arbitrary geometry, non-diffuse materials, and illumination from a wide variety of sources including point lights, area lights, HDR environment maps, sun/sky models, and indirect illumination. At its core is a new algorithm for accurately approximating illumination from many point lights with a strongly sublinear cost. We show how a group of lights can be cheaply approximated while bounding the maximum approximation error. A binary light tree and perceptual metric are then used to adaptively partition the lights into groups to control the error vs. cost tradeoff.We also introduce reconstruction cuts that exploit spatial coherence to accelerate the generation of anti-aliased images with complex illumination. Results are demonstrated for five complex scenes and show that lightcuts can accurately approximate hundreds of thousands of point lights using only a few hundred shadow rays. Reconstruction cuts can reduce the number of shadow rays to tens.

Adaptive shadow maps

Shadow maps provide a fast and convenient method of identifying shadows in scenes but can introduce aliasing. This paper introduces the Adaptive Shadow Map (ASM) as a solution to this problem. An ASM removes aliasing by resolving pixel size mismatches between the eye view and the light source view. It achieves this goal by storing the light source view (i.e., the shadow map for the light source) as a hierarchical grid structure as opposed to the conventional flat structure. As pixels are transformed from the eye view to the light source view, the ASM is refined to create higher-resolution pieces of the shadow map when needed. This is done by evaluating the contributions of shadow map pixels to the overall image quality. The improvement process is view-driven, progressive, and confined to a user-specifiable memory footprint. We show that ASMs enable dramatic improvements in shadow quality while maintaining interactive rates.

Visual equivalence: towards a new standard for image fidelity

Efficient, realistic rendering of complex scenes is one of the grand challenges in computer graphics. Perceptually based rendering addresses this challenge by taking advantage of the limits of human vision. However, existing methods, based on predicting visible image differences, are too conservative because some kinds of image differences do not matter to human observers. In this paper, we introduce the concept of visual equivalence, a new standard for image fidelity in graphics. Images are visually equivalent if they convey the same impressions of scene appearance, even if they are visibly different. To understand this phenomenon, we conduct a series of experiments that explore how object geometry, material, and illumination interact to provide information about appearance, and we characterize how two kinds of transformations on illumination maps (blurring and warping) affect these appearance attributes. We then derive visual equivalence predictors (VEPs): metrics for predicting when images rendered with transformed illumination maps will be visually equivalent to images rendered with reference maps. We also run a confirmatory study to validate the effectiveness of these VEPs for general scenes. Finally, we show how VEPs can be used to improve the efficiency of two rendering algorithms: Light-cuts and precomputed radiance transfer. This work represents some promising first steps towards developing perceptual metrics based on higher order aspects of visual coding.

Learning visual similarity for product design with convolutional neural networks

Popular sites like Houzz, Pinterest, and LikeThatDecor, have communities of users helping each other answer questions about products in images. In this paper we learn an embedding for visual search in interior design. Our embedding contains two different domains of product images: products cropped from internet scenes, and products in their iconic form. With such a multi-domain embedding, we demonstrate several applications of visual search including identifying products in scenes and finding stylistically similar products. To obtain the embedding, we train a convolutional neural network on pairs of images. We explore several training architectures including re-purposing object classifiers, using siamese networks, and using multitask learning. We evaluate our search quantitatively and qualitatively and demonstrate high quality results for search across multiple visual domains, enabling new applications in interior design.

Matching Real Fabrics with Micro-Appearance Models

Micro-appearance models explicitly model the interaction of light with microgeometry at the fiber scale to produce realistic appearance. To effectively match them to real fabrics, we introduce a new appearance matching framework to determine their parameters. Given a micro-appearance model and photographs of the fabric under many different lighting conditions, we optimize for parameters that best match the photographs using a method based on calculating derivatives during rendering. This highly applicable framework, we believe, is a useful research tool because it simplifies development and testing of new models. Using the framework, we systematically compare several types of micro-appearance models. We acquired computed microtomography (micro CT) scans of several fabrics, photographed the fabrics under many viewing/illumination conditions, and matched several appearance models to this data. We compare a new fiber-based light scattering model to the previously used microflake model. We also compare representing cloth microgeometry using volumes derived directly from the micro CT data to using explicit fibers reconstructed from the volumes. From our comparisons, we make the following conclusions: (1) given a fiber-based scattering model, volume- and fiber-based microgeometry representations are capable of very similar quality, and (2) using a fiber-specific scattering model is crucial to good results as it achieves considerably higher accuracy than prior work.

Intrinsic images in the wild

Intrinsic image decomposition separates an image into a reflectance layer and a shading layer. Automatic intrinsic image decomposition remains a significant challenge, particularly for real-world scenes. Advances on this longstanding problem have been spurred by public datasets of ground truth data, such as the MIT Intrinsic Images dataset. However, the difficulty of acquiring ground truth data has meant that such datasets cover a small range of materials and objects. In contrast, real-world scenes contain a rich range of shapes and materials, lit by complex illumination. In this paper we introduce Intrinsic Images in the Wild, a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes. We create this benchmark through millions of crowdsourced annotations of relative comparisons of material properties at pairs of points in each scene. Crowdsourcing enables a scalable approach to acquiring a large database, and uses the ability of humans to judge material comparisons, despite variations in illumination. Given our database, we develop a dense CRF-based intrinsic image algorithm for images in the wild that outperforms a range of state-of-the-art intrinsic image algorithms. Intrinsic image decomposition remains a challenging problem; we release our code and database publicly to support future research on this problem, available online at http://intrinsic.cs.cornell.edu/.

Matrix row-column sampling for the many-light problem

Rendering complex scenes with indirect illumination, high dynamic range environment lighting, and many direct light sources remains a challenging problem. Prior work has shown that all these effects can be approximated by many point lights. This paper presents a scalable solution to the many-light problem suitable for a GPU implementation. We view the problem as a large matrix of sample-light interactions; the ideal final image is the sum of the matrix columns. We propose an algorithm for approximating this sum by sampling entire rows and columns of the matrix on the GPU using shadow mapping. The key observation is that the inherent structure of the transfer matrix can be revealed by sampling just a small number of rows and columns. Our prototype implementation can compute the light transfer within a few seconds for scenes with indirect and environment illumination, area lights, complex geometry and arbitrary shaders. We believe this approach can be very useful for rapid previewing in applications like cinematic and architectural lighting design.

Material recognition in the wild with the Materials in Context Database

Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild. MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolutional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. For patch-based classification on MINC we found that the best performing CNN architectures can achieve 85.2% mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1% mean class accuracy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation.

Multidimensional lightcuts

Multidimensional lightcuts is a new scalable method for efficiently rendering rich visual effects such as motion blur, participating media, depth of field, and spatial anti-aliasing in complex scenes. It introduces a flexible, general rendering framework that unifies the handling of such effects by discretizing the integrals into large sets of gather and light points and adaptively approximating the sum of all possible gather-light pair interactions.We create an implicit hierarchy, the product graph, over the gather-light pairs to rapidly and accurately approximate the contribution from hundreds of millions of pairs per pixel while only evaluating a tiny fraction (e.g., 200--1,000). We build upon the techniques of the prior Lightcuts method for complex illumination at a point, however, by considering the complete pixel integrals, we achieve much greater efficiency and scalability.Our example results demonstrate efficient handling of volume scattering, camera focus, and motion of lights, cameras, and geometry. For example, enabling high quality motion blur with 256x temporal sampling requires only a 6.7x increase in shading cost in a scene with complex moving geometry, materials, and illumination.

OpenSurfaces: a richly annotated catalog of surface appearance

The appearance of surfaces in real-world scenes is determined by the materials, textures, and context in which the surfaces appear. However, the datasets we have for visualizing and modeling rich surface appearance in context, in applications such as home remodeling, are quite limited. To help address this need, we present OpenSurfaces, a rich, labeled database consisting of thousands of examples of surfaces segmented from consumer photographs of interiors, and annotated with material parameters (reflectance, material names), texture information (surface normals, rectified textures), and contextual information (scene category, and object names). Retrieving usable surface information from uncalibrated Internet photo collections is challenging. We use human annotations and present a new methodology for segmenting and annotating materials in Internet photo collections suitable for crowdsourcing (e.g., through Amazons Mechanical Turk). Because of the noise and variability inherent in Internet photos and novice annotators, designing this annotation engine was a key challenge; we present a multi-stage set of annotation tasks with quality checks and validation. We demonstrate the use of this database in proof-of-concept applications including surface retexturing and material and image browsing, and discuss future uses. OpenSurfaces is a public resource available at http://opensurfaces.cs.cornell.edu/.