Graphics processing units (GPUs) power today’s fastest supercomputers, are the dominant platform for deep learning, and provide the intelligence for devices ranging from self-driving cars to robots and smart cameras. They also generate compelling photorealistic images at real-time frame rates. GPUs have evolved by adding features to support new use cases. NVIDIA’s GeForce 256, the first GPU, was a dedicated processor for real-time graphics, an application that demands large amounts of floating-point arithmetic for vertex and fragment shading computations and high memory bandwidth.

Virtual reality drawing applications let users draw 3D shapes using brushes that form ribbon shaped, or ruled-surface, strokes. Each ribbon is uniquely defined by its user-specified ruling length, path, and the ruling directions at each point along this path. Existing brushes use the trajectory of a handheld controller in 3D space as the ribbon path, and compute the ruling directions using a fixed mapping from a specific controller coordinate-frame axis.

Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time. These methods, which we call neural fields, have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation. However, due to rapid progress in a short time, many papers exist but a comprehensive review and formulation of the problem has not yet emerged.

Reference datasets are a key tool in the creation of new algorithms. They allow us to compare different existing solutions and identify problems and weaknesses during the development of new algorithms. The signed distance function (SDF) is enjoying a renewed focus of research activity in computer graphics, but until now there has been no standard reference dataset of such functions. We present a database of 63 curated, optimized, and regularized functions of varying complexity.

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. SDFs encode 3D surfaces with a function of position that returns the closest distance to a surface. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics applications.

Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art results are obtained by conditioning a neural approximation with a lookup from trainable feature grids that take on part of the learning task and allow for smaller, more efficient neural networks. Unfortunately, these feature grids usually come at the cost of significantly increased memory consumption compared to stand-alone neural network models.

We introduce a general framework for transforming biased estimators into unbiased and consistent estimators for the same quantity. We show how several existing unbiased and consistent estimation strategies in rendering are special cases of this framework, and are part of a broader debiasing principle. We provide a recipe for constructing estimators using our generalized framework and demonstrate its applicability by developing novel unbiased forms of transmittance estimation, photon mapping, and finite differences.

Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator be trained "blindly"? Leveraging the semantic power of large scale Contrastive-Language-Image-Pre-training (CLIP) models, we present a text-driven method that allows shifting a generative model to new domains, without having to collect even a single image.

Many sketch processing applications target precise vector drawings with accurately specified stroke intersections, yet free-form artist drawn sketches are typically inexact: strokes that are intended to intersect often stop short of doing so. While human observers easily perceive the artist intended stroke connectivity, manually, or even semi-manually, correcting drawings to generate correctly connected outputs is tedious and highly time consuming.

Vector clip-art images consist of regions bounded by a network of vector curves. Users often wish to reshape, or rescale, existing clip-art images by changing the locations, proportions, or scales of different image elements. When reshaping images depicting synthetic content they seek to preserve global and local structures.