Estimating Neural Reflectance Field from Radiance Field using Tree Structures

We present a new method for estimating the Neural Reflectance Field (NReF) of an object from a set of posed multi-view images under unknown lighting. NReF represents 3D geometry and appearance of objects in a disentangled manner, and are hard to be estimated from images only. Our method solves this problem by exploiting the Neural Radiance Field (NeRF) as a proxy representation, from which we perform further decomposition. A high-quality NeRF decomposition relies on good geometry information extraction as well as good prior terms to properly resolve ambiguities between different components. To extract high-quality geometry information from radiance fields, we re-design a new ray-casting based method for surface point extraction. To efficiently compute and apply prior terms, we convert different prior terms into different type of filter operations on the surface extracted from radiance field. We then employ two type of auxiliary data structures, namely Gaussian KD-tree and octree, to support fast querying of surface points and efficient computation of surface filters during training. Based on this, we design a multi-stage decomposition optimization pipeline for estimating neural reflectance field from neural radiance fields. Extensive experiments show our method outperforms other state-of-the-art methods on different data, and enable high-quality free-view relighting as well as material editing tasks

Gravitational Lensing by Spinning Black Holes in Astrophysics, and in the Movie Interstellar

Interstellar is the first Hollywood movie to attempt depicting a black hole as it would actually be seen by somebody nearby. For this we developed a code called DNGR (Double Negative Gravitational Renderer) to solve the equations for ray-bundle (light-beam) propagation through the curved spacetime of a spinning (Kerr) black hole, and to render IMAX-quality, rapidly changing images. Our ray-bundle techniques were crucial for achieving IMAX-quality smoothness without flickering. This paper has four purposes: (i) To describe DNGR for physicists and CGI practitioners . (ii) To present the equations we use, when the camera is in arbitrary motion at an arbitrary location near a Kerr black hole, for mapping light sources to camera images via elliptical ray bundles. (iii) To describe new insights, from DNGR, into gravitational lensing when the camera is near the spinning black hole, rather than far away as in almost all prior studies. (iv) To describe how the images of the black hole Gargantua and its accretion disk, in the movie \emph{Interstellar}, were generated with DNGR. There are no new astrophysical insights in this accretion-disk section of the paper, but disk novices may find it pedagogically interesting, and movie buffs may find its discussions of Interstellar interesting.Comment: 46 pages, 17 figure

NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering

Recent advances in neural implicit fields enables rapidly reconstructing 3D geometry from multi-view images. Beyond that, recovering physical properties such as material and illumination is essential for enabling more applications. This paper presents a new method that effectively learns relightable neural surface using pre-intergrated rendering, which simultaneously learns geometry, material and illumination within the neural implicit field. The key insight of our work is that these properties are closely related to each other, and optimizing them in a collaborative manner would lead to consistent improvements. Specifically, we propose NeuS-PIR, a method that factorizes the radiance field into a spatially varying material field and a differentiable environment cubemap, and jointly learns it with geometry represented by neural surface. Our experiments demonstrate that the proposed method outperforms the state-of-the-art method in both synthetic and real datasets

Towards a filmic look and feel in real time computer graphics

Film footage has a distinct look and feel that audience can instantly recognize, making its replication desirable for computer generated graphics. This thesis presents methods capable of replicating significant portions of the film look and feel while being able to fit within the constraints imposed by real-time computer generated graphics on consumer hardware

Accelerating Missile Threat Engagement Simulations Using Personal Computer Graphics Cards

The 453rd Electronic Warfare Squadron supports on-going military operations by providing battlefield commanders with aircraft ingress and egress routes that minimize the risk of shoulder or ground-fired missile attacks on our aircraft. To determine these routes, the 453rd simulates engagements between ground-to-air missiles and allied aircraft to determine the probability of a successful attack. The simulations are computationally expensive, often requiring two-hours for a single 10-second missile engagement. Hundreds of simulations are needed to perform a complete risk assessment which includes evaluating the effectiveness of countermeasures such as flares, chaff, jammers, and missile warning systems. Thus, the need for faster simulations is acute. This research speeds up these mission critical simulations by using inexpensive commodity PC graphics cards to perform intensive image processing computations used to simulate a heat seeking missile\u27s tracking system. The innovative techniques developed in this research reduce execution time by 33% and incorporate a user-selectable fidelity feature to perform high-fidelity simulations when required. Furthermore, these image processing computations use only 5% of the available computational capacity of the graphics cards, providing a ready source of additional computational power for future simulation enhancements. Analysts can now meet shorter suspenses with more accurate products, ultimately enhancing the safety of Air Force pilots and their weapon systems. With ongoing operations in Iraq and Afghanistan, and a growing threat at home and abroad posed by the proliferation of man-portable missiles, the speed of these simulations play an important role in protecting forces and saving lives

Efficient Many-Light Rendering of Scenes with Participating Media

We present several approaches based on virtual lights that aim at capturing the light transport without compromising quality, and while preserving the elegance and efficiency of many-light rendering. By reformulating the integration scheme, we obtain two numerically efficient techniques; one tailored specifically for interactive, high-quality lighting on surfaces, and one for handling scenes with participating media

Compression, Modeling, and Real-Time Rendering of Realistic Materials and Objects

The realism of a scene basically depends on the quality of the geometry, the illumination and the materials that are used. Whereas many sources for the creation of three-dimensional geometry exist and numerous algorithms for the approximation of global illumination were presented, the acquisition and rendering of realistic materials remains a challenging problem. Realistic materials are very important in computer graphics, because they describe the reflectance properties of surfaces, which are based on the interaction of light and matter. In the real world, an enormous diversity of materials can be found, comprising very different properties. One important objective in computer graphics is to understand these processes, to formalize them and to finally simulate them. For this purpose various analytical models do already exist, but their parameterization remains difficult as the number of parameters is usually very high. Also, they fail for very complex materials that occur in the real world. Measured materials, on the other hand, are prone to long acquisition time and to huge input data size. Although very efficient statistical compression algorithms were presented, most of them do not allow for editability, such as altering the diffuse color or mesostructure. In this thesis, a material representation is introduced that makes it possible to edit these features. This makes it possible to re-use the acquisition results in order to easily and quickly create deviations of the original material. These deviations may be subtle, but also substantial, allowing for a wide spectrum of material appearances. The approach presented in this thesis is not based on compression, but on a decomposition of the surface into several materials with different reflection properties. Based on a microfacette model, the light-matter interaction is represented by a function that can be stored in an ordinary two-dimensional texture. Additionally, depth information, local rotations, and the diffuse color are stored in these textures. As a result of the decomposition, some of the original information is inevitably lost, therefore an algorithm for the efficient simulation of subsurface scattering is presented as well. Another contribution of this work is a novel perception-based simplification metric that includes the material of an object. This metric comprises features of the human visual system, for example trichromatic color perception or reduced resolution. The proposed metric allows for a more aggressive simplification in regions where geometric metrics do not simplif

Real-time Realistic Rendering Of Nature Scenes With Dynamic Lighting

Rendering of natural scenes has interested the scientific community for a long time due to its numerous applications. The targeted goal is to create images that are similar to what a viewer can see in real life with his/her eyes. The main obstacle is complexity: nature scenes from real life contain a huge number of small details that are hard to model, take a lot of time to render and require a huge amount of memory unavailable in current computers. This complexity mainly comes from geometry and lighting. The goal of our research is to overcome this complexity and to achieve real-time rendering of nature scenes while providing visually convincing dynamic global illumination. Our work focuses on grass and trees as they are commonly visible in everyday life. We handle geometry and lighting complexities for grass to render millions of grass blades interactively with dynamic lighting. As for lighting complexity, we address real-time rendering of trees by proposing a lighting model that handles indirect lighting. Our work makes extensive use of the current generation of Graphics Processing Units (GPUs) to meet the real-time requirement and to leave the CPU free to carry out other tasks