Implicit incompressible SPH (IISPH) solves a pressure Poisson equation (PPE). While the solution of the PPE provides pressure at fluid samples, the embedded boundary handling does not compute pressure at boundary samples. Instead, IISPH uses various approximations to remedy this deficiency. In this paper, we illustrate the issues of these IISPH approximations. We particularly derive Pressure Boundaries, a novel boundary handling that overcomes previous IISPH issues by the computation of physically meaningful pressure values at boundary samples. This is basically achieved with an extended PPE. We provide a detailed description of the approach that focuses on additional technical challenges due to the incorporation of boundary samples into the PPE. We therefore use volume-centric SPH discretizations instead of typically used density-centric ones. We further analyze the properties of the proposed boundary handling and compare it to the previous IISPH boundary handling. In addition to the fact that the proposed boundary handling provides physically meaningful pressure and pressure gradients at boundary samples, we show further benefits such as reduced pressure oscillations, improved solver convergence and larger possible time steps.
In shape analysis and matching, it is often important to encode information about the relation between a given point and other points on a shape, namely its context. To this aim we propose a theoretically sound and efficient approach for the simulation of a discrete time evolution process that runs through all the possible geodesic paths between pairs of points on a surface represented as a triangle mesh in the discrete setting. We demonstrate how this construction can be used to efficiently construct a multiscale point descriptor, called Discrete time Evolution Process Descriptor, which robustly encodes the structure of geodesic neighborhoods of a point across multiple scales. Our work is closely related to the methods based on diffusion geometry, and derived signatures such as the HKS or the WKS, and provides information that is complementary to these descriptors. We demonstrate through extensive experimental evaluation that our descriptor shows promising results in both shape matching and shape segmentation scenarios. Our approach outperforms similar methods especially when comparing shapes undergoing large non-isometric deformations and in the presence of missing parts.
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision. Given a collection of 3D shapes spanning multiple object categories and styles, we perform style co-analysis over projected feature lines of each 3D shape and then back project the learned style features onto the 3D shapes. Our core analysis pipeline starts with mid-level patch sampling and pre-selection of candidate style patches. Projective features are then encoded via patch convolution. Multi-view feature integration and style clustering are carried out under the frame work of partially shared latent factor (PSLF) learning, a multi-view feature learning scheme. PSLF achieves effective multi-view feature fusion by distilling and exploiting consistent and complementary feature information from multiple views, while also selecting style patches from the candidates. Our style analysis approach supports both unsupervised and semi-supervised analysis. For the latter, our method accepts both user-specified shape labels and style-ranked triplets as clustering constraints. We demonstrate results from 3D shape style analysis and patch localization as well as improvements over state-of-the-art methods. We also present several applications enabled by our style analysis.
The depth resolution achieved by a continuous wave time-of-flight (C-ToF) imaging system is determined by the coding (modulation and demodulation) functions that it uses. Almost all current C-ToF systems use sinusoid or square coding functions, resulting in a limited depth resolution. In this paper, we present a mathematical framework for exploring and characterizing the space of C-ToF coding functions in a geometrically intuitive space. Using this framework, we design families of novel coding functions that are based on Hamiltonian cycles on hypercube graphs. Given a fixed total source power and acquisition time, the new Hamiltonian coding scheme can achieve up to an order of magnitude higher resolution as compared to the current state-of-the art methods, especially in low SNR settings. We also develop a comprehensive physically-motivated simulator for C-ToF cameras that can be used to evaluate various coding schemes prior to a real hardware implementation. Since most off-the-shelf C-ToF sensors use sinusoid or square functions, we develop a hardware prototype that can implement a wide range of coding functions. Using this prototype and our software simulator, we demonstrate the performance advantages of the proposed Hamiltonian coding functions in a wide range of imaging settings.
Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this paper a deep learning approach that is instead trained on unpaired data, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits, rather than just a "black box" result. Through quantitative comparisons and user studies, we show that our retouching results surpass those of strong baselines.
We present an integrated approach for reconstructing high-fidelity 3D models using consumer RGB-D cameras. RGB-D registration and reconstruction algorithms are prone to errors from scanning noise, making it hard to perform 3D reconstruction accurately. The key idea of our method is to assign a probabilistic uncertainty model to each depth measurement, which then guides the scan alignment and depth fusion. This allows us to effectively handle inherent noise and distortion in depth maps while keeping the overall scan registration procedure under the iterative closest point (ICP) frame-work for simplicity and efficiency. We further introduce a local-to-global, submap-based, and uncertainty-aware global pose optimization scheme to improve scalability and guarantee global model consistency. Finally, we have implemented the proposed algorithm on the GPU, achieving real-time 3D scanning frame rates and updating the reconstructed model on-the-fly. Experimental results on simulated and real-world data demonstrate that the proposed method outperforms state-of-the-art systems in terms of the accuracy of both recovered camera trajectories and reconstructed models.
We present a discrete theory for modeling developable surfaces as quadrilateral meshes satisfying simple angle constraints. The basis of our model is a lesser known characterization of developable surfaces as manifolds that can be parameterized through orthogonal geodesics. Our model is simple, local, and, unlike previous works, it does not directly encode the surface rulings. This allows us to model continuous deformations of discrete developable surfaces independently of their decomposition into torsal and planar patches or the surface topology. We prove and experimentally demonstrate strong ties to smooth developable surfaces, including a theorem stating that every sampling of the smooth counterpart satisfies our constraints up to second order. We further present an extension of our model that enables a local definition of discrete isometry. We demonstrate the effectiveness of our discrete model in a developable surface editing system, as well as computation of an isometric interpolation between isometric discrete developable shapes.
Sony Imageworks implementation of the Arnold renderer is a fork of the commercial product of the same name, which has evolved independently since around 2009. This paper focuses on the design choices that are unique to this version and have tailored the renderer to the specific requirements of film rendering at our studio. We detail our approach to subdivision surface tessellation, hair rendering, sampling and variance reduction techniques, as well as a description of our open source texturing and shading language components. We also discuss some ideas we once implemented but have since discarded to highlight the evolution of the software over the years
Walt Disney Animation Studios has transitioned to path-traced global illumination as part of a progression of brute-force physically based rendering in the name of artist efficiency. To achieve this without compromising our geometric or shading complexity, we built our Hyperion renderer based on a novel architecture that extracts traversal and shading coherence from large, sorted ray batches. In this paper we describe our architecture and discuss our design decisions. We also explain how we are able to provide artistic control in a physically based renderer, and we demonstrate through case studies how we have benefited from having a proprietary renderer that can evolve with production needs.
Non-linear hyperelastic energies play a key role in capturing the fleshy appearance of virtual characters. Real-world, volume-preserving biological tissues have Poisson's ratios near 1/2, but numerical simulation within this regime is notoriously challenging. In order to robustly capture these visual characteristics, we present a novel version of Neo-Hookean elasticity. Our model maintains the fleshy appearance of the Neo-Hookean model, exhibits superior volume preservation, and is robust to extreme kinematic rotations and inversions. We obtain closed-form expressions for the eigenvalues and eigenvectors of all of the system's components, which allows us to directly project the Hessian to semi-positive-definiteness, and also leads to insights into the numerical behavior of the material. These findings also inform the design of more sophisticated hyperelastic models, which we explore by applying our analysis to Fung and Arruda-Boyce elasticity. We provide extensive comparisons against existing material models.
Many applications in rendering rely on integrating functions over spherical polygons. Here, numerical integration is most commonly adopted, as only a limited subset of these problems admit analytic solutions. We present a closed-form solution for computing the integral of arbitrary polynomial functions over spherical polygons. Our solution, based on zonal decompositions of spherical integrands and discrete contour integration, greatly expands the class of integrands and domains admissible to analytic solutions. Our method is simple, efficient, and scales linearly in the integrands harmonic expansion. As such, we are able to demonstrate new analytic solutions to many problems in rendering, including surface and volumetric shading with arbitrary BRDFs and phase functions, hierarchical product importance sampling for local light sources, and fast basis projection for interac- tive rendering. Moreover, we show how to handle general, non-polynomial integrands by leveraging our analytic solutions in a Monte Carlo setting using control variates. Our technique is general, accurate, and competitive with (or faster than) numeric integration for a broad class of problems, both in offline and interactive contexts. Our implementation is simple, relying only on self-contained spherical harmonic evaluation and discrete contour integration routines, and we release a full source implementation (< 650 lines of code).
We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.
Quadrotor drones equipped with high quality cameras have rapidely raised as novel, cheap and stable devices for filmmakers. While professional drone pilots can create aesthetically pleasing videos in short time, the smooth and cinematographic control of a camera drone remains challenging for most users, despite recent tools that either automate part of the process or enable the manual design of waypoints to create drone trajectories. This paper proposes to move a step further towards more accessible cinematographic drones by designing techniques to automatically or interactively plan quadrotor drone motions in 3D dynamic environments that satisfy both cinematographic and physical quadrotor constraints. We first propose the design of a Drone Toric Space as a dedicated camera parameter space with embedded constraints and derive some intuitive on-screen viewpoint manipulators. Second, we propose a specific path planning technique which ensures both that cinematographic properties can be enforced along the path, and that the path is physically feasible by a quadrotor drone. At last, we build on the Drone Toric Space and the specific path planning technique to coordinate the motion of multiple drones around dynamic targets. A number of results then demonstrate the interactive and automated capacities of our approaches on a number of use-cases.
Pixar's RenderMan renderer is used to render all of Pixar's films, and by many film studios to render visual effects for live-action movies. RenderMan started as a scanline renderer based on the Reyes algorithm, and was extended over the years with ray tracing and several global illumination algorithms. This paper describes the modern version of RenderMan, a new architecture for an extensible and programmable path tracer with many features that are essential to handle the fiercely complex scenes in movie production. Users can write their own materials using a bxdf interface, and their own light transport algorithms using an integrator interface -- or they can use the materials and light transport algorithms provided with RenderMan. Complex geometry and textures are handled with efficient multi-resolution representations, with resolution chosen using path differentials. We trace rays and shade ray hit points in medium-sized groups, which provides the benefits of SIMD execution without excessive memory overhead or data streaming. The path-tracing architecture handles surface, subsurface, and volume scattering. We show examples of the use of path tracing, bidirectional path tracing, VCM, and UPBP light transport algorithms. We also describe our progressive rendering for interactive use and our adaptation of denoising techniques.
Distributing a simulation across many machines can drastically speed up computations and increase detail. The computing cloud provides tremendous computing resources, but weak service guarantees force programs to manage significant system complexity: nodes, networks, and storage occasionally perform poorly or fail. We describe Halo, a system that automatically distributes grid-based and hybrid simulations across cloud computing nodes. The main simulation loop is written as simple sequential code and launches distributed computations across many cores. The simulation on each core runs as if it is stand-alone: Halo automatically stitches these multiple simulations into a single, larger one. To do this efficiently, Halo introduces a four-layer data model that translates between the contiguous, geometric objects used by simulation libraries and the replicated, fine-grained objects managed by its underlying cloud computing runtime. Using PhysBAM particle level-set fluid simulations, we demonstrate that Halo can run higher detail simulations faster, distribute simulations on up to 512 cores and run enormous simulations ($1024^3$ cells). Halo automatically manages these distributed simulations, balancing load across nodes and recovering from failures. Implementations of PhysBAM water and smoke simulations as well as an open source heat-diffusion simulations show Halo is general and can support complex simulations.
The Manuka rendering architecture has been designed to enable visually rich computer generated imagery for visual effects in movie production. This means supporting extremely complex geometry, texturing and shading. In the current generation of renderers, it is essential to support very accurate global illumination as a means to naturally tie together different assets in a picture. This is achieved with Monte Carlo path tracing, using a paradigm often called shade on hit, in which the renderer alternates tracing rays with running shaders on the various ray hits. The shaders take the role of generating the inputs of the local material structure which is then used by path sampling logic to evaluate contributions and to inform what further rays to cast through the scene. We propose a shade before hit paradigm instead and minimise I/O strain on the system, leveraging locality of reference by running pattern generation shaders before we execute light transport simulation by path sampling. We describe a full architecture built around this approach, featuring spectral light transport and a flexible implementation of multiple importance sampling, resulting in a system able to support a comparable amount of extensibility to what made the reyes rendering architecture successful over many decades.
Arnold is a physically-based renderer for feature-length animation and visual effects. Arnold has been created to take on the challenge of making the simple and elegant approach of brute-force Monte Carlo path tracing practical for production rendering. Achieving this requires building a robust piece of ray tracing software that can ingest large amounts of geometry with detailed shading and lighting and produce images with high fidelity, while scaling well with the available memory and processing power. Arnold's guiding principles are to expose as few controls as possible, provide rapid feedback to artists, and adapt to various production workflows. In this paper we describe its architecture with a focus on the design and implementation choices made during its evolutionary development to meet the aforementioned requirements and goals. Arnold's workhorse is a unidirectional path tracer that avoids the use of hard to manage and artifact-prone caching and sits on top of a ray tracing engine optimized to shoot and shade billions of spatially incoherent rays throughout a scene. A comprehensive API provides the means to configure and extend the system's functionality, to describe a scene, render it, and write the results to dis
We introduce n-field splines, an approach for modeling tangential n-fields on surfaces that supports interpolation and alignment constraints, placing singularities and local editing of n-fields and provides real-time responses. The approach is based on novel biharmonic and m-harmonic energies for n-fields on surface meshes and the integration of hard constraints to the resulting optimization problems. Real-time computation rates are achieved by a model reduction approach employing a Fourier-like n-fields decomposition, which associates frequencies and modes to n-fields on surfaces. To demonstrate the benefits of the proposed n-field modeling approach, we use it for controlling stroke directions in line-art drawings of surfaces and for the modeling of anisotropic BRDFs which define the reflection behavior of surfaces.
We introduce FontCode, an information embedding technique for text documents. Provided a text document with specific fonts, our method embeds user-specified information in the text by perturbing the glyphs of text characters while preserving the text content. We devise an algorithm to choose unobtrusive yet machine-recognizable glyph perturbations, leveraging a recently developed generative model that alters the glyphs of each character continuously on a font manifold. We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document. We also present a glyph recognition method that recovers the embedded information from an encoded document stored as a vector graphic or pixel image, or even on a printed paper. In addition, we introduce a new error-correction coding scheme that rectifies a certain number of recognition errors. Lastly, we demonstrate that our technique enables a wide array of applications, using it as a text document metadata holder, an unobtrusive optical barcode, a cryptographic message embedding scheme, and a text document signature.