bluuebunny/crossref_metadata_2025 huggingface dataset:
Geohazard assessment related to submarine instabilities in Bjørnafjorden, Norway
Authors: Carlton Brian , Vanneste Maarten , Forsberg Carl Fredrik , Knudsen Siren , Løvholt Finn , Kvalstad Tore , Holm Søren , Kjennbakken Heidi , Mazhar Muhammad Adeel , Degago Samson , Haflidason Haflidi
Published: July 2018

This paper presents the geohazard assessment for a proposed bridge across Bjørnafjorden in western Norway. The fjord is
c.
5 km wide with a maximum depth of 550 m at the proposed bridge crossing. The main geohazards of concern are submarine slope instabilities. To identify locations of instability, their susceptibility to failure, and their potential runout distances, we performed the following analyses: (1) static and pseudo-static limit equilibrium analyses for the entire fjord crossing area; (2) 1D seismic slope stability sensitivity analyses for different slope angles and soil depths; (3) 2D static and pseudo-static finite element analyses for selected profiles; (4) back-analysis of a palaeolandslide; and (5) quasi-2D and quasi-3D landslide dynamic simulations calibrated using results from the back-analysis. The workflow progresses from simplified to more advanced analyses focusing on the most critical locations. The results show that the soils in many locations of the fjord are potentially unstable and could be the loci of landslides and debris flows. The evidence of numerous palaeosubmarine landslides identified on geophysical records reinforces this conclusion. However, the landslide triggers and timing are currently unknown. This paper demonstrates the need for comprehensive and multidisciplinary geohazard analyses for any infrastructure projects conducted in fjords.
Mechanical characterization of structured sheet materials
Authors: Schumacher Christian , Marschner Steve , Gross Markus , Thomaszewski Bernhard
Published: July 2018

We propose a comprehensive approach to characterizing the mechanical properties of structured sheet materials, i.e., planar rod networks whose mechanics and aesthetics are inextricably linked. We establish a connection between the complex mesoscopic deformation behavior of such structures and their macroscopic elastic properties through numerical homogenization. Our approach leverages 3D Kirchhoff rod simulation in order to capture nonlinear effects for both in-plane and bending deformations. We apply our method to different families of structures based on isohedral tilings---a simple yet extensive and aesthetically interesting group of space-filling patterns. We show that these tilings admit a wide range of material properties, and our homogenization approach allows us to create concise and intuitive descriptions of a material's direction-dependent macromechanical behavior that are easy to communicate even to non-experts. We perform this characterization for an extensive set of structures and organize these data in a material browser to enable efficient forward exploration of the aesthetic-mechanical space of structured sheet materials. We also propose an inverse design method to automatically find structure parameters that best approximate a user-specified target behavior.
Anderson acceleration for geometry optimization and physics simulation
Authors: Peng Yue , Deng Bailin , Zhang Juyong , Geng Fanyu , Qin Wenjie , Liu Ligang
Published: July 2018

Many computer graphics problems require computing geometric shapes subject to certain constraints. This often results in non-linear and non-convex optimization problems with globally coupled variables, which pose great challenge for interactive applications. Local-global solvers developed in recent years can quickly compute an approximate solution to such problems, making them an attractive choice for applications that prioritize efficiency over accuracy. However, these solvers suffer from lower convergence rate, and may take a long time to compute an accurate result. In this paper, we propose a simple and effective technique to accelerate the convergence of such solvers. By treating each local-global step as a fixed-point iteration, we apply Anderson acceleration, a well-established technique for fixed-point solvers, to speed up the convergence of a local-global solver. To address the stability issue of classical Anderson acceleration, we propose a simple strategy to guarantee the decrease of target energy and ensure its global convergence. In addition, we analyze the connection between Anderson acceleration and quasi-Newton methods, and show that the canonical choice of its mixing parameter is suitable for accelerating local-global solvers. Moreover, our technique is effective beyond classical local-global solvers, and can be applied to iterative methods with a common structure. We evaluate the performance of our technique on a variety of geometry optimization and physics simulation problems. Our approach significantly reduces the number of iterations required to compute an accurate result, with only a slight increase of computational cost per iteration. Its simplicity and effectiveness makes it a promising tool for accelerating existing algorithms as well as designing efficient new algorithms.
Space-time tomography for continuously deforming objects
Authors: Zang Guangming , Idoughi Ramzi , Tao Ran , Lubineau Gilles , Wonka Peter , Heidrich Wolfgang
Published: July 2018

X-ray computed tomography (CT) is a valuable tool for analyzing objects with interesting internal structure or complex geometries that are not accessible with optical means. Unfortunately, tomographic reconstruction of complex shapes requires a multitude (often hundreds or thousands) of projections from different viewpoints. Such a large number of projections can only be acquired in a time-sequential fashion. This significantly limits the ability to use x-ray tomography for either objects that undergo uncontrolled shape change at the time scale of a scan, or else for analyzing dynamic phenomena, where the motion itself is under investigation.

In this work, we present a non-parametric space-time tomographic method for tackling such dynamic settings. Through a combination of a new CT image acquisition strategy, a space-time tomographic image formation model, and an alternating, multi-scale solver, we achieve a general approach that can be used to analyze a wide range of dynamic phenomena. We demonstrate our method with extensive experiments on both real and simulated data.
Megapixel adaptive optics
Authors: Wang Congli , Fu Qiang , Dun Xiong , Heidrich Wolfgang
Published: July 2018

Adaptive optics has become a valuable tool for correcting minor optical aberrations in applications such as astronomy and microscopy. However, due to the limited resolution of both the wavefront sensing and the wavefront correction hardware, it has so far not been feasible to use adaptive optics for correcting large-scale waveform deformations that occur naturally in regular photography and other imaging applications.

In this work, we demonstrate an adaptive optics system for regular cameras. We achieve a significant improvement in focus for large wavefront distortions by improving upon a recently developed high resolution coded wavefront sensor, and combining it with a spatial phase modulator to create a megapixel adaptive optics system with unprecedented capability to sense and correct large distortions.
Fast and deep deformation approximations
Authors: Bailey Stephen W. , Otte Dave , Dilorenzo Paul , O'Brien James F.
Published: July 2018

Character rigs are procedural systems that compute the shape of an animated character for a given pose. They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance. When comparing film-quality character rigs with those designed for real-time applications, there is typically a substantial and readily apparent difference in the quality of the mesh deformations. Real-time rigs are limited by a computational budget and often trade realism for performance. Rigs for film do not have this same limitation, and character riggers can make the rig as complicated as necessary to achieve realistic deformations. However, increasing the rig complexity slows rig evaluation, and the animators working with it can become less efficient and may experience frustration. In this paper, we present a method to reduce the time required to compute mesh deformations for film-quality rigs, allowing better interactivity during animation authoring and use in real-time games and applications. Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions. The linear deformations are computed directly from the transformations of the rig's underlying skeleton. We use deep learning methods to approximate the remaining nonlinear portion. In the examples we show from production rigs used to animate lead characters, our approach reduces the computational time spent on evaluating deformations by a factor of 5X-10X. This significant savings allows us to run the complex, film-quality rigs in real-time even when using a CPU-only implementation on a mobile device.
Designing patterns using triangle-quad hybrid meshes
Authors: Peng Chi-Han , Pottmann Helmut , Wonka Peter
Published: July 2018

We present a framework to generate mesh patterns that consist of a
hybrid
of both triangles and quads. Given a 3D surface, the generated patterns fit the surface boundaries and curvatures. Such regular and near regular triangle-quad hybrid meshes provide two key advantages: first, novel-looking polygonal patterns achieved by mixing different arrangements of triangles and quads together; second, a finer discretization of angle deficits than utilizing triangles or quads alone. Users have controls over the generated patterns in global and local levels. We demonstrate applications of our approach in architectural geometry and pattern design on surfaces.
Gaussian material synthesis
Authors: Zsolnai-Fehér Károly , Wonka Peter , Wimmer Michael
Published: July 2018

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumination, which makes this process impracticable for real-world workflows. Our neural network eliminates this bottleneck by providing high-quality image predictions in real time, after which it is possible to pick the desired materials from a gallery and assign them to a scene in an intuitive manner. Workflow timings against Disney's "principled" shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling. Similarly, expert users experience a significant decrease in the total modeling time when populating a scene with materials. Furthermore, our proposed solution also offers controllable recommendations and a novel latent space variant generation step to enable the real-time fine-tuning of materials without requiring any domain expertise.
Perception-driven semi-structured boundary vectorization
Authors: Hoshyari Shayan , Dominici Edoardo Alberto , Sheffer Alla , Carr Nathan , Wang Zhaowen , Ceylan Duygu , Shen I-Chao
Published: July 2018

Artist-drawn images with distinctly colored, piecewise continuous boundaries, which we refer to as
semi-structured imagery
, are very common in online raster databases and typically allow for a perceptually unambiguous mental vector interpretation. Yet, perhaps surprisingly, existing vectorization algorithms frequently fail to generate these viewer-expected interpretations on such imagery. In particular, the vectorized region boundaries they produce frequently diverge from those anticipated by viewers. We propose a new approach to region boundary vectorization that targets semi-structured inputs and leverages observations about human perception of shapes to generate vector images consistent with viewer expectations. When viewing raster imagery observers expect the vector output to be an accurate representation of the raster input. However, perception studies suggest that viewers implicitly account for the lossy nature of the rasterization process and mentally smooth and simplify the observed boundaries. Our core algorithmic challenge is to balance these conflicting cues and obtain a piecewise continuous vectorization whose discontinuities, or corners, are aligned with human expectations.

Our framework centers around a simultaneous spline fitting and corner detection method that combines a learned metric, that approximates human perception of boundary discontinuities on raster inputs, with perception-driven algorithmic discontinuity analysis. The resulting method balances local cues provided by the learned metric with global cues obtained by balancing simplicity and continuity expectations. Given the finalized set of corners, our framework connects those using simple, continuous curves that capture input regularities. We demonstrate our method on a range of inputs and validate its superiority over existing alternatives via an extensive comparative user study.
Reconstructing scenes with mirror and glass surfaces
Authors: Whelan Thomas , Goesele Michael , Lovegrove Steven J. , Straub Julian , Green Simon , Szeliski Richard , Butterfield Steven , Verma Shobhit , Newcombe Richard
Published: July 2018

Planar reflective surfaces such as glass and mirrors are notoriously hard to reconstruct for most current 3D scanning techniques. When treated naïvely, they introduce duplicate scene structures, effectively destroying the reconstruction altogether. Our key insight is that an easy to identify structure attached to the scanner---in our case an AprilTag---can yield reliable information about the existence and the geometry of glass and mirror surfaces in a scene. We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two. Furthermore, our system can automatically segment observations of multiple reflective surfaces in a scene based on their estimated planes and locations. In the proposed setup, minimal additional hardware is needed to create high-quality results. We demonstrate this using reconstructions of several scenes with a variety of real mirrors and glass.
Stereo magnification
Authors: Zhou Tinghui , Tucker Richard , Flynn John , Fyffe Graham , Snavely Noah
Published: July 2018

The view synthesis problem---generating novel views of a scene from known imagery---has garnered recent attention due in part to compelling applications in virtual and augmented reality. In this paper, we explore an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including VR cameras and now-widespread dual-lens camera phones. We call this problem
stereo magnification
, and propose a learning framework that leverages a new layered representation that we call
multiplane images
(MPIs). Our method also uses a massive new data source for learning view extrapolation: online videos on YouTube. Using data mined from such videos, we train a deep network that predicts an MPI from an input stereo image pair. This inferred MPI can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline. We show that our method compares favorably with several recent view synthesis methods, and demonstrate applications in magnifying narrow-baseline stereo images.
An advection-reflection solver for detail-preserving fluid simulation
Authors: Zehnder Jonas , Narain Rahul , Thomaszewski Bernhard
Published: July 2018

Advection-projection methods for fluid animation are widely appreciated for their stability and efficiency. However, the projection step dissipates energy from the system, leading to artificial viscosity and suppression of small-scale details. We propose an alternative approach for detail-preserving fluid animation that is surprisingly simple and effective. We replace the energy-dissipating projection operator applied at the end of a simulation step by an
energy-preserving reflection
operator applied at mid-step. We show that doing so leads to two orders of magnitude reduction in energy loss, which in turn yields vastly improved detail-preservation. We evaluate our reflection solver on a set of 2D and 3D numerical experiments and show that it compares favorably to state-of-the-art methods. Finally, our method integrates seamlessly with existing projection-advection solvers and requires very little additional implementation.
Toonsynth
Authors: Dvorožnák Marek , Li Wilmot , Kim Vladimir G. , Sýkora Daniel
Published: July 2018

We present a new example-based approach for synthesizing hand-colored cartoon animations. Our method produces results that preserve the specific visual appearance and stylized motion of manually authored animations without requiring artists to draw every frame from scratch. In our framework, the artist first stylizes a limited set of known source skeletal animations from which we extract a
style-aware puppet
that encodes the appearance and motion characteristics of the artwork. Given a new target skeletal motion, our method automatically transfers the style from the source examples to create a hand-colored target animation. Compared to previous work, our technique is the first to preserve both the detailed visual appearance and stylized motion of the original hand-drawn content. Our approach has numerous practical applications including traditional animation production and content creation for games.
Synthetic depth-of-field with a single-camera mobile phone
Authors: Wadhwa Neal , Garg Rahul , Jacobs David E. , Feldman Bryan E. , Kanazawa Nori , Carroll Robert , Movshovitz-Attias Yair , Barron Jonathan T. , Pritch Yael , Levoy Marc
Published: July 2018

Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optically, as their short focal lengths and small apertures capture nearly all-in-focus images. We present a system to computationally synthesize shallow depth-of-field images with a single mobile camera and a single button press. If the image is of a person, we use a person segmentation network to separate the person and their accessories from the background. If available, we also use dense
dual-pixel
auto-focus hardware, effectively a 2-sample light field with an approximately 1 millimeter baseline, to compute a dense depth map. These two signals are combined and used to render a defocused image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile phone, is fully automatic, and is robust enough to be used by non-experts. The modular nature of our system allows it to degrade naturally in the absence of a dual-pixel sensor or a human subject.
In the blink of an eye
Authors: Langbehn Eike , Steinicke Frank , Lappe Markus , Welch Gregory F. , Bruder Gerd
Published: July 2018

Immersive computer-generated environments (akavirtual reality, VR) are limited by the physical space around them, e.g., enabling natural walking in VR is only possible by perceptually-inspired locomotion techniques such as redirected walking (RDW). We introduce a completely new approach to imperceptible position and orientation redirection that takes advantage of the fact that even healthy humans are functionally blind for circa ten percent of the time under normal circumstances due to motor processes preventing light from reaching the retina (such as eye blinks) or perceptual processes suppressing degraded visual information (such as blink-induced suppression). During such periods of missing visual input,change blindnessoccurs, which denotes the inability to perceive a visual change such as the motion of an object or self-motion of the observer. We show that this phenomenon can be exploited in VR by synchronizing the computer graphics rendering system with the human visual processes for imperceptible camera movements, in particular to implement position and orientation redirection. We analyzed human sensitivity to such visual changes with detection thresholds, which revealed that commercial off-the-shelf eye trackers and head-mounted displays suffice to translate a user by circa 4 -- 9 cm and rotate the user by circa 2 -- 5 degrees in any direction, which could be accumulated each time the user blinks. Moreover, we show the potential for RDW, whose performance could be improved by approximately 50% when using our technique.
CoreCavity
Authors: Nakashima Kazutaka , Auzinger Thomas , Iarussi Emmanuel , Zhang Ran , Igarashi Takeo , Bickel Bernd
Published: July 2018

Molding is a popular mass production method, in which the initial expenses for the mold are offset by the low per-unit production cost. However, the physical fabrication constraints of the molding technique commonly restrict the shape of moldable objects. For a complex shape, a decomposition of the object into moldable parts is a common strategy to address these constraints, with plastic model kits being a popular and illustrative example. However, conducting such a decomposition requires considerable expertise, and it depends on the technical aspects of the fabrication technique, as well as aesthetic considerations. We present an interactive technique to create such decompositions for two-piece molding, in which each part of the object is cast between two rigid mold pieces. Given the surface description of an object, we decompose its thin-shell equivalent into moldable parts by first performing a coarse decomposition and then utilizing an active contour model for the boundaries between individual parts. Formulated as an optimization problem, the movement of the contours is guided by an energy reflecting fabrication constraints to ensure the
moldability
of each part. Simultaneously the user is provided with editing capabilities to enforce aesthetic guidelines. Our interactive interface provides control of the contour positions by allowing, for example, the alignment of part boundaries with object features. Our technique enables a novel workflow, as it empowers novice users to explore the design space, and it generates fabrication-ready two-piece molds that can be used either for casting or industrial injection molding of free-form objects.
Support-free volume printing by multi-axis motion
Authors: Dai Chengkai , Wang Charlie C. L. , Wu Chenming , Lefebvre Sylvain , Fang Guoxin , Liu Yong-Jin
Published: July 2018

This paper presents a new method to fabricate 3D models on a robotic printing system equipped with multi-axis motion. Materials are accumulated inside the volume along curved tool-paths so that the need of supporting structures can be tremendously reduced - if not completely abandoned - on all models. Our strategy to tackle the challenge of tool-path planning for multi-axis 3D printing is to perform two successive decompositions, first volume-to-surfaces and then surfaces-to-curves. The volume-to-surfaces decomposition is achieved by optimizing a scalar field within the volume that represents the fabrication sequence. The field is constrained such that its iso-values represent curved layers that are supported from below, and present a convex surface affording for collision-free navigation of the printer head. After extracting all curved layers, the surfaces-to-curves decomposition covers them with tool-paths while taking into account constraints from the robotic printing system. Our method successfully generates tool-paths for 3D printing models with large overhangs and high-genus topology. We fabricated several challenging cases on our robotic platform to verify and demonstrate its capabilities.
Polyhedral voronoi diagrams for additive manufacturing
Authors: Martínez Jonàs , Hornus Samuel , Song Haichuan , Lefebvre Sylvain
Published: July 2018

A critical advantage of additive manufacturing is its ability to fabricate complex small-scale structures. These microstructures can be understood as a
metamaterial
: they exist at a much smaller scale than the volume they fill, and are collectively responsible for an average elastic behavior different from that of the base printing material making the fabricated object lighter and/or flexible along specific directions. In addition, the average behavior can be graded spatially by progressively modifying the micro structure geometry.

The definition of a microstructure is a careful trade-off between the geometric requirements of manufacturing and the properties one seeks to obtain within a shape: in our case a wide range of elastic behaviors. Most existing microstructures are designed for stereolithography (SLA) and laser sintering (SLS) processes. The requirements are however different than those of continuous deposition systems such as fused filament fabrication (FFF), for which there is currently a lack of microstructures enabling graded elastic behaviors.

In this work we introduce a novel type of microstructures that
strictly enforce
all the requirements of FFF-like processes: continuity, self-support and overhang angles. They offer a range of orthotropic elastic responses that can be graded spatially. This allows to fabricate parts usually reserved to the most advanced technologies on widely available inexpensive printers that also benefit from a continuously expanding range of materials.
Shape representation by zippables
Authors: Schüller Christian , Poranne Roi , Sorkine-Hornung Olga
Published: July 2018

Fabrication from developable parts is the basis for arts such as papercraft and needlework, as well as modern architecture and CAD in general, and it has inspired much research. We observe that the assembly of complex 3D shapes created by existing methods often requires first fabricating many small parts and then carefully following instructions to assemble them together. Despite its significance, this error prone and tedious process is generally neglected in the discussion. We present the concept of
zippables - single
, two dimensional, branching, ribbon-like pieces of fabric that can be quickly zipped up without any instructions to form 3D objects. Our inspiration comes from the so-called
zipit
bags [zipit 2017], which are made of a single, long ribbon with a zipper around its boundary. In order to "assemble" the bag, one simply needs to zip up the ribbon. Our method operates in the same fashion, but it can be used to approximate a wide variety of shapes. Given a 3D model, our algorithm produces plans for a single 2D shape that can be laser cut in few parts from fabric or paper. A zipper can then be attached along the boundary by sewing, or by gluing using a custom-built fastening rig. We show physical and virtual results that demonstrate the capabilities of our method and the ease with which shapes can be assembled.
Rendering specular microgeometry with wave optics
Authors: Yan Ling-Qi , Hašan Miloš , Walter Bruce , Marschner Steve , Ramamoorthi Ravi
Published: July 2018

Simulation of light reflection from specular surfaces is a core problem of computer graphics. Existing solutions either make the approximation of providing only a large-area average solution in terms of a fixed BRDF (ignoring spatial detail), or are specialized for specific microgeometry (e.g. 1D scratches), or are based only on geometric optics (which is an approximation to more accurate wave optics). We design the first rendering algorithm based on a wave optics model that is also able to compute spatially-varying specular highlights with high-resolution detail on general surface microgeometry. We compute a wave optics reflection integral over the coherence area; our solution is based on approximating the phase-delay grating representation of a micron-resolution surface heightfield using Gabor kernels. We found that the appearance difference between the geometric and wave solution is more dramatic when spatial detail is taken into account. The visualizations of the corresponding BRDF lobes differ significantly. Moreover, the wave optics solution varies as a function of wavelength, predicting noticeable color effects in the highlights. Our results show both single-wavelength and spectral solution to reflection from common everyday objects, such as brushed, scratched and bumpy metals.
Quadrangulation through morse-parameterization hybridization
Authors: Fang Xianzhong , Bao Hujun , Tong Yiying , Desbrun Mathieu , Huang Jin
Published: July 2018

We introduce an approach to quadrilateral meshing of arbitrary triangulated surfaces that combines the theoretical guarantees of Morse-based approaches with the practical advantages of parameterization methods. We first construct, through an eigensolver followed by a few Gauss-Newton iterations, a periodic four-dimensional vector field that aligns with a user-provided frame field and/or a set of features over the input mesh. A field-aligned parameterization is then greedily computed along a spanning tree based on the Dirichlet energy of the optimal periodic vector field, from which quad elements are efficiently extracted over most of the surface. The few regions not yet covered by elements are then upsampled and the first component of the periodic vector field is used as a Morse function to extract the remaining quadrangles. This hybrid parameterization- and Morse-based quad meshing method is not only fast (the parameterization is greedily constructed, and the Morse function only needs to be upsampled in the few uncovered patches), but is guaranteed to provide a feature-aligned quad mesh with non-degenerate cells that closely matches the input frame field over an arbitrary surface. We show that our approach is much faster than Morse-based techniques since it does not require a densely tessellated input mesh, and is significantly more robust than parameterization-based techniques on models with complex features.
Stitch meshing
Authors: Wu Kui , Gao Xifeng , Ferguson Zachary , Panozzo Daniele , Yuksel Cem
Published: July 2018

We introduce the first fully automatic pipeline to convert arbitrary 3D shapes into knit models. Our pipeline is based on a global parametrization remeshing pipeline to produce an isotropic quad-dominant mesh aligned with a 2-RoSy field. The knitting directions over the surface are determined using a set of custom topological operations and a two-step global optimization that minimizes the number of irregularities. The resulting mesh is converted into a valid stitch mesh that represents the knit model. The yarn curves are generated from the stitch mesh and the final yarn geometry is computed using a yarn-level relaxation process. Thus, we produce topologically valid models that can be used with a yarn-level simulation. We validate our algorithm by automatically generating knit models from complex 3D shapes and processing over a hundred models with various shapes without any user input or parameter tuning. We also demonstrate applications of our approach for custom knit model generation using fabrication via 3D printing.
Deep convolutional priors for indoor scene synthesis
Authors: Wang Kai , Savva Manolis , Chang Angel X. , Ritchie Daniel
Published: July 2018

We present a convolutional neural network based approach for indoor scene synthesis. By representing 3D scenes with a semantically-enriched image-based representation based on orthographic top-down views, we learn convolutional object placement priors from the entire context of a room. Our approach iteratively generates rooms from scratch, given only the room architecture as input. Through a series of perceptual studies we compare the plausibility of scenes generated using our method against baselines for object selection and object arrangement, as well as scenes modeled by people. We find that our method generates scenes that are preferred over the baselines, and in some cases are equally preferred to human-created scenes.
Optimal cone singularities for conformal flattening
Authors: Soliman Yousuf , Slepčev Dejan , Crane Keenan
Published: July 2018

Angle-preserving or
conformal
surface parameterization has proven to be a powerful tool across applications ranging from geometry processing, to digital manufacturing, to machine learning, yet conformal maps can still suffer from severe area distortion.
Cone singularities
provide a way to mitigate this distortion, but finding the best configuration of cones is notoriously difficult. This paper develops a strategy that is globally optimal in the sense that it minimizes total area distortion among all possible cone configurations (number, placement, and size) that have no more than a fixed total cone angle. A key insight is that, for the purpose of optimization, one should not work directly with curvature measures (which naturally represent cone configurations), but can instead apply
Fenchel-Rockafellar duality
to obtain a formulation involving only ordinary functions. The result is a convex optimization problem, which can be solved via a sequence of sparse linear systems easily built from the usual cotangent Laplacian. The method supports user-defined notions of importance, constraints on cone angles (
e.g.
, positive, or within a given range), and sophisticated boundary conditions (
e.g.
, convex, or polygonal). We compare our approach to previous techniques on a variety of challenging models, often achieving dramatically lower distortion, and demonstrating that global optimality leads to extreme robustness in the presence of noise or poor discretization.
Computing a high-dimensional euclidean embedding from an arbitrary smooth riemannian metric
Authors: Zhong Zichun , Wang Wenping , Lévy Bruno , Hua Jing , Guo Xiaohu
Published: July 2018

This article presents a new method to compute a

self-intersection free high-dimensional Euclidean embedding (SIFHDE
2
)

for surfaces and volumes equipped with an arbitrary Riemannian metric. It is already known that given a high-dimensional (high-d) embedding, one can easily compute an anisotropic Voronoi diagram by back-mapping it to 3D space. We show here how to solve the inverse problem, i.e., given an input metric, compute a smooth intersection-free high-d embedding of the input such that the pullback metric of the embedding matches the input metric. Our numerical solution mechanism matches the deformation gradient of the 3D → higher-d mapping with the given Riemannian metric. We demonstrate the applicability of our method, by using it to construct anisotropic Restricted Voronoi Diagram (RVD) and anisotropic meshing, that are otherwise extremely difficult to compute. In SIFHDE
2
-space constructed by our algorithm, difficult 3D anisotropic computations are replaced with simple Euclidean computations, resulting in an isotropic RVD and its dual mesh on this high-d embedding. Results are compared with the state-of-the-art in anisotropic surface and volume meshings using several examples and evaluation metrics.
Computational design of nanostructural color for additive manufacturing
Authors: Auzinger Thomas , Heidrich Wolfgang , Bickel Bernd
Published: July 2018

Additive manufacturing has recently seen drastic improvements in resolution, making it now possible to fabricate features at scales of hundreds or even dozens of nanometers, which previously required very expensive lithographic methods. As a result, additive manufacturing now seems poised for optical applications, including those relevant to computer graphics, such as material design, as well as display and imaging applications.

In this work, we explore the use of additive manufacturing for generating structural colors, where the structures are designed using a fabrication-aware optimization process. This requires a combination of full-wave simulation, a feasible parameterization of the design space, and a tailored optimization procedure. Many of these components should be re-usable for the design of other optical structures at this scale.

We show initial results of material samples fabricated based on our designs. While these suffer from the prototype character of state-of-the-art fabrication hardware, we believe they clearly demonstrate the potential of additive nanofabrication for structural colors and other graphics applications.
Instant 3D photography
Authors: Hedman Peter , Kopf Johannes
Published: July 2018

We present an algorithm for constructing 3D panoramas from a sequence of aligned color-and-depth image pairs. Such sequences can be conveniently captured using dual lens cell phone cameras that reconstruct depth maps from synchronized stereo image capture. Due to the small baseline and resulting triangulation error the depth maps are considerably degraded and contain low-frequency error, which prevents alignment using simple global transformations. We propose a novel optimization that jointly estimates the camera poses as well as spatially-varying adjustment maps that are applied to deform the depth maps and bring them into good alignment. When fusing the aligned images into a seamless mosaic we utilize a carefully designed data term and the high quality of our depth alignment to achieve two orders of magnitude speedup w.r.t. previous solutions that rely on discrete optimization by removing the need for label smoothness optimization. Our algorithm processes about one input image per second, resulting in an end-to-end runtime of about one minute for mid-sized panoramas. The final 3D panoramas are highly detailed and can be viewed with binocular and head motion parallax in VR.
Interactive exploration of design trade-offs
Authors: Schulz Adriana , Wang Harrison , Grinspun Eitan , Solomon Justin , Matusik Wojciech
Published: July 2018

Typical design for manufacturing applications requires simultaneous optimization of conflicting performance objectives: Design variations that improve one performance metric may decrease another performance metric. In these scenarios, there is no unique optimal design but rather a set of designs that are optimal for different trade-offs (called Pareto-optimal). In this work, we propose a novel approach to discover the Pareto front, allowing designers to navigate the landscape of compromises efficiently. Our approach is based on a first-order approximation of the Pareto front, which allows entire neighborhoods rather than individual points on the Pareto front to be captured. In addition to allowing for efficient discovery of the Pareto front and the corresponding mapping to the design space, this approach allows us to represent the entire trade-off manifold as a small collection of patches that comprise a high-quality and piecewise-smooth approximation. We illustrate how this technique can be used for navigating performance trade-offs in computer-aided design (CAD) models.
A multi-scale model for simulating liquid-fabric interactions
Authors: Fei Yun (Raymond) , Batty Christopher , Grinspun Eitan , Zheng Changxi
Published: July 2018

We propose a method for simulating the complex dynamics of partially and fully saturated woven and knit fabrics interacting with liquid, including the effects of buoyancy, nonlinear drag, pore (capillary) pressure, dripping, and convection-diffusion. Our model evolves the velocity fields of both the liquid and solid relying on mixture theory, as well as tracking a scalar saturation variable that affects the pore pressure forces in the fluid. We consider the porous microstructure implied by the fibers composing individual threads, and use it to derive homogenized drag and pore pressure models that faithfully reflect the anisotropy of fabrics. In addition to the bulk liquid and fabric motion, we derive a quasi-static flow model that accounts for liquid spreading within the fabric itself. Our implementation significantly extends standard numerical cloth and fluid models to support the diverse behaviors of wet fabric, and includes a numerical method tailored to cope with the challenging nonlinearities of the problem. We explore a range of fabric-water interactions to validate our model, including challenging animation scenarios involving splashing, wringing, and collisions with obstacles, along with qualitative comparisons against simple physical experiments.
Online optical marker-based hand tracking with deep labels
Authors: Han Shangchen , Liu Beibei , Wang Robert , Ye Yuting , Twigg Christopher D. , Kin Kenrick
Published: July 2018

Optical marker-based motion capture is the dominant way for obtaining high-fidelity human body animation for special effects, movies, and video games. However, motion capture has seen limited application to the human hand due to the difficulty of automatically identifying (or labeling) identical markers on self-similar fingers. We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks. We demonstrate robustness of our labeling solution to occlusion, ghost markers, hand shape, and even motions involving two hands or handheld objects. Our technique is equally applicable to sparse or dense marker sets and can run in real-time to support interaction prototyping with high-fidelity hand tracking and hand presence in virtual reality.
Deep appearance models for face rendering
Authors: Lombardi Stephen , Saragih Jason , Simon Tomas , Sheikh Yaser
Published: July 2018

We introduce a deep appearance model for rendering the human face. Inspired by Active Appearance Models, we develop a data-driven rendering pipeline that learns a joint representation of facial geometry and appearance from a multiview capture setup. Vertex positions and view-specific textures are modeled using a deep variational autoencoder that captures complex nonlinear effects while producing a smooth and compact latent representation. View-specific texture enables the modeling of view-dependent effects such as specularity. In addition, it can also correct for imperfect geometry stemming from biased or low resolution estimates. This is a significant departure from the traditional graphics pipeline, which requires highly accurate geometry as well as all elements of the shading model to achieve realism through physically-inspired light transport. Acquiring such a high level of accuracy is difficult in practice, especially for complex and intricate parts of the face, such as eyelashes and the oral cavity. These are handled naturally by our approach, which does not rely on precise estimates of geometry. Instead, the shading model accommodates deficiencies in geometry though the flexibility afforded by the neural network employed. At inference time, we condition the decoding network on the viewpoint of the camera in order to generate the appropriate texture for rendering. The resulting system can be implemented simply using existing rendering engines through dynamic textures with flat lighting. This representation, together with a novel unsupervised technique for mapping images to facial states, results in a system that is naturally suited to real-time interactive settings such as Virtual Reality (VR).
Magnetization dynamics for magnetic object interactions
Authors: Kim Seung-Wook , Park Sun Young , Han Junghyun
Published: July 2018

The goal of this paper is to simulate the interactions between magnetic objects in a physically correct way. The simulation scheme is based on magnetization dynamics, which describes the temporal change of magnetic moments. For magnetization dynamics, the Landau-Lifshitz-Gilbert equation is adopted, which is widely used in micromagnetics. Through effectively-designed novel models of magnets, it is extended into the macro scale so as to be combined with real-time rigid-body dynamics. The overall simulation is stable and enables us to implement mutual induction and remanence that have not been tackled by the state-of-the-art technique in magnet simulation. The proposed method can be applied to various fields including magnet experiments in the virtual world.
Scalable Metadata Management Techniques for Ultra-Large Distributed Storage Systems -- A Systematic Review
Authors: Singh Harcharan Jit , Bawa Seema
Published: July 2018

The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.
A Survey on Automatic Detection of Hate Speech in Text
Authors: Fortuna Paula , Nunes Sérgio
Published: July 2018

The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.
The new robber barons
Authors: Walsh Toby
Published: July 2018

You hear a lot today about Artificial Intelligence and Ethics. For good reason. As algorithms get smarter, they are increasingly making decisions that impact on people's lives. And so we need to be careful that these algorithms are fair and transparent. But it's not only the algorithms that concern me.
The simple essence of automatic differentiation
Authors: Elliott Conal
Published: July 2018

Automatic differentiation (AD) in reverse mode (RAD) is a central component of deep learning and other uses of large-scale optimization. Commonly used RAD algorithms such as backpropagation, however, are complex and stateful, hindering deep understanding, improvement, and parallel execution. This paper develops a simple, generalized AD algorithm calculated from a simple, natural specification. The general algorithm is then specialized by varying the representation of derivatives. In particular, applying well-known constructions to a naive representation yields two RAD algorithms that are far simpler than previously known. In contrast to commonly used RAD implementations, the algorithms defined here involve no graphs, tapes, variables, partial derivatives, or mutation. They are inherently parallel-friendly, correct by construction, and usable directly from an existing programming language with no need for new data types or programming style, thanks to use of an AD-agnostic compiler plugin.
A spectrum of type soundness and performance
Authors: Greenman Ben , Felleisen Matthias
Published: July 2018

The literature on gradual typing presents three fundamentally different ways of thinking about the integrity of programs that combine statically typed and dynamically typed code. This paper presents a uniform semantic framework that explains all three approaches, illustrates how each approach affects a developer's work, and adds a systematic performance comparison for a single implementation platform.