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The LivePaper system augments ordinary pieces of paper with projected information. Pages, cards and books are placed on an instrumented tabletop to activate their enhancement. To the user, it appears as if the paper gains new visual and auditory features. Projected annotations track the orientation and location of pages as the user moves them on the desktop. A piece of paper that is removed, but then returned to the desk, regains the same features that it previously exhibited. The LivePaper system accomplishes this by using features extracted from written material on the page, not from glyphs or other artificial marks. The paper describes both the system as a whole, and a number of sample applications we have developed to illustrate the feasibility of the LivePaper system. These applications include an architectural visualization tool, which projects a 3D hidden-line rendering of walls onto a page. The user may rotate and move the page to view the rendering from different angles. Another application is an audio player, which begins playing when a page (such as a business card) is laid on the desk. The user may control playback with his or her finger via projected buttons. Other applications include page-sharing, remote collaboration, and World Wide Web page viewing. From the user's perspective, all of these applications are attributes of the particular page, not features of the tabletop. Particular attention is given to the design of interaction: LivePaper is object-oriented, because the individual sheets are treated as computational units, but it also provides functions that involve several objects. The design principles applied to handle the different kinds of functionality are explained and illustrated in the LivePaper system, but are also proposed for wider use in augmented reality.

A practical technique is presented for efficiently rendering oriental black ink paintings with realistic diffusion effects, being based on models which simulate a variety of paper types and black ink properties. Strokes can be generated by bristles or elastic brush models simulating those actually used in oriental black ink painting. Incorporation of paper modeling allows users to draw on a wide variety of papers having either randomly generated fiber meshes or uniform meshes commonly found in fabrics which are also frequently used. This accordingly provides a unique platform for real-time dynamic simulation of realistic ink diffusion in that the resultant diffusion images reflect the texture and the global change of gray tones as well as local temporal variations specific to a particular paper. Examples are presented demonstrating the capability of the proposed technique for handling different ink and paper properties.

This research investigated the space compositions of Chernikhov’s 101 Architectural Fantasies via computer-aided simulation to interpret the relationships between architectural components and spatial organization. An algorithmic approach and a perception approach were tested. Traditional analysis emphasized the simulation of corresponding objects by perspective deconstruction methods, which might not be able to show the exactly correct spatial relationship between objects. This research adopted photogrammetry to investigate the non-orthogonal spatial construction of 3D objects in 2D pictures. Research results showed that the algorithmic approach may derive different degrees of angles of parallel or intersected objects, and that observers tend to be misled by the effect of “orthogonal assumption” in terms of their own visual experiences. This finding revealed that Chernikhov had created unreasonable descriptions of space. This result was verified by the existence of false parallel and orthogonal relationships between drawn building parts. Three tests were conducted. Observers used a reverse verification process to analyze three-dimensional objects re-built in simulation. The verification mirrored a two-way construction relationship between 2D perspective and 3D models.

Maximally stable component detection is a very popular method for feature analysis in images, mainly due to its low computation cost and high repeatability. With the recent advance of feature-based methods in geometric shape analysis, there is significant interest in finding analogous approaches in the 3D world. In this paper, we formulate a diffusion-geometric framework for stable component detection in non-rigid 3D shapes, which can be used for geometric feature detection and description. A quantitative evaluation of our method on the SHREC'10 feature detection benchmark shows its potential as a source of high-quality features.

This paper proposes a novel technique for converting a given animated mesh into a series of displaced subdivision surfaces. Instead of independently converting each mesh frame in the animated mesh, our technique produces displaced subdivision surfaces that share the same topology of the control mesh and a single displacement map. We first propose a conversion framework that enables sharing the same control mesh topology and a single displacement map among frames, and then present the details of the components in the framework. Each component is specifically designed to minimize the shape conversion errors that can be caused by enforcing a single displacement map. The resulting displaced subdivision surfaces have a compact representation, while reproducing the details of the original animated mesh. The representation can also be used for efficient rendering on modern graphics hardware that supports accelerated rendering of subdivision surfaces.

Classical (or biaxial) twill is a textile weave in which the weft threads pass over and under two or more warp threads, with an offset between adjacent weft threads to give an appearance of diagonal lines. This paper introduces a theoretical framework for constructing twill-woven objects, i.e., cyclic twill-weavings on arbitrary surfaces, and it provides methods to convert polygonal meshes into twill-woven objects. It also develops a general technique to obtain exact triaxial-woven objects from an arbitrary polygonal mesh surface.

This paper introduces the concept of weighted topology to model a 3D object whose connectivity and metric depend on a novel notion of weighted arc-length. The weighted arc-length between any two points of the shape takes into account the fact that a part of the object may be either weakly or strongly connected to another part. The new model is useful to treat problems which are intrinsically not robust to small topological changes. We describe an example implementation of the model and show how it can be exploited to (1) extend the applicability domain of existing segmentation algorithms and (2) improve the performances of a shape descriptor in a 3D object retrieval scenario.

In this paper, we present a new algorithm for quad-dominant meshing of unorganized point clouds based on periodic global parameterization. Our meshing method is guided by principal directions so as to preserve the intrinsic geometric properties. We use local Delaunay triangulation to smooth the initial principal directions and adapt the global parameterization to point clouds. By optimizing the fairness measure we can find the two scalar functions whose gradients best align with the guided principal directions. To handle the redundant vertices in the iso-lines due to overlapped triangles, an approach is specially designed to clean the iso-lines. Our approach is fully automatic and applicable to a surface of arbitrary genus. We also show an application of our method in curve skeleton extraction from incomplete point cloud data.

The automatic generation of volumes bounding the intersection of two implicit surfaces (isosurfaces of real functions of 3D point coordinates) or feature based volumes (FBV) is presented. Such FBVs are defined by constructive operations, function normalization and offsetting. By applying various offset operations to the intersection of two surfaces, we can obtain variations in the shape of an FBV. The resulting volume can be used as a boundary for blending operations applied to two corresponding volumes, and also for visualization of feature curves and modeling of surface based structures including microstructures.

We present an approach to compute the perceived complexity of a given 3D shape using the similarity between its views. Previous studies on 3D shape complexity relied on geometric and/or topological properties of the shape and are not appropriate for incorporating results from human shape perception which claim that humans perceive 3D shapes as organizations of 2D views. Therefore, we base our approach to computing 3D shape complexity on the (dis)similarity matrix of the shape's 2D views. To illustrate the application of our approach, we note that simple shapes lead to similar views whereas complex ones result in different, dissimilar views. This reflected in the View Similarity Graph (VSG) of a shape as tight clusters of points if the shape is simple and increasingly dispersed points as it gets more complex. To get a visual intuition of the VSG, we project it to 2D using Multi-Dimensional Scaling (MDS) and introduce measures to compute shape complexity through point dispersion in the resulting MDS plot. Experiments show that results obtained using our measures alleviate some of the drawbacks present in previous approaches.

Modeling of realistic garments is essential for creating believable virtual environments. Sketch-based modeling of garments presents an appealing, easy to use alternative to the established modeling approaches which are time consuming and require significant tailoring expertise. Unfortunately, the results created using existing sketch-based methods lack realism. Driven by human perception of garment sketches, we propose a context-aware garment sketch interpretation based on a set of observations about key factors that affect the shape of garments. Based on this analysis we develop a geometric method for sketch-based modeling of garments which obtains more realistic results than previous techniques. We demonstrate the effectiveness of our method on a variety of inputs and validate our approach via a user study where viewers were asked to compare the believability of our outputs versus previous ones.

We introduce a surface reconstruction algorithm suitable for large point sets. The algorithm is an octree-based version of the Cocone reconstruction algorithm [4], allowing independent processing of small subsets of the total input point set. When the points are sufficiently sampled from a smooth surface, the global guarantee of topological correctness of the original algorithm is preserved, together with the geometric accuracy guarantees.

In recent years, there has been a growing interest in computer graphics and geometric modeling in the ability to create and manipulate seemingly impossible models (SIMs). Methods to create derivative work and modify drawings and paintings of SIMs made by artists were suggested. Similarly, 3D realization of SIMs of some of these drawings were also offered. In this work, we further explore the nature of SIMs and identify a class of SIMs that can be realized and modeled in 3D. As part of this analysis we show an invariance whose preservation allows one to model SIM artifacts that are completely realizable. We further present a mini-modeling package that allow end-users to realize whole new SIMs in two stages: modeling a regular 3D model and then converting it into a SIM using special deformations. We conclude with some examples, a discussion on the current limitations and a layout of possible future work.

A common variant of caricature relies on exaggerating characteristics of a shape that differs from a reference template, usually the distinctive traits of a human portrait. This work introduces a caricature tool that interactively emphasizes the differences between two three-dimensional meshes. They are represented in the manifold harmonic basis of the shape to be caricatured, providing intrinsic controls on the deformation and its scales. It further provides a smooth localization scheme for the deformation. This lets the user edit the caricature part by part, combining different settings and models of exaggeration, all expressed in terms of harmonic filter. This formulation also allows for interactivity, rendering the resulting 3d shape in real time.

Search and retrieval remains a major research topic in several domains, including computer graphics, computer vision, engineering design, etc. A search engine requires primarily an input search query and a database of items to search from. In engineering, which is the primary context of this paper, the database consists of 3D CAD models, such as washers, pistons, connecting rods, etc. A query from a user is typically in the form of a sketch, which attempts to capture the details of a 3D model. However, sketches have certain typical defects such as gaps, over-drawn portions (multi-strokes), etc. Since the retrieved results are only as good as the input query, sketches need cleaning-up and enhancement for better retrieval results. In this paper, a deep learning approach is proposed to improve or clean the query sketches. Initially, sketches from various categories are analysed in order to understand the many possible defects that may occur. A dataset of cleaned-up or enhanced query sketches is then created based on an understanding of these defects. Consequently, an end-to-end training of a deep neural network is carried out in order to provide a mapping between the defective and the clean sketches. This network takes the defective query sketch as the input and generates a clean or an enhanced query sketch. Qualitative and quantitative comparisons of the proposed approach with other state-of-the-art techniques show that the proposed approach is effective. The results of the search engine are reported using both the defective and enhanced query sketches, and it is shown that using the enhanced query sketches from the developed approach yields improved search results.

Folding a thin sheet material such as paper along curves creates a developable surface composed of ruled surface patches. When using such surfaces in design, designers often repeat a process of folding along curves drawn on a sheet and checking the folded shape. Although several methods for constructing such shapes on a computer have been proposed, it is still difficult to check the folded shapes instantly from the crease patterns. In this paper, we propose a simple method that approximately realizes a simulation of curved folds with a triangular mesh from its crease pattern. The proposed method first approximates curves in a crease pattern with polylines and then generates a triangular mesh. In order to construct the discretized developable surface, the edges in the mesh are rearranged so that they align with the estimated rulings. The proposed method is characterized by its simplicity and is implemented on an existing origami simulator that runs in a web browser.

In recent years, with the improvement of artificial intelligence technology, it has become possible to reconstruct high-precision 3D human body models based on ordinary RGB images. The current 3D human body reconstruction technology requires complex external equipment to scan all angles of the human body, which is complicated to be implemented and cannot be popularized. In order to solve this problem, this paper applies deep learning models on reconstructing 3D human body based on monocular images. First of all, this paper uses Stacked Hourglass network to perform convolution operations on monocular images collected from different views. Then Multi-Layer Perceptrons (MLPs) are used to decode the encoded high-level images. The feature codes in the two views(main and side) are fused, and the interior and exterior points are classified by the fusion features, so as to obtain the corresponding 3D occupancy field. At last, the Marching Cube algorithm is used for 3D reconstruction with a specific threshold and then we use Laplace smoothing algorithm to remove artifacts. This paper proposes a dense sampling strategy based on the important joint points of the human body, which has a certain optimization effect on the realization of high-precision 3D reconstruction. The performance of the proposed scheme has been validated on the open source datasets, MGN dataset and the THuman dataset, provided by Tsinghua University. The proposed scheme can reconstruct features such as clothing folds, color textures, and facial details,and has great potential to be applied in different applications.

International audience; Weathering phenomena are ubiquitous in urban environments, where it is easy to observe severely degraded old buildings as a result of water penetration. Despite being an important part of any realistic city, this kind of phenomenon has received little attention from the Computer Graphics community compared to stains resulting from biological or flow effects on the building exteriors. In this paper, we present physically-inspired deep weathering effects, where the penetration of humidity (i.e., water particles) and its interaction with a building's internal structural elements result in large, visible degradation effects. Our implementation is based on a particle-based propagation model for humidity propagation, coupled with a spring-based interaction simulation that allows chemical interactions, like the formation of rust, to deform and destroy a building's inner structure. To illustrate our methodology, we show a collection of deep degradation effects applied to urban models involving the creation of rust or of ice within walls.

International audience; Despite the wide availability of large motion databases, and recent advances in exploiting them to create tailored contents, artists still spend tremendous efforts in editing and adapting existing character animations to new contexts. This motion editing problem is a well-addressed research topic. Yet only a few approaches have considered the influence of the camera angle, and the resulting visual features it yields, as a mean to control and warp a character animation. This paper proposes the design of viewpoint-dependent motion warping units that perform subtle updates on animations through the specification of visual motion features such as visibility, or spatial extent. We express the problem as a specific case of visual servoing, where the warping of a given character motion is regulated by a number of visual features to enforce. Results demonstrate the relevance of the approach for different motion editing tasks and its potential to empower virtual characters with attention-aware communication abilities.

Robotic-Assisted Surgery (RAS) is beginning to unlock its potential. However, despite the latest advances in RAS, the steep learning curve of RAS devices remains a problem. A common teaching resource in surgery is the use of videos of previous procedures, which in RAS are almost always stereoscopic. It is important to be able to add virtual annotations onto these videos so that certain elements of the surgical process are tracked and highlighted during the teaching session. Including virtual annotations in stereoscopic videos turns them into Mixed Reality (MR) experiences, in which tissues, tools and procedures are better observed. However, an MR-based annotation of objects requires tracking and some kind of depth estimation. For this reason, this paper proposes a real-time hybrid tracking–matching method for performing virtual annotations on RAS videos. The proposed method is hybrid because it combines tracking and stereo matching, avoiding the need to calculate the real depth of the pixels. The method was tested with six different state-of-the-art trackers and assessed with videos of a sigmoidectomy of a sigma neoplasia, performed with a Da Vinci® X surgical system. Objective assessment metrics are proposed, presented and calculated for the different solutions. The results show that the method can successfully annotate RAS videos in real-time. Of all the trackers tested for the presented method, the CSRT (Channel and Spatial Reliability Tracking) tracker seems to be the most reliable and robust in terms of tracking capabilities. In addition, in the absence of an absolute ground truth, an assessment with a domain expert using a novel continuous-rating method with an Oculus Quest 2 Virtual Reality device was performed, showing that the depth perception of the virtual annotations is good, despite the fact that no absolute depth values are calculated.

Gears play a pivotal role in machine design. This paper proposes an algorithm to simplify the shapes of planar gears. This is achieved via iterative conjugation, using precise algebraic sweeps. The notion of shape simplification is introduced in a mathematically rigorous manner and it is shown that the conjugation process converges, yielding a pair of meshing gears that follow the desired motion. Simplified gear shapes may lead to improved mechanical characteristics and reduction in manufacturing costs. The generality of algebraic sweeps allows precise design of gears with freeform shapes and non-uniform motion transmission. Moreover, the computational framework proposed in this paper is versatile, with applications beyond gear design. A variety of examples from an implementation of our algorithm, that offers topological guarantees, are presented, which demonstrate the robustness and efficacy of our approach.

In this paper, we present a novel adaptive particle resampling method tailored for surface reconstruction of level-sets defined by the boundary particles from a particle-based liquid simulation. The proposed approach is simple and easy to implement, and only requires the positions of the particles to identify and refine regions with small and thin fluid features accurately. The method comprises four main stages: boundary detection, feature classification, particle refinement, and surface reconstruction. For each simulation frame, firstly the free-surface particles are captured through a boundary detection method. Then, the boundary particles are classified and labeled according to the deformation and the stretching of the free-surface computed from the Principal Component Analysis (PCA) of the particle positions. The particles placed at feature regions are then refined according to their feature classification. Finally, we extract the free-surface of the zero level-set defined by the resampled boundary particles and its normals. In order to render the free-surface, we demonstrate how the traditional methods of surface fitting in Computer Graphics and Computational Physics literature can benefit from the proposed resampling method. Furthermore, the results shown in the paper attest the effectiveness and robustness of our method when compared to state-of-the-art adaptive particle resampling techniques.

The ability to edit an image using intuitive commands and primitives is a desired feature for any image editing software. In this paper, we combine recent results in medial axes with the well-established morphological tree representations to develop an interactive image editing tool that provides global and local image manipulation using high-level primitives. We propose a new way to render interactive morphological trees using icicle plots and introduce different ways of manipulating spline-based medial axis transforms for grayscale and colored image editing. Different applications of the tool, such as watermark removal, image deformation, dataset augmentation for machine learning, artistic illumination manipulation, image rearrangement, and clothing design, are described and showcased on examples.

Solving linear system of equations stemming from Laplacian operators is at the heart of a wide range of applications. Due to the sparsity of the linear systems, iterative solvers such as Conjugate Gradient and Multigrid are usually employed when the solution has a large number of degrees of freedom. These iterative solvers can be seen as sparse approximations of the Green's function for the Laplacian operator. In this paper we propose a machine learning approach that regresses a Green's function from boundary conditions. This is enabled by a Green's function that can be effectively represented in a multi-scale fashion, drastically reducing the cost associated with a dense matrix representation. Additionally, since the Green's function is solely dependent on boundary conditions, training the proposed neural network does not require sampling the right-hand side of the linear system. We show results that our method outperforms state of the art Conjugate Gradient and Multigrid methods., Computers & Graphics, 107, ISSN:0097-8493, ISSN:1873-7684

This paper explores narrative techniques combined with medical visualizations to tell data-driven stories about diseases for a general audience. The field of medical illustration uses narrative visualization through hand-crafted techniques to promote health literacy. However, data-driven narrative visualization has rarely been applied to medical data. We derived a template for creating stories about diseases and applied it to three selected diseases to demonstrate how narrative techniques could support visual communication and facilitate understanding of medical data. One of our main considerations is how interactive 3D anatomical models can be integrated into the story and whether this leads to compelling stories in which the users feel involved. A between-subject study with 90 participants suggests that the combination of a carefully designed narrative structure, the constant involvement of a specific patient, high-qualitative visualizations combined with easy-to-use interactions, are critical for an understandable story about diseases that would be remembered by participants.

This special issue contains extended and revised versions of the best papers presented at the 7th Conference on Smart Tools and Applications in Graphics (STAG 2020), held virtually on November 12–13, 2020. The two selected papers cover two different visual computing domains. The first contribution proposes a novel Level-of-Detail data structure (Motion Tree) to efficiently render motion blur of animated objects, while the second contribution presents an evaluation of current deep-learning based 3D registration solutions.

Meshes obtained by 3D scanning or photogrammetry are usually very dense and multi-chart textured. Effectively reducing the amount of data while preserving both geometry and appearance is an important and also challenging problem. This paper presents a new method for seamless simplification of dense triangular meshes with multi-chart textures. The method is composed of three components: geometry simplification, correspondence map construction and texture image generation. The core of the method lies in the ideas of decoupling geometry, UV-parameterization and texture processes to make the simplification process less constrained by the underlying attribute encoding, a reliable updating procedure for constructing the correspondence map between the simplified and original meshes, and a least squares model for filling texture elements, which are the key to producing artifact-free visual appearance. Compared to the state-of-the-art, the proposed method has several advantages: (1) it enables aggressive simplification with well preserved geometry and appearance; (2) it automatically achieves seamless simplification without special treatment on the seams between multi-charts of the texture; and (3) it is reliable. Experimental results have demonstrated these features. Nanyang Technological University National Research Foundation (NRF) This work was supported by a joint WASP/NTU project (04INS000440C130), Ynnerman Scholar (KAW 2019.0024) and National Research Foundation (NRF) Singapore, under its Virtual Singapore (VSG) Programme (Award No. NRF2015VSG-AA3DCM001-018).

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools.

High-end Terrestrial Lidar Scanners are often equipped with RGB cameras that are used to colorize the point samples. Some of these scanners produce panoramic HDR images by encompassing the information of multiple pictures with different exposures. Unfortunately, exported RGB color values are not in an absolute color space, and thus point samples with similar reflectivity values might exhibit strong color differences depending on the scan the sample comes from. These color differences produce severe visual artifacts if, as usual, multiple point clouds colorized independently are combined into a single point cloud. In this paper we propose an automatic algorithm to minimize color differences among a collection of registered scans. The basic idea is to find correspondences between pairs of scans, i.e. surface patches that have been captured by both scans. If the patches meet certain requirements, their colors should match in both scans. We build a graph from such pair-wise correspondences, and solve for the gain compensation factors that better uniformize color across scans. The resulting panoramas can be used to colorize the point clouds consistently. We discuss the characterization of good candidate matches, and how to find such correspondences directly on the panorama images instead of in 3D space. We have tested this approach to uniformize color across scans acquired with a Leica RTC360 scanner, with very good results. This work has been partially supported by the project TIN2017-88515-C2-1-R funded by MCIN/AEI/10.13039/5011000- 11033/FEDER ‘‘A way to make Europe’’, by the EU Horizon 2020, JPICH Conservation, Protection and Use initiative (JPICH-0127) and the Spanish Agencia Estatal de Invesigación (grant PCI2020- 111979), by the Universidad Rey Juan Carlos through the Distinguished Researcher position INVESDIST-04 under the call from 17/12/2020, and a Maria Zambrano research fellowship at Universitat Politècnica de Catalunya funded by Ministerio de Universidades.

One of the most essential interactions in Virtual Reality (VR) is the user’s ability to move around and explore the virtual environment. The design of the navigation technique plays a crucial role in the user experience since it determines key usability aspects. VR devices allow for an immersive exploration of 3D worlds, but navigation in VR is challenging for many users, due to potential usability issues related to specific VR controllers, user skills, and motion sickness. Although hundreds of interaction techniques have been proposed for this task, VR navigation still poses a high entry barrier for many users. In this paper we argue that adapting the navigation technique to its context of use can lead to substantial improvements in navigation usability and accessibility. The context of use includes the type of scene, the available physical space, as well as the profile of the user. We present a test platform to facilitate the design and fine-tuning of interaction techniques for 3D navigation. We focus on mainstream VR devices (headsets and controllers) and support the most common navigation metaphors (walking, flying, teleportation). The key idea is to let developers specify, at runtime, the exact mapping between user actions and locomotion changes, for any of the supported metaphors. Such mappings are described by a collection of parameters (e.g. maximum speed) whose values can be adjusted interactively through a GUI, or be provided by user-defined code which can be edited at runtime. Feedback obtained from developers suggests that this approach can be used to quickly adapt the navigation techniques to various people including persons with no previous 3D navigation skills, elderly people, and people with disabilities, as well as to the type, size and semantics of the virtual environment. This work has been funded by MCIN/AEI/10.13039/501100011033/FEDER ‘‘A way to make Europe’’. Pedret model partially funded by EU Horizon 2020, JPICH Conservation, Protection and Use initiative (JPICH-0127) and the Spanish Agencia Estatal de Investigación, grant PCI2020-111979 Enhancement of Heritage Experiences: the Middle Ages; Digital Layered Models of Architecture and Mural Paintings over Time (EHEM)

The computational fabrication community is developing an increasing interest in the use of patterned surfaces, which can be designed to show ornamental and unconventional aesthetics or to perform as a proper structural material with a wide range of features. Geometrically designing and controlling the deformation capabilities of these patterns in response to external stimuli is a complex task due to the large number of variables involved. This paper introduces a method for generating sets of tileable and exchangeable flat patterns as well as a model-reduction strategy that enables their mechanical simulation at interactive rates. This method is included in a design pipeline that aims to turn any general flat surface into a pattern tessellation, which is able to deform under a given loading scenario. To validate our approach, we apply it to different contexts, including real-scale 3D printed specimens, for which we compare our results with the ones provided by a ground-truth solver.

Multimodal registration is a challenging problem in visual computing, commonly faced during medical image-guided interventions, data fusion and 3D object retrieval. The main challenge of multimodal registration is finding accurate correspondence between modalities, since different modalities do not exhibit the same characteristics. This paper explores how the coherence of different modalities can be utilized for the challenging task of 3D multimodal registration. A novel deep learning multimodal registration framework is proposed by introducing a siamese deep learning architecture, especially designed for aligning and fusing modalities of different structural and physical principles. The cross-modal attention blocks lead the network to establish correspondences between features of different modalities. The proposed framework focuses on the alignment of 3D point clouds and the micro-CT 3D volumes of the same object. A multimodal dataset consisting of real micro-CT scans and their synthetically generated 3D models (point clouds) is presented and utilized for evaluating our methodology.

Although 3D Voronoi diagrams and medial axis transforms have numerous applications in biology, robotics, and manufacturing, most researchers use Voronoi diagrams of points instead of the true 3D input geometry, due to issues of robustness and scalability. In this paper, we present a robust sample-based GPU algorithm for calculating the full topology of Voronoi diagrams of non-general position spheres. Prior work demonstrated that the presence, geometry, and combinatorial basis of spheres that contribute to Voronoi vertices can be efficiently computed by shooting rays from each input sphere, mapping ray intersections with the nearest bisector surface to parametric bounding cubes, and analyzing the results. In this paper, we propose an algorithm on this parametric bounding cube to compute Voronoi edges in addition to the vertices. We successfully extract the full topology of the Voronoi diagram, including special cases such as isolated Voronoi edges that do not contain Voronoi vertices, more than three Voronoi edges emanating from a Voronoi vertex, and Voronoi edges that are shared by more than three Voronoi cells. Our GPU implementation efficiently and robustly handles all input, whether in general or non-general position, and finds all Voronoi vertices and edges, modulo the sampling density, including isolated disconnected edges.

User studies are indispensable for visualization application papers in order to assess the value and limitations of the presented approach. Important aspects are how well depth and shape information can be perceived, as coding of these aspects is essential to enable an understandable representation of complex 3D data. In practice, there is usually little time to perform such studies, and the establishment and conduction of user studies can be labour-intensive. In addition, it can be difficult to reach enough participants to obtain expressive results regarding the quality of different visualization techniques. In this paper, we propose a framework that allows visualization researchers to quickly create task-based user studies on depth and shape perception for different surface visualizations and perform the resulting tasks via a web interface. With our approach, the effort for generating user studies is reduced and at the same time the web-based component allows researchers to attract more participants to their study. We demonstrate our framework by applying shape and depth evaluation tasks to visualizations of various surface representations used in many technical and biomedical applications.

This paper proposes a hybrid Lagrangian–Eulerian method involving all participating media for high-speed fluid simulation and its accompanying phenomena. We take advantages of both particle- and grid-based solvers in our new approach by dividing all the physical states into two different parts based on their individual characteristics. Lagrangian-based solver gives rise to liquid, droplet, and foam creation and facilitates the drastic shape distortion and state transition during the multi-physical processes simulation from the perspective of individual particles. At the same time, Eulerian-based solver mainly focuses on the animation of spray, which enables a fog-like atmosphere. Essential to all of the aforementioned functionalities is the state transition among different media considering both physical and geometric features in this paper. The state transition enables to streamline the dis-association and generation of certain physical states. Moreover, to ensure the synchronization of hybrid models, we establish essential interaction and build tight coupling between the particle- and grid-based subsystems. With the comprehensive experimental results, we have manifested that our new modeling approach is both effective and highly modularized, and is capable of producing convincing visual effects for a wider range of graphics and animation tasks.

In this paper, we review Augmented Reality Training systems and how they can be combined with Intelligent Tutoring Systems to improve pedagogical outcomes on spatial learning tasks. Intelligent Tutoring Systems (ITSs) are computer-based systems that provide personalised feedback, learner support and task selection by modelling learner knowledge. They have been shown to significantly improve learning, however currently provide little support for psychomotor tasks that involve spatial learning. For such tasks, ITSs may not be able to present instruction in an intuitive manner for mental model reconstruction. Augmented Reality (AR) overlays 3-Dimensional virtual objects on the real world, and has been shown to be useful for training in physical manipulation tasks. However, most current AR training systems provide only step by step instructions and do not adapt training content to user needs. In this paper we show how combining ITS with AR systems has the potential to overcome the limitations of both approaches and produce a significantly improved learning experience. We provide examples of previous work in this area, and provide guidelines for future AR ITS systems. Refereed/Peer-reviewed

This paper describes a group of auxetic-inspired material structures that can transform into various shapes upon compression. We developed four cellular-based material structure units composed of rigid plates and elastic/rotary hinges. Different compositions of these units lead to a variety of tunable shape-changing possibilities, such as uniform scaling, shearing, bending and rotating. By tessellating those transformations together, we can create various higher level transformations for product design. In the paper, we first give a geometrical and numerical description of the units’ configuration and their degrees of freedom. An interactive simulation tool is developed for users to input designed structures and preview the transformation. Finally we present three application prototypes that utilize our proposed structures.

International audience; To capture the real-world luminance values, High Dynamic Range (HDR) image processing has been developed. HDR images have a richer content than the widely-used Standard Dynamic Range (SDR) images, and are used in a number of situations, e.g. in film industry. As HDR displays are more and more commercially available, we need to be able to process HDR images as well as SDR ones (for example, devising denoising algorithms, inpainting or anti-aliasing). The most powerful methods to process images are now deep neural networks. However, the training of such networks calls for a lot of images, and HDR images datasets are relatively small.One way to generate HDR images is inverse tone mapping operators (iTMOs). They are algorithms that expand the dynamic range of SDR images. In this paper, we propose HDR-LFNet, a novel iTMO, and its HDR training dataset. Our method merges several existing handcrafted iTMOs, combined in a supervised neural network to produce an HDR output. Our lightweight network requires less training images than state-of-the-art methods, and has faster training phase. Besides, the quality of the generated images is similar to the state-of-the-art. We present the architecture as well as the subjective and experimental evaluations of our method.

The comparison of meteorological data is a fundamental task within the techniques of meteorological forecasting due to the cyclical nature of the climate. However, interpretation of meteorological datasets can be difficult due to their large size. The goal of information visualization is to support a better understanding of the data what includes assisting the user in the data comparison process. However, few visualization techniques have been specifically developed to support the comparison process of big meteorological data. In this paper, we improved a visualization technique for large time-series comparison. The new technique is more suitable for the comparison of meteorological data.

Real-time global illumination is an extremely challenging problem because of its intrinsic complexity due to the interplay between complex geometry, multiple light bounces, and stringent real-time frame-rate requirements. In this paper, we present a new technique that enables the real-time computation of global illumination in generic scenes for diffuse surfaces and static geometry. Our technique combines a voxel-based representation of the scene, a voxel-clustering algorithm, and an iterative light-propagation algorithm based on the resulting clusters. Although our implementation is currently designed to handle the first (and main) bounce, the technique allows for multiple light bounces. Summing up all these components results in a flexible algorithm capable of providing global illumination effects and on-the-fly illumination computations This work was partially funded by the TIN2017-88515-C2-2-R from Ministerio de Ciencia, Innovación y Universidades, Spain Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier

Monte Carlo integration is an established method in simulating light transport. However, due to its stochastic nature, it requires copious amounts of samples to eliminate the estimation error, and variance reduction techniques such as importance sampling are used to improve the convergence speed as a remedy. Path guiding is a class of adaptive importance sampling methods devised specifically for light transport simulation, which demonstrates significant improvements over classical sampling techniques. Yet most of the previous path guiding methods only account for the radiance term, omitting the bidirectional scattering distribution function (BSDF) term. This results in suboptimal sample quality for non-diffuse light transport. This paper presents a path guiding method which guides paths according to the full product of the BSDF and radiance terms in light transport equation. Extending a spatio-directional tree based path guiding method, we use a semi-adaptive grid-quadtree hybrid subdividing the directional domain in a spatial subdomain to learn and represent the radiance field. With the help of BSDF lookup tables used to accelerate BSDF evaluations, this grid-quadtree allows us to efficiently construct approximate product distributions and guide paths according to the product of incident radiance and cosine-weighted BSDF at each path vertex. The resulting method is relatively simple to implement and enables more robust sampling on scenes with many glossy surfaces.

Object skeleton is a useful geometric tool for shape analysis tasks. It encodes the topological structure of the primitive shape and preserves a geometric cue as well. Skeletonization is a seemingly simple but difficult research problem. A desirable skeletonization algorithm has to be topologically complete, insensitive to boundary noise, without any branch pruning and free of user-specified parameters. In this paper, we propose a novel method based on the heat equation to extract skeletons assuming that the input is a binary image. Based on the connection between the skeleton and ridge lines, we use the smooth heat diffusion height surface to approximate the real distance field, and then extract the ridge lines of the surface as the output skeleton. Different from the existing approaches, the proposed method in this paper has the above-mentioned excellent properties and can capture the clean and stable skeleton directly. Extensive experimental results show that the new approach can yield better skeletons than the state-of-the-art.

The animation of articulated characters is a central problem in the computer graphics field. Skeletal animation techniques define a workflow which has proven to be effective for boundary representations (B-Reps). This paper extends the classical skeletal animation pipeline to deal with characters internal tissues. In contrast to most common approaches, the proposed technique automates all the stages of this workflow. Well known skinning algorithms, such as Linear Blending Skinning, Dual Quaternion Skinning or Optimized Centers of Rotation were adapted to allow the use of our technique in applications where interactivity is required. The pipeline proposed in this paper can be used in many computer graphics systems such as games or educational applications to visualize and animate the internal anatomy of a virtual character at interactive rates.