Volume 40 (2021)

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Thin-Volume Visualization on Curved Domains
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Herter, Felix; Hege, Hans-Christian; Hadwiger, Markus; Lepper, Verena; Baum, Daniel; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, as structure-adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view. We solve this problem by rendering a spatially transformed view of the volume so that an unobstructed visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray-casting, in which the rays traverse the original volume. In order to carve out volumes of varying thicknesses, the lengths of the rays as well as the positions of the mesh vertices can be easily modified by interactive painting under view control. We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and material sciences. The visual representation of the structure as a whole allows for easy inspection of interesting substructures in their original spatial context. Overall, we show that thin, curved structures in volumetric data can be excellently visualized using ray-casting-based volume rendering of transformed views defined by guiding surface meshes, supplemented by interactive, local modifications of ray lengths and vertex positions.
Coherent Mark-based Stylization of 3D Scenes at the Compositing Stage
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Garcia, Maxime; Vergne, Romain; Farhat, Mohamed-Amine; Bénard, Pierre; Noûs, Camille; Thollot, Joëlle; Mitra, Niloy and Viola, Ivan
We present a novel temporally coherent stylized rendering technique working entirely at the compositing stage. We first generate a distribution of 3D anchor points using an implicit grid based on the local object positions stored in a G-buffer, hence following object motion. We then draw splats in screen space anchored to these points so as to be motion coherent. To increase the perceived flatness of the style, we adjust the anchor points density using a fractalization mechanism. Sudden changes are prevented by controlling the anchor points opacity and introducing a new order-independent blending function. We demonstrate the versatility of our method by showing a large variety of styles thanks to the freedom offered by the splats content and their attributes that can be controlled by any G-buffer.
Blending of Hyperbolic Closed Curves
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Ikemakhen, Aziz; Ahanchaou, Taoufik; Digne, Julie and Crane, Keenan
In recent years, game developers are interested in developing games in the hyperbolic space. Shape blending is one of the fundamental techniques to produce animation and videos games. This paper presents two algorithms for blending between two closed curves in the hyperbolic plane in a manner that guarantees that the intermediate curves are closed. We deal with hyperbolic discrete curves on Poincaré disc which is a famous model of the hyperbolic plane. We use the linear interpolation approach of the geometric invariants of hyperbolic polygons namely hyperbolic side lengths, exterior angles and geodesic discrete curvature. We formulate the closing condition of a hyperbolic polygon in terms of its geodesic side lengths and exterior angles. This is to be able to generate closed intermediate curves. Finally, some experimental results are given to illustrate that the proposed methods generate aesthetic blending of closed hyperbolic curves.
Neural Acceleration of Scattering-Aware Color 3D Printing
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Rittig, Tobias; Sumin, Denis; Babaei, Vahid; Didyk, Piotr; Voloboy, Alexey; Wilkie, Alexander; Bickel, Bernd; Myszkowski, Karol; Weyrich, Tim; Krivánek, Jaroslav; Mitra, Niloy and Viola, Ivan
With the wider availability of full-color 3D printers, color-accurate 3D-print preparation has received increased attention. A key challenge lies in the inherent translucency of commonly used print materials that blurs out details of the color texture. Previous work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels. To date, the highest-quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution; that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by replacing the light transport simulation with a data-driven approach. Leveraging a deep neural network to predict the scattering within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering while yielding optimization results of similar quality level. The network is based on an established method from atmospheric cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces the network size. We analyze its performance in an end-to-end print preparation pipeline and compare quality and runtime to alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables full heterogenous material optimization for 3D-print preparation within time frames in the order of the actual printing time.
Example‐Based Colour Transfer for 3D Point Clouds
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Goudé, Ific; Cozot, Rémi; Le Meur, Olivier; Bouatouch, Kadi; Benes, Bedrich and Hauser, Helwig
Example‐based colour transfer between images, which has raised a lot of interest in the past decades, consists of transferring the colour of an image to another one. Many methods based on colour distributions have been proposed, and more recently, the efficiency of neural networks has been demonstrated again for colour transfer problems. In this paper, we propose a new pipeline with methods adapted from the image domain to automatically transfer the colour from a target point cloud to an input point cloud. These colour transfer methods are based on colour distributions and account for the geometry of the point clouds to produce a coherent result. The proposed methods rely on simple statistical analysis, are effective, and succeed in transferring the colour style from one point cloud to another. The qualitative results of the colour transfers are evaluated and compared with existing methods.
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(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
A Data-Driven Approach to Functional Map Construction and Bases Pursuit
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Azencot, Omri; Lai, Rongjie; Digne, Julie and Crane, Keenan
We propose a method to simultaneously compute scalar basis functions with an associated functional map for a given pair of triangle meshes. Unlike previous techniques that put emphasis on smoothness with respect to the Laplace-Beltrami operator and thus favor low-frequency eigenfunctions, we aim for a basis that allows for better feature matching. This change of perspective introduces many degrees of freedom into the problem allowing to better exploit non-smooth descriptors. To effectively search in this high-dimensional space of solutions, we incorporate into our minimization state-of-the-art regularizers. We solve the resulting highly non-linear and non-convex problem using an iterative scheme via the Alternating Direction Method of Multipliers. At each step, our optimization involves simple to solve linear or Sylvester-type equations. In practice, our method performs well in terms of convergence, and we additionally show that it is similar to a provably convergent problem. We show the advantages of our approach by extensively testing it on multiple datasets in a few applications including shape matching, consistent quadrangulation and scalar function transfer.
SketchZooms: Deep Multi‐view Descriptors for Matching Line Drawings
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Navarro, Pablo; Orlando, J. Ignacio; Delrieux, Claudio; Iarussi, Emmanuel; Benes, Bedrich and Hauser, Helwig
Finding point‐wise correspondences between images is a long‐standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper, we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non‐photorealistic rendering over a large collection of part‐based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown togeneralize correctly in unseen sketches hand‐drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.
SREC‐RT: A Structure for Ray Tracing Rounded Edges and Corners
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Courtin, Simon; Ribardière, Mickael; Horna, Sebastien; Poulin, Pierre; Meneveaux, Daniel; Benes, Bedrich and Hauser, Helwig
Man‐made objects commonly exhibit rounded edges and corners generated through their manufacturing processes. The variation of surface normals at these confined locations produces shading details that are visually essential to the realism of synthetic scenes. The more specular the surface, the finer and more prominent its highlights. However, most geometric modellers represent rounded edges and corners with dense polygonal meshes that are limited in terms of smoothness, while tremendously increasing scene complexity. This paper proposes a non‐invasive method (i.e. that does not modify the original geometry) for the modelling and rendering of smooth edges and corners from any input polygonal geometry defined with infinitely sharp edges. At the heart of our contribution is a geometric structure that automatically and accurately defines the geometry of edge and corner rounded areas, as well as the topological relationships at edges and vertices. This structure, called SREC‐RT, is integrated in a ray‐tracing‐based acceleration structure in order to determine the region of interest of each rounded edge and corner. It allows systematic rounding of all edges and vertices without increasing the 3D scene geometric complexity. While the underlying rounded geometry can be of any type, we propose a practical ray‐edge and ray‐corner intersection based on parametric surfaces. We analyse comparisons generated with existing methods. Our results present the advantages of our method, including extreme close‐up views of surfaces with a much higher quality for very little additional memory, and reasonable computation time overhead.
Temporally Reliable Motion Vectors for Real-time Ray Tracing
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Zeng, Zheng; Liu, Shiqiu; Yang, Jinglei; Wang, Lu; Yan, Ling-Qi; Mitra, Niloy and Viola, Ivan
Real-time ray tracing (RTRT) is being pervasively applied. The key to RTRT is a reliable denoising scheme that reconstructs clean images from significantly undersampled noisy inputs, usually at 1 sample per pixel as limited by current hardware's computing power. The state of the art reconstruction methods all rely on temporal filtering to find correspondences of current pixels in the previous frame, described using per-pixel screen-space motion vectors. While these approaches are demonstrated powerful, they suffer from a common issue that the temporal information cannot be used when the motion vectors are not valid, i.e. when temporal correspondences are not obviously available or do not exist in theory. We introduce temporally reliable motion vectors that aim at deeper exploration of temporal coherence, especially for the generally-believed difficult applications on shadows, glossy reflections and occlusions, with the key idea to detect and track the cause of each effect. We show that our temporally reliable motion vectors produce significantly better temporal results on a variety of dynamic scenes when compared to the state of the art methods, but with negligible performance overhead.
Fabrication-Aware Reverse Engineering for Carpentry
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Noeckel, James; Zhao, Haisen; Curless, Brian; Schulz, Adriana; Digne, Julie and Crane, Keenan
We propose a novel method to generate fabrication blueprints from images of carpentered items. While 3D reconstruction from images is a well-studied problem, typical approaches produce representations that are ill-suited for computer-aided design and fabrication applications. Our key insight is that fabrication processes define and constrain the design space for carpentered objects, and can be leveraged to develop novel reconstruction methods. Our method makes use of domain-specific constraints to recover not just valid geometry, but a semantically valid assembly of parts, using a combination of image-based and geometric optimization techniques. We demonstrate our method on a variety of wooden objects and furniture, and show that we can automatically obtain designs that are both easy to edit and accurate recreations of the ground truth. We further illustrate how our method can be used to fabricate a physical replica of the captured object as well as a customized version, which can be produced by directly editing the reconstructed model in CAD software.
UprightRL: Upright Orientation Estimation of 3D Shapes via Reinforcement Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Chen, Luanmin; Xu, Juzhan; Wang, Chuan; Huang, Haibin; Huang, Hui; Hu, Ruizhen; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
In this paper, we study the problem of 3D shape upright orientation estimation from the perspective of reinforcement learning, i.e. we teach a machine (agent) to orientate 3D shapes step by step to upright given its current observation. Unlike previous methods, we take this problem as a sequential decision-making process instead of a strong supervised learning problem. To achieve this, we propose UprightRL, a deep network architecture designed for upright orientation estimation. UprightRL mainly consists of two submodules: an Actor module and a Critic module which can be learned with a reinforcement learning manner. Specifically, the Actor module selects an action from the action space to perform a point cloud transformation and obtain the new point cloud for the next environment state, while the Critic module evaluates the strategy and guides the Actor to choose the next stage action. Moreover, we design a reward function that encourages the agent to select action which is conducive to orient model towards upright orientation with a positive reward and negative otherwise. We conducted extensive experiments to demonstrate the effectiveness of the proposed model, and experimental results show that our network outperforms the stateof- the-art. We also apply our method to the robot grasping-and-placing experiment, to reveal the practicability of our method.
Modeling Visual Containment for Web Page Layout Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Kikuchi, Kotaro; Otani, Mayu; Yamaguchi, Kota; Simo-Serra, Edgar; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
Web pages have become fundamental in conveying information for companies and individuals, yet designing web page layouts remains a challenging task for inexperienced individuals despite web builders and templates. Visual containment, in which elements are grouped together and placed inside container elements, is an efficient design strategy for organizing elements in a limited display, and is widely implemented in most web page designs. Yet, visual containment has not been explicitly addressed in the research on generating layouts from scratch, which may be due to the lack of hierarchical structure. In this work, we represent such visual containment as a layout tree, and formulate the layout design task as a hierarchical optimization problem. We first estimate the layout tree from a given a set of elements, which is then used to compute tree-aware energies corresponding to various desirable design properties such as alignment or spacing. Using an optimization approach also allows our method to naturally incorporate user intentions and create an interactive web design application. We obtain a dataset of diverse and popular real-world web designs to optimize and evaluate various aspects of our method. Experimental results show that our method generates better quality layouts compared to the baseline method.
Fashion Transfer: Dressing 3D Characters from Stylized Fashion Sketches
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Fondevilla, Amelie; Rohmer, Damien; Hahmann, Stefanie; Bousseau, Adrien; Cani, Marie‐Paule; Benes, Bedrich and Hauser, Helwig
Fashion design often starts with hand‐drawn, expressive sketches that communicate the essence of a garment over idealized human bodies. We propose an approach to automatically dress virtual characters from such input, previously complemented with user‐annotations. In contrast to prior work requiring users to draw garments with accurate proportions over each virtual character to be dressed, our method follows a style transfer strategy : the information extracted from a single, annotated fashion sketch can be used to inform the synthesis of one to many new garment(s) with similar style, yet different proportions. In particular, we define the style of a loose garment from its silhouette and folds, which we extract from the drawing. Key to our method is our strategy to extract both shape and repetitive patterns of folds from the 2D input. As our results show, each input sketch can be used to dress a variety of characters of different morphologies, from virtual humans to cartoon‐style characters.
Optimizing LBVH‐Construction and Hierarchy‐Traversal to accelerate kNN Queries on Point Clouds using the GPU
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Jakob, J.; Guthe, M.; Benes, Bedrich and Hauser, Helwig
Processing point clouds often requires information about the point neighbourhood in order to extract, calculate and determine characteristics. We continue the tradition of developing increasingly faster neighbourhood query algorithms and present a highly efficient algorithm for solving the exact neighbourhood problem in point clouds using the GPU. Both, the required data structures and the NN query, are calculated entirely on the GPU. This enables real‐time performance for large queries in extremely large point clouds. Our experiments show a more than threefold acceleration, compared to state‐of‐the‐art GPU based methods including all memory transfers. In terms of pure query performance, we achieve over answered neighbourhood queries per millisecond for 16 nearest neighbours on common graphics hardware.
Restricted Power Diagrams on the GPU
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Basselin, Justine; Alonso, Laurent; Ray, Nicolas; Sokolov, Dmitry; Lefebvre, Sylvain; Lévy, Bruno; Mitra, Niloy and Viola, Ivan
We propose a method to simultaneously decompose a 3D object into power diagram cells and to integrate given functions in each of the obtained simple regions.We offer a novel, highly parallel algorithm that lends itself to an efficient GPU implementation. It is optimized for algorithms that need to compute many decompositions, for instance, centroidal Voronoi tesselation algorithms and incompressible fluid dynamics simulations. We propose an efficient solution that directly evaluates the integrals over every cell without computing the power diagram explicitly and without intersecting it with a tetrahedralization of the domain. Most computations are performed on the fly, without storing the power diagram. We manipulate a triangulation of the boundary of the domain (instead of tetrahedralizing the domain) to speed up the process. Moreover, the cells are treated independently one from another, making it possible to trivially scale up on a parallel architecture. Despite recent Voronoi diagram generation methods optimized for the GPU, computing integrals over restricted power diagrams still poses significant challenges; the restriction to a complex simulation domain is difficult and likely to be slow. It is not trivial to determine when a cell of a power diagram is completely computed, and the resulting integrals (e.g. the weighted Laplacian operator matrix) do not fit into fast (shared) GPU memory. We address all these issues and boost the performance of the state-of-the-art algorithms by a factor 2 to 3 for (unrestricted) Voronoi diagrams and ax50 speed-up with respect to CPU implementations for restricted power diagrams. An essential ingredient to achieve this is our new scheduling strategy that allows us to treat each Voronoi/power diagram cell with optimal settings and to benefit from the fast memory.
Optimizing Ray Tracing of Trimmed NURBS Surfaces on the GPU
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sloup, Jaroslav; Havran, Vlastimil; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
The representation of geometric models by trimmed NURBS surfaces has become a standard in the CAD industry. In CAD applications, the rendering of surfaces is usually solved by tessellation followed up by z-buffer rendering. Ray tracing of NURBS surfaces has not been widely used in industry due to its computational complexity that hinders achieving real-time performance in practice. We propose novel methods achieving faster point location search needed by trimming in the context of ray tracing trimmed NURBS surfaces. The proposed 2D data structure based on kd-trees allows for faster ray tracing while it requires less memory for its representation and less preprocessing time than previously published methods. Further, we show the current state of the art for ray tracing trimmed NURBS surfaces on a GPU. With careful design and implementation, the number of rays cast on a GPU may reach real-time performance in the order of tens to hundreds of million rays per second for moderately to large complex scenes containing hundreds of thousands of NURBS surfaces and trimming curves.
Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sulzer, Raphael; Landrieu, Loic; Marlet, Renaud; Vallet, Bruno; Digne, Julie and Crane, Keenan
We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of point cloud defects encountered in real-life Multi-View Stereo (MVS) acquisitions. Our method relies on a 3D Delaunay tetrahedralization whose cells are classified as inside or outside the surface by a graph neural network and an energy model solvable with a graph cut. Our model, making use of both local geometric attributes and line-of-sight visibility information, is able to learn a visibility model from a small amount of synthetic training data and generalizes to real-life acquisitions. Combining the efficiency of deep learning methods and the scalability of energybased models, our approach outperforms both learning and non learning-based reconstruction algorithms on two publicly available reconstruction benchmarks.
IMAT: The Iterative Medial Axis Transform
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Lee, Yonghyeon; Baek, Jonghyuk; Kim, Young Min; Park, Frank Chongwoo; Benes, Bedrich and Hauser, Helwig
We present the iterative medial axis transform (IMAT), an iterative descent method that constructs a medial axis transform (MAT) for a sparse, noisy, oriented point cloud sampled from an object's boundary. We first establish the equivalence between the traditional definition of the MAT of an object, i.e., the set of centres and corresponding radii of all balls maximally inscribed inside the object, with an alternative characterization matching the boundary enclosing the union of the balls with the object boundary. Based on this boundary equivalence characterization, a new MAT algorithm is proposed, in which an error function that reflects the difference between the two boundaries is minimized while restricting the number of balls to within some a priori specified upper limit. An iterative descent method with guaranteed local convergence is developed for the minimization that is also amenable to parallelization. Both quantitative and qualitative analyses of diverse 2D and 3D objects demonstrate the noise robustness, shape fidelity, and representation efficiency of the resulting MAT.
LoBSTr: Real-time Lower-body Pose Prediction from Sparse Upper-body Tracking Signals
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Yang, Dongseok; Kim, Doyeon; Lee, Sung-Hee; Mitra, Niloy and Viola, Ivan
With the popularization of games and VR/AR devices, there is a growing need for capturing human motion with a sparse set of tracking data. In this paper, we introduce a deep neural network (DNN) based method for real-time prediction of the lowerbody pose only from the tracking signals of the upper-body joints. Specifically, our Gated Recurrent Unit (GRU)-based recurrent architecture predicts the lower-body pose and feet contact states from a past sequence of tracking signals of the head, hands, and pelvis. A major feature of our method is that the input signal is represented by the velocity of tracking signals. We show that the velocity representation better models the correlation between the upper-body and lower-body motions and increases the robustness against the diverse scales and proportions of the user body than position-orientation representations. In addition, to remove foot-skating and floating artifacts, our network predicts feet contact state, which is used to post-process the lower-body pose with inverse kinematics to preserve the contact. Our network is lightweight so as to run in real-time applications. We show the effectiveness of our method through several quantitative evaluations against other architectures and input representations with respect to wild tracking data obtained from commercial VR devices.

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