Volume 42 (2023)

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https://diglib.eg.org/handle/10.2312/2633290

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Data Parallel Multi-GPU Path Tracing using Ray Queue Cycling
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Wald, Ingo; Jaros, Milan; Zellmann, Stefan; Bikker, Jacco; Gribble, Christiaan
We propose a novel approach to data-parallel path tracing on single-node/multi-GPU hardware that builds on ray forwarding, but which aims-above all else-at generality and practicability. We do this by avoiding any attempts at reducing the number of traces or forward operations performed, and instead focus on always using all GPUs' aggregate compute and bandwidth to effectively trace each ray on every GPU. We show that-counter-intuitively-this is both feasible and desirable; and that when run on typical data-center/cloud hardware, the resulting framework not only achieves good performance and scalability, but also comes with significantly fewer limitations, assumptions, or preprocessing requirements than existing techniques.
CubeGAN: Omnidirectional Image Synthesis Using Generative Adversarial Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2023) May, Christopher; Aliaga, Daniel; Myszkowski, Karol; Niessner, Matthias
We propose a framework to create projectively-correct and seam-free cube-map images using generative adversarial learning. Deep generation of cube-maps that contain the correct projection of the environment onto its faces is not straightforward as has been recognized in prior work. Our approach extends an existing framework, StyleGAN3, to produce cube-maps instead of planar images. In addition to reshaping the output, we include a cube-specific volumetric initialization component, a projective resampling component, and a modification of augmentation operations to the spherical domain. Our results demonstrate the network's generation capabilities trained on imagery from various 3D environments. Additionally, we show the power and quality of our GAN design in an inversion task, combined with navigation capabilities, to perform novel view synthesis.
Remeshing‐free Graph‐based Finite Element Method for Fracture Simulation
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Mandal, A.; Chaudhuri, P.; Chaudhuri, S.; Hauser, Helwig and Alliez, Pierre
Fracture produces new mesh fragments that introduce additional degrees of freedom in the system dynamics. Existing finite element method (FEM) based solutions suffer from increasing computational cost as the system matrix size increases. We solve this problem by presenting a graph‐based FEM model for fracture simulation that is remeshing‐free and easily scales to high‐resolution meshes. Our algorithm models fracture on the graph induced in a volumetric mesh with tetrahedral elements. We relabel the edges of the graph using a computed damage variable to initialize and propagate fracture. We prove that non‐linear, hyper‐elastic strain energy density is expressible entirely in terms of the edge lengths of the induced graph. This allows us to reformulate the system dynamics for the relabelled graph without changing the size of the system dynamics matrix and thus prevents the computational cost from blowing up. The fractured surface has to be reconstructed explicitly only for visualization purposes. We simulate standard laboratory experiments from structural mechanics and compare the results with corresponding real‐world experiments. We fracture objects made of a variety of brittle and ductile materials, and show that our technique offers stability and speed that is unmatched in current literature.
Efficient Storage and Importance Sampling for Fluorescent Reflectance
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hua, Q.; Tázlar, V.; Fichet, A.; Wilkie, A.; Hauser, Helwig and Alliez, Pierre
We propose a technique for efficient storage and importance sampling of fluorescent spectral data. Fluorescence is fully described by a re‐radiation matrix, which for a given input wavelength indicates how much energy is re‐emitted at other wavelengths. However, such representation has a considerable memory footprint. To significantly reduce memory requirements, we propose the use of Gaussian mixture models for the representation of re‐radiation matrices. Instead of the full‐resolution matrix, we work with a set of Gaussian parameters that also allow direct importance sampling. Furthermore, if accuracy is of concern, a re‐radiation matrix can be used jointly with efficient importance sampling provided by the Gaussian mixture. In this paper, we present our pipeline for efficient storage of bispectral data and provide its extensive evaluation on a large set of bispectral measurements. We show that our method is robust and colour accurate even with its comparably minor memory requirements and that it can be seamlessly integrated into a standard Monte Carlo path tracer.
Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Filipov, Velitchko; Arleo, Alessio; Miksch, Silvia; Hauser, Helwig and Alliez, Pierre
Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever‐growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a “roadmap” detailing the current research trends in the field of network visualization. We design our contribution as a meta‐survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.
HDRNet: High‐Dimensional Regression Network for Point Cloud Registration
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Gao, Jian; Zhang, Yuhe; Liu, Zehua; Li, Siyi; Hauser, Helwig and Alliez, Pierre
Abstract‐3D point cloud registration is a crucial topic in the reverse engineering, computer vision and robotics fields. The core of this problem is to estimate a transformation matrix for aligning the source point cloud with a target point cloud. Several learning‐based methods have achieved a high performance. However, they are challenged with both partial overlap point clouds and multiscale point clouds, since they use the singular value decomposition (SVD) to find the rotation matrix without fully considering the scale information. Furthermore, previous networks cannot effectively handle the point clouds having large initial rotation angles, which is a common practical case. To address these problems, this paper presents a learning‐based point cloud registration network, namely HDRNet, which consists of four stages: local feature extraction, correspondence matrix estimation, feature embedding and fusion and parametric regression. HDRNet is robust to noise and large rotation angles, and can effectively handle the partial overlap and multi‐scale point clouds registration. The proposed model is trained on the ModelNet40 dataset, and compared with ICP, SICP, FGR and recent learning‐based methods (PCRNet, IDAM, RGMNet and GMCNet) under several settings, including its performance on moving to invisible objects, with higher success rates. To verify the effectiveness and generality of our model, we also further tested our model on the Stanford 3D scanning repository.
Proliferating cell nuclear antigen sliding along DNA
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hauser, Helwig and Alliez, Pierre
One-to-Many Spectral Upsampling of Reflectances and Transmittances
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Belcour, Laurent; Barla, Pascal; Guennebaud, Gaël; Ritschel, Tobias; Weidlich, Andrea
Spectral rendering is essential for the production of physically-plausible synthetic images, but requires to introduce several changes in the content generation pipeline. In particular, the authoring of spectral material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new problems. While a large panel of computer graphics methods exists to upsample a RGB color to a spectrum, they all provide a one-to-one mapping. This limits the ability to control interesting color changes such as the Usambara effect or metameric spectra. In this work, we introduce a one-to-many mapping in which we show how we can explore the set of all spectra reproducing a given input color. We apply this method to different colour changing effects such as vathochromism - the change of color with depth, and metamerism.
Issue Information
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hauser, Helwig and Alliez, Pierre
Improved Evaluation and Generation Of Grid Layouts Using Distance Preservation Quality and Linear Assignment Sorting
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Barthel, K. U.; Hezel, N.; Jung, K.; Schall, K.; Hauser, Helwig and Alliez, Pierre
Images sorted by similarity enables more images to be viewed simultaneously, and can be very useful for stock photo agencies or e‐commerce applications. Visually sorted grid layouts attempt to arrange images so that their proximity on the grid corresponds as closely as possible to their similarity. Various metrics exist for evaluating such arrangements, but there is low experimental evidence on correlation between human perceived quality and metric value. We propose distance preservation quality (DPQ) as a new metric to evaluate the quality of an arrangement. Extensive user testing revealed stronger correlation of DPQ with user‐perceived quality and performance in image retrieval tasks compared to other metrics. In addition, we introduce Fast linear assignment sorting (FLAS) as a new algorithm for creating visually sorted grid layouts. FLAS achieves very good sorting qualities while improving run time and computational resources.
Real-Time Ray Tracing of Micro-Poly Geometry with Hierarchical Level of Detail
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Benthin, Carsten; Peters, Christoph; Bikker, Jacco; Gribble, Christiaan
In recent work, Nanite has demonstrated how to rasterize virtualized micro-poly geometry in real time, thus enabling immense geometric complexity. We present a system that employs similar methods for real-time ray tracing of micro-poly geometry. The geometry is preprocessed in almost the same fashion: Nearby triangles are clustered together and clusters get merged and simplified to obtain hierarchical level of detail (LOD). Then these clusters are compressed and stored in a GPU-friendly data structure. At run time, Nanite selects relevant clusters, decompresses them and immediately rasterizes them. Instead of rasterization, we decompress each selected cluster into a small bounding volume hierarchy (BVH) in the format expected by the ray tracing hardware. Then we build a complete BVH on top of the bounding volumes of these clusters and use it for ray tracing. Our BVH build reaches more than 74% of the attainable peak memory bandwidth and thus it can be done per frame. Since LOD selection happens per frame at the granularity of clusters, all triangles cover a small area in screen space.
Deep Shape and SVBRDF Estimation using Smartphone Multi-lens Imaging
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Fan, Chongrui; Lin, Yiming; Ghosh, Abhijeet; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
We present a deep neural network-based method that acquires high-quality shape and spatially varying reflectance of 3D objects using smartphone multi-lens imaging. Our method acquires two images simultaneously using a zoom lens and a wide angle lens of a smartphone under either natural illumination or phone flash conditions, effectively functioning like a single-shot method. Unlike traditional multi-view stereo methods which require sufficient differences in viewpoint and only estimate depth at a certain coarse scale, our method estimates fine-scale depth by utilising an optical-flow field extracted from subtle baseline and perspective due to different optics in the two images captured simultaneously. We further guide the SVBRDF estimation using the estimated depth, resulting in superior results compared to existing single-shot methods.
tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Ande, Abhijath; Subhash, Varshini; Natarajan, Vijay; Hauser, Helwig and Alliez, Pierre
The extremum graph is a succinct representation of the Morse decomposition of a scalar field. It has increasingly become a useful data structure that supports topological feature‐directed visualization of 2D/3D scalar fields, and enables dimensionality reduction together with exploratory analysis of high‐dimensional scalar fields. Current methods that employ the extremum graph compute it either using a simple sequential algorithm for computing the Morse decomposition or by computing the more detailed Morse–Smale complex. Both approaches are typically limited to two and three‐dimensional scalar fields. We describe a GPU–CPU hybrid parallel algorithm for computing the extremum graph of scalar fields in all dimensions. The proposed shared memory algorithm utilizes both fine‐grained parallelism and task parallelism to achieve efficiency. An open source software library, , that implements the algorithm exhibits superior performance and good scaling behaviour.
In-the-wild Material Appearance Editing using Perceptual Attributes
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Subías, José Daniel; Lagunas, Manuel; Myszkowski, Karol; Niessner, Matthias
Intuitively editing the appearance of materials from a single image is a challenging task given the complexity of the interactions between light and matter, and the ambivalence of human perception. This problem has been traditionally addressed by estimating additional factors of the scene like geometry or illumination, thus solving an inverse rendering problem and subduing the final quality of the results to the quality of these estimations. We present a single-image appearance editing framework that allows us to intuitively modify the material appearance of an object by increasing or decreasing high-level perceptual attributes describing such appearance (e.g., glossy or metallic). Our framework takes as input an in-the-wild image of a single object, where geometry, material, and illumination are not controlled, and inverse rendering is not required. We rely on generative models and devise a novel architecture with Selective Transfer Unit (STU) cells that allow to preserve the high-frequency details from the input image in the edited one. To train our framework we leverage a dataset with pairs of synthetic images rendered with physically-based algorithms, and the corresponding crowd-sourced ratings of high-level perceptual attributes. We show that our material editing framework outperforms the state of the art, and showcase its applicability on synthetic images, in-the-wild real-world photographs, and video sequences.
Investigation and Simulation of Diffraction on Rough Surfaces
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Clausen, O.; Chen, Y.; Fuhrmann, A.; Marroquim, R.; Hauser, Helwig and Alliez, Pierre
Simulating light–matter interaction is a fundamental problem in computer graphics. A particular challenge is the simulation of light interaction with rough surfaces due to diffraction and multiple scattering phenomena. To properly model these phenomena, wave‐optics have to be considered. Nevertheless, the most accurate BRDF models, including wave‐optics, are computationally expensive, and the resulting renderings have not been systematically compared to real‐world measurements. This work sheds more light on reflectance variations due to surface roughness. More specifically, we look at wavelength shifts that lead to reddish and blueish appearances. These wavelength shifts have been scarcely reported in the literature, and, in this paper, we provide the first thorough analysis from precise measured data. We measured the spectral in‐plane BRDF of aluminium samples with varying roughness and further acquired the surface topography with a confocal microscope. The measurements show that the rough samples have, on average, a reddish and blueish appearance in the forward and back‐scattering, respectively. Our investigations conclude that this is a diffraction‐based effect that dominates the overall appearance of the samples. Simulations using a virtual gonioreflectometer further confirm our claims. We propose a linear model that can closely fit such phenomena, where the slope of the wavelength shifts depends on the incident and reflection direction. Based on these insights, we developed a simple BRDF model based on the Cook–Torrance model that considers such wavelength shifts.
Immersive Free‐Viewpoint Panorama Rendering from Omnidirectional Stereo Video
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Mühlhausen, Moritz; Kappel, Moritz; Kassubeck, Marc; Wöhler, Leslie; Grogorick, Steve; Castillo, Susana; Eisemann, Martin; Magnor, Marcus; Hauser, Helwig and Alliez, Pierre
In this paper, we tackle the challenging problem of rendering real‐world 360° panorama videos that support full 6 degrees‐of‐freedom (DoF) head motion from a prerecorded omnidirectional stereo (ODS) video. In contrast to recent approaches that create novel views for individual panorama frames, we introduce a video‐specific temporally‐consistent multi‐sphere image (MSI) scene representation. Given a conventional ODS video, we first extract information by estimating framewise descriptive feature maps. Then, we optimize the global MSI model using theory from recent research on neural radiance fields. Instead of a continuous scene function, this multi‐sphere image (MSI) representation depicts colour and density information only for a discrete set of concentric spheres. To further improve the temporal consistency of our results, we apply an ancillary refinement step which optimizes the temporal coherency between successive video frames. Direct comparisons to recent baseline approaches show that our global MSI optimization yields superior performance in terms of visual quality. Our code and data will be made publicly available.
EUROGRAPHICS 2023: CGF 42-2 Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Myszkowski, Karol; Niessner, Matthias; Myszkowski, Karol; Niessner, Matthias
Variational Pose Prediction with Dynamic Sample Selection from Sparse Tracking Signals
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Milef, Nicholas; Sueda, Shinjiro; Kalantari, Nima Khademi; Myszkowski, Karol; Niessner, Matthias
We propose a learning-based approach for full-body pose reconstruction from extremely sparse upper body tracking data, obtained from a virtual reality (VR) device. We leverage a conditional variational autoencoder with gated recurrent units to synthesize plausible and temporally coherent motions from 4-point tracking (head, hands, and waist positions and orientations). To avoid synthesizing implausible poses, we propose a novel sample selection and interpolation strategy along with an anomaly detection algorithm. Specifically, we monitor the quality of our generated poses using the anomaly detection algorithm and smoothly transition to better samples when the quality falls below a statistically defined threshold. Moreover, we demonstrate that our sample selection and interpolation method can be used for other applications, such as target hitting and collision avoidance, where the generated motions should adhere to the constraints of the virtual environment. Our system is lightweight, operates in real-time, and is able to produce temporally coherent and realistic motions.
Dissection Puzzles Composed of Multicolor Polyominoes
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Kita, Naoki; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
Dissection puzzles leverage geometric dissections, wherein a set of puzzle pieces can be reassembled in various configurations to yield unique geometric figures. Mathematically, a dissection between two 2D polygons can always be established. Consequently, researchers and puzzle enthusiasts strive to design unique dissection puzzles using the fewest pieces feasible. In this study, we introduce novel dissection puzzles crafted with multi-colored polyominoes. Diverging from the traditional aim of establishing geometric dissection between two 2D polygons with the minimal piece count, we seek to identify a common pool of polyomino pieces with colored faces that can be configured into multiple distinct shapes and appearances. Moreover, we offer a method to identify an optimized sequence for rearranging pieces from one form to another, thus minimizing the total relocation distance. This approach can guide users in puzzle assembly and lessen their physical exertion when manually reconfiguring pieces. It could potentially also decrease power consumption when pieces are reorganized using robotic assistance. We showcase the efficacy of our proposed approach through a wide range of shapes and appearances.
3D Keypoint Estimation Using Implicit Representation Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Zhu, Xiangyu; Du, Dong; Huang, Haibin; Ma, Chongyang; Han, Xiaoguang; Memari, Pooran; Solomon, Justin
In this paper, we tackle the challenging problem of 3D keypoint estimation of general objects using a novel implicit representation. Previous works have demonstrated promising results for keypoint prediction through direct coordinate regression or heatmap-based inference. However, these methods are commonly studied for specific subjects, such as human bodies and faces, which possess fixed keypoint structures. They also suffer in several practical scenarios where explicit or complete geometry is not given, including images and partial point clouds. Inspired by the recent success of advanced implicit representation in reconstruction tasks, we explore the idea of using an implicit field to represent keypoints. Specifically, our key idea is employing spheres to represent 3D keypoints, thereby enabling the learnability of the corresponding signed distance field. Explicit keypoints can be extracted subsequently by our algorithm based on the Hough transform. Quantitative and qualitative evaluations also show the superiority of our representation in terms of prediction accuracy.

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