VMV2022

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https://diglib.eg.org/handle/10.2312/2633214

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Evaluation of Volume Representation Networks for Meteorological Ensemble Compression
(The Eurographics Association, 2022) Höhlein, Kevin; Weiss, Sebastian; Necker, Tobias; Weissmann, Martin; Miyoshi, Takemasa; Westermann, Rüdiger; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Recent studies have shown that volume scene representation networks constitute powerful means to transform 3D scalar fields into extremely compact representations, from which the initial field samples can be randomly accessed. In this work, we evaluate the capabilities of such networks to compress meteorological ensemble data, which are comprised of many separate weather forecast simulations. We analyze whether these networks can effectively exploit similarities between the ensemble members, and how alternative classical compression approaches perform in comparison. Since meteorological ensembles contain different physical parameters with various statistical characteristics and variations on multiple scales of magnitude, we analyze the impact of data normalization schemes on learning quality. Along with an evaluation of the trade-offs between reconstruction quality and network model parameterization, we compare compression ratios and reconstruction quality for different model architectures and alternative compression schemes.
Clasping Trees - A Pipeline for Interactive Procedural Tree Generation
(The Eurographics Association, 2022) Lieb, Simon J.; Klee, Nicolas; Lawonn, Kai; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Trees in computer games are important components of an immersive game world. Realistic trees adapt to the environment in terms of shape and growth. Manually adapting each tree to its immediate environment is time-consuming. Hence, we present a pipeline to procedurally generate trees. This pipeline's input consists of tree-parameters and mesh sets. Tree-parameters have a direct influence on the final appearance of the tree. Meshes are used to indicate the space of the tree crown and surface for roots. We provide an overview of the necessary methods for procedural tree generation. Our method allows game developers to integrate the pipeline directly into their game engine, skipping the process of importing and maintaining external 3D-models. We used the Space Colonization Algorithm to generate roots of trees on the surface of a set of meshes. For the crown generation, we use an extended Space Colonization Algorithm called Self Organizing Trees. To receive the combined surface and volume of a set of meshes, we voxelize the individual mesh and compose it into a single voxel grid. We introduce two novel optimization methods to further increase the usability of the generated trees. These optimization methods decrease the necessary triangle count of the final mesh. The resulting trees can be used for real-life applications, such as games.
Relaxed Parallel Priority Queue with Filter Levels for Parallel Mesh Decimation
(The Eurographics Association, 2022) Stümmel, Marvin; Brüll, Felix; Grosch, Thorsten; Bender, Jan; Botsch, Mario; Keim, Daniel A.
We propose a novel implementation of a parallel priority queue in the context of multithreaded mesh decimation. Previous parallel priority queues either have a major bottleneck when extracting nodes, cannot guarantee reasonable node quality for the extracted nodes, or cannot be used for mesh decimation. Our data structure allows the extraction of multiple high-priority elements at the same time. For this, we relax the requirement of returning the highest priority element to returning an element that belongs to the top k elements. We demonstrate its use in the context of parallel mesh decimation and show that our decimated mesh is almost indistinguishable from an optimally decimated mesh while being 2 to 2.6 times faster than a naive parallel priority queue implementation.
VMV 2022: Frontmatter
(The Eurographics Association, 2022) Bender, Jan; Botsch, Mario; Keim, Daniel A.; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Visually Comparing Rendering Performance from Multiple Perspectives
(The Eurographics Association, 2022) Tarner, Hagen; Bruder, Valentin; Frey, Steffen; Ertl, Thomas; Beck, Fabian; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Evaluation of rendering performance is crucial when selecting or developing algorithms, but challenging as performance can largely differ across a set of selected scenarios. Despite this, performance metrics are often reported and compared in a highly aggregated way. In this paper we suggest a more fine-grained approach for the evaluation of rendering performance, taking into account multiple perspectives on the scenario: camera position and orientation along different paths, rendering algorithms, image resolution, and hardware. The approach comprises a visual analysis system that shows and contrasts the data from these perspectives. The users can explore combinations of perspectives and gain insight into the performance characteristics of several rendering algorithms. A stylized representation of the camera path provides a base layout for arranging the multivariate performance data as radar charts, each comparing the same set of rendering algorithms while linking the performance data with the rendered images. To showcase our approach, we analyze two types of scientific visualization benchmarks.
Visualizing the Movement of Space-Defining Rotatable Elements in Architecture
(The Eurographics Association, 2022) Ehgartner, Ayumi; Hemmerling, Julia; Mosayebi, Elli; Günther, Tobias; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Space-defining rotatable architectural elements enable inhabitants to reshape the living space to their needs. In a field study, a prototype home was built that includes various multifunctional elements such as a rotatable wall, closet and lamps. Over the course of multiple months, multivariate time-series data was collected using sensors placed in the prototype home. To visualize frequent element constellations and to compare constellations across different user groups and periods of time, we developed a visualization system that embeds rotation distributions on the floor plan. Based on the visualization, we report on observations made by the researchers from the field of architecture and sociology.
Real-Time Caustics Using Cascaded Image-Space Photon Tracing
(The Eurographics Association, 2022) Meenrattanakorn, Krittin; Lambers, Martin; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Caustics are formed when transparent or specular materials focus many light rays onto confined areas. Such effects are among the most challenging optical effects to render. Typical path tracing algorithms need many light ray samples to form realistic caustic highlights and/or clever strategies to sample the most relevant light paths. This paper presents an approach for rendering caustics from one specular bounce at an interactive performance by using cascaded image-space photon tracing based on both G-buffer (camera perspective) and Reflective Shadow Maps (light source perspective). A denoiser is applied to remove Monte Carlo noise before applying the caustics texture. Our results demonstrate caustics with more realistic and precise details compared to state-of-the-art Caustics Mapping, at real-time frame rates even on moderately powerful GPUs.
An Overview of Techniques for Egocentric Black Hole Visualization and Their Suitability for Planetarium Applications
(The Eurographics Association, 2022) Hissbach, Anny-Marleen; Dick, Christian; Lawonn, Kai; Bender, Jan; Botsch, Mario; Keim, Daniel A.
The visualization of black holes is used in science communication to educate people about our universe and concepts of general relativity. Recent visualizations aim to depict black holes in realtime, overcoming the challenge of efficient general relativistic ray tracing. In this state-of-the-art report, we provide the first overview of existing works about egocentric black hole visualization that generate images targeted at general audiences. We focus on Schwarzschild and Kerr black holes and discuss current methods to depict the distortion of background panoramas, point-shaped stars, nearby objects, and accretion disks. Approaches to realtime visualizations are highlighted. Furthermore, we present the implementation of a state-of-the-art black hole visualization in the planetarium software Uniview.
Alignment and Reassembly of Broken Specimens for Creep Ductility Measurements
(The Eurographics Association, 2022) Knauthe, Volker; Kraus, Maurice; Buelow, Max von; Wirth, Tristan; Rak, Arne; Merth, Laurenz; Erbe, Alexander; Kontermann, Christian; Guthe, Stefan; Kuijper, Arjan; Fellner, Dieter W.; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Designing new types of heat-resistant steel components is an important and active research field in material science. It requires detailed knowledge of the inherent steel properties, especially concerning their creep ductility. Highly precise automatic stateof- the-art approaches for such measurements are very expensive and often times invasive. The alternative requires manual work from specialists and is time consuming and unrobust. In this paper, we present a novel approach that uses a photometric scanning system for capturing the geometry of steel specimens, making further measurement extractions possible. In our proposed system, we apply calibration for pan angles that occur during capturing and a robust reassembly for matching two broken specimen pieces to extract the specimen's geometry. We compare our results against µCT scans and found that it deviates by 0.057mm on average distributed over the whole specimen for a small amount of 36 captured images. Additionally, comparisons to manually measured values indicate that our system leads to more robust measurements.
Neural Adaptive Scene Tracing (NAScenT)
(The Eurographics Association, 2022) Li, Rui; Rückert, Darius; Wang, Yuanhao; Idoughi, Ramzi; Heidrich, Wolfgang; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost. While the most recent generation of such methods has made progress on the rendering (inference) times, very little progress has been made on improving the reconstruction (training) times. In this work we present Neural Adaptive Scene Tracing (NAScenT ), that directly trains a hybrid explicit-implicit neural representation. NAScenT uses a hierarchical octree representation with one neural network per leaf node and combines this representation with a two-stage sampling process that concentrates ray samples where they matter most - near object surfaces. As a result, NAScenT is capable of reconstructing challenging scenes including both large, sparsely populated volumes like UAV captured outdoor environments, as well as small scenes with high geometric complexity. NAScenT outperforms existing neural rendering approaches in terms of both quality and training time.
Honeycomb Plots: Visual Enhancements for Hexagonal Maps
(The Eurographics Association, 2022) Trautner, Thomas; Sbardellati, Maximilian; Stoppel, Sergej; Bruckner, Stefan; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.
Visualizing Optimizers using Chebyshev Proxies and Fatou Sets
(The Eurographics Association, 2022) Winchenbach, Rene; Thuerey, Nils; Bender, Jan; Botsch, Mario; Keim, Daniel A.
With recent advances in optimization many different optimization approaches have been proposed, especially regarding the optimization of weights for neural networks. However, comparing these approaches in a visually succinct and intuitive manner is difficult to do, especially without relying on simplified toy examples that may not be representative. In this paper, we present a visualization toolkit using a modified variant of Fatou sets of functions in the complex domain to directly visualize the convergence behavior of an optimizer across a large range of input values. Furthermore, we propose an approach of generating test functions based on polynomial Chebyshev proxies, with polynomial degrees up to 11217, and a modification of these proxies to yield functions that are strictly positive with known global minima, i.e., roots. Our proposed toolkit is provided as a cross platform open source framework in C++ using OpenMP for parallelization. Finally, for menomorphic functions the process generates visually interesting fractals, which might also be interesting from an artistic standpoint.
Semi-Automatic Particle Tracking for and Visualization of Particle Detector Data
(The Eurographics Association, 2022) Eschbach, Robin; Messerschmidt, Kai; Keidel, Ralf; Wiebel, Alexander; Bender, Jan; Botsch, Mario; Keim, Daniel A.
In high energy physics, tracking particles in point-based particle detector data is important to reconstruct the particles' trajectories. As the numbers of particles can be in the hundreds or over a thousand, automatic algorithmic tracking is usually preferred over manual tracking. However, in some cases, manual tracking is needed as a baseline to assess the quality of the algorithmic tracking. Tracking particle locations manually is time-consuming and challenging when dealing with thousands of particles in the same data frame. In this paper, we describe Semi-Automatic Particle Tracking (SAPT), a collection of methods that aid manual particle tracking and visualization. These methods aim to make related particle hits easier to recognize by stretching the data and hiding likely irrelevant hits based on an angle criterion. They also help with finding the most likely track among a set of intuitively selected detector hits. These methods, together with a prediction of the most probable continuation of a track that can simply be accepted by the human user, accelerate the manual tracking process tremendously. We demonstrate the usefulness and efficiency of our methods by applying them to simulation data of a detector for proton computed tomography (pCT).
Astray: A Performance-Portable Geodesic Ray Tracer
(The Eurographics Association, 2022) Demiralp, Ali Can; Krüger, Marcel; Chao, Chu; Kuhlen, Torsten W.; Gerrits, Tim; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Geodesic ray tracing is the numerical method to compute the motion of matter and radiation in spacetime. It enables visualization of the geometry of spacetime and is an important tool to study the gravitational fields in the presence of astrophysical phenomena such as black holes. Although the method is largely established, solving the geodesic equation remains a computationally demanding task. In this work, we present Astray; a high-performance geodesic ray tracing library capable of running on a single or a cluster of computers equipped with compute or graphics processing units. The library is able to visualize any spacetime given its metric tensor and contains optimized implementations of a wide range of spacetimes, including commonly studied ones such as Schwarzschild and Kerr. The performance of the library is evaluated on standard consumer hardware as well as a compute cluster through strong and weak scaling benchmarks. The results indicate that the system is capable of reaching interactive frame rates with increasing use of high-performance computing resources. We further introduce a user interface capable of remote rendering on a cluster for interactive visualization of spacetimes.
HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow
(The Eurographics Association, 2022) Wang, Jiayi; Luvizon, Diogo; Mueller, Franziska; Bernard, Florian; Kortylewski, Adam; Casas, Dan; Theobalt, Christian; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Reconstructing two-hand interactions from a single image is a challenging problem due to ambiguities that stem from projective geometry and heavy occlusions. Existing methods are designed to estimate only a single pose, despite the fact that there exist other valid reconstructions that fit the image evidence equally well. In this paper we propose to address this issue by explicitly modeling the distribution of plausible reconstructions in a conditional normalizing flow framework. This allows us to directly supervise the posterior distribution through a novel determinant magnitude regularization, which is key to varied 3D hand pose samples that project well into the input image. We also demonstrate that metrics commonly used to assess reconstruction quality are insufficient to evaluate pose predictions under such severe ambiguity. To address this, we release the first dataset with multiple plausible annotations per image called MultiHands. The additional annotations enable us to evaluate the estimated distribution using the maximum mean discrepancy metric. Through this, we demonstrate the quality of our probabilistic reconstruction and show that explicit ambiguity modeling is better-suited for this challenging problem.
Efficient High-Quality Rendering of Ribbons and Twisted Lines
(The Eurographics Association, 2022) Neuhauser, Christoph; Wang, Junpeng; Kern, Michael; Westermann, Rüdiger; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Flat twisting ribbons are often used for visualizing twists along lines in 3D space. Flat ribbons can disappear when looking at them under oblique angles, and they introduce flickering due to aliasing during animations. We demonstrate that this limitation can be overcome by procedurally rendering generalized cylinders with elliptic profiles. By adjusting the length of the cylinder's semi-minor axis, the ribbon thickness can be controlled so that it always remains visible. The proposed rendering approach further enables the visualization of twists via the projection of a line spiralling around the cylinder's center line. In contrast to texture mapping, this keeps the line width fixed, regardless of the strength of the twist, and provides efficient control over the spiralling frequency and coloring between the twisting lines. The proposed rendering approach can be performed efficiently on recent GPUs by exploiting programmable pulling, mesh shaders and hardware-accelerated ray tracing.
Interactive Segmentation of Textured Point Clouds
(The Eurographics Association, 2022) Schmitz, Patric; Suder, Sebastian; Schuster, Kersten; Kobbelt, Leif; Bender, Jan; Botsch, Mario; Keim, Daniel A.
We present a method for the interactive segmentation of textured 3D point clouds. The problem is formulated as a minimum graph cut on a k-nearest neighbor graph and leverages the rich information contained in high-resolution photographs as the discriminative feature. We demonstrate that the achievable segmentation accuracy is significantly improved compared to using an average color per point as in prior work. The method is designed to work efficiently on large datasets and yields results at interactive rates. This way, an interactive workflow can be realized in an immersive virtual environment, which supports the segmentation task by improved depth perception and the use of tracked 3D input devices. Our method enables to create high-quality segmentations of textured point clouds fast and conveniently.
A Moving Least Squares Material Point Method for Varied Porous Material Interactions and Non-sticky Coupling of Phases
(The Eurographics Association, 2022) Nilles, Alexander Maximilian; Müller, Stefan; Bender, Jan; Botsch, Mario; Keim, Daniel A.
The Material Point Method (MPM) has become very popular in computer graphics due to its capability to handle a variety of materials and ease of coupling for multi-material simulations. However, MPM suffers from numerical stickyness, which is especially apparent in fluid-solid coupling. Furthermore, the free-flowing nature of fluids can cause issues for simulating immiscible fluids, leading to improper separation of phases especially at lower resolutions. Furthermore, some MPM formulations suffer from unwanted dissipation, noise or instability. Our MPM framework solves the latter using the Moving Least Squares MPM, while the former is addressed by coupling two grids only on contact, with an optional friction term for tangential coupling. This is further enhanced with a buoyancy penalty force that can achieve clean separation of immiscible fluids even at low resolutions. We combine this with a method for porous solids which we generalize in order to allow for highly varied material interactions.
Local Attention Guided Joint Depth Upsampling
(The Eurographics Association, 2022) Mallick, Arijit; Engelhardt, Andreas; Braun, Raphael; Lensch, Hendrik P. A.; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Image super resolution is a classical computer vision problem. A branch of super resolution tasks deals with guided depth super resolution as objective. Here, the goal is to accurately upsample a given low resolution depth map with the help of features aggregated from the high resolution color image of that particular scene. Recently, the development of transformers has improved performance for general image processing tasks credited to self-attention. Unlike previous methods for guided joint depth upsampling which rely mostly on CNNs, we efficiently compute self-attention with the help of local image attention which avoids the quadratic growth typically found in self-attention layers. Our work combines CNNs and transformers to analyze the two input modalities and employs a cross-modal fusion network in order to predict both a weighted per-pixel filter kernel and a residual for the depth estimation. To further enhance the final output, we integrate a differentiable and a trainable deep guided filtering network which provides an additional depth prior. An ablation study and empirical trials demonstrate the importance of each proposed module. Our method shows comparable as well as state-of-the-art performance on the guided depth upsampling task.

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