348 Works

Generalizing Discrete Convolutions for Unstructured Point Clouds

Alexandre Boulch
Point clouds are unstructured and unordered data, as opposed to images. Thus, most of machine learning approaches, developed for images, cannot be directly transferred to point clouds. It usually requires data transformation such as voxelization, inducing a possible loss of information. In this paper, we propose a generalization of the discrete convolutional neural networks (CNNs) able to deal with sparse input point cloud. We replace the discrete kernels by continuous ones. The formulation is simple,...

The Human User in Progressive Visual Analytics

Luana Micallef, Hans-Jörg Schulz, Marco Angelini, Michaël Aupetit, Remco Chang, Jörn Kohlhammer, Adam Perer & Giuseppe Santucci
The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from...

A Survey on Sleep Visualizations for Fitness Trackers

Ranjini Aravind, Tanja Blascheck & Petra Isenberg
We contribute the results of an exploratory study and a survey on visualizations for fitness trackers. Fitness trackers are becoming ubiquitous trackers of personal data. They often come with small attached displays that show micro visualizations of data such as heart rate, step counts, sleep duration, or number of floors climbed. Unfortunately, little is known about how wearers of fitness trackers use and perceive these micro visualizations. To collect data on the use of fitness...

Visualizing Transport and Mixing in Particle-based Fluid Flows

Tobias Rapp & Carsten Dachsbacher
To gain insight into large, time-dependent particle-based fluid flows, we visually analyze Lagrangian coherent structures (LCS), a robust skeleton of the underlying particle dynamics. To identify these coherent structures, we build on recent work that efficiently computes the finite-time Lyapunov exponent (FTLE) directly on particle data. We formulate the LCS definitions for particles based on robust approximations for higher-order derivatives of the FTLE. Based on these formulations, we derive a per-particle distance to the closest...

A Visual Analytics Tool for Cohorts in Motion Data

Ali Sheharyar, Alexander Ruh, Dimitar Valkov, Michael Markl, Othmane Bouhali & Lars Linsen
Motion data are curves over time in a 1D, 2D, or 3D space. To analyze sets of curves, machine learning methods can be applied to cluster them and detect outliers. However, often metadata or prior knowledge of the analyst drives the analysis by defining cohorts. Our goal is to provide a flexible system for comparative visual analytics of cohorts in motion data. The analyst interactively defines cohorts by filtering on metadata properties. We, then, apply...

Visual Analysis of Probabilistic Infection Contagion in Hospitals

Marcel Wunderlich, Isabelle Block, Tatiana Von Landesberger, Markus Petzold, Michael Marschollek & Simone Scheithauer
Clinicians and hygienists need to know how an infection of one patient could be transmitted among other patients in the hospital (e.g., to prevent outbreaks). They need to analyze how many and which patients will possibly be infected, how fast the infection could spread, and which contacts are likely to transfer the infections within the hospital. Currently, infection contagion is modeled and visualized for populations only on an aggregate level, without identification and exploration of...

Local Remote Photoplethysmography Signal Analysis for Application in Presentation Attack Detection

Benjamin Kossack, Eric L. Wisotzky, Anna Hilsmann & Peter Eisert
This paper presents a method to analyze and visualize the local blood flow through human skin tissue within the face and neck. The method is based on the local signal characteristics and extracts and analyses the local propagation of blood flow from video recordings. In a first step, the global pulse rate is identified in RGB images using normalized green color channel intensities. We then calculate for an image sequence, a local remote photoplethysmography (rPPG)...

Consistent Filtering of Videos and Dense Light-Fields Without Optic-Flow

Sumit Shekhar, Amir Semmo, Matthias Trapp, Okan Tarhan Tursun, Sebastian Pasewaldt, Karol Myszkowski & Jürgen Döllner
A convenient post-production video processing approach is to apply image filters on a per-frame basis. This allows the flexibility of extending image filters-originally designed for still images-to videos. However, per-image filtering may lead to temporal inconsistencies perceived as unpleasant flickering artifacts, which is also the case for dense light-fields due to angular inconsistencies. In this work, we present a method for consistent filtering of videos and dense light-fields that addresses these problems. Our assumption is...

Polarization Demosaicking for Monochrome and Color Polarization Focal Plane Arrays

Simeng Qiu, Qiang Fu, Congli Wang & Wolfgang Heidrich
Division-of-focal-plane (DoFP) polarization image sensors allow for snapshot imaging of linear polarization effects with inexpensive and straightforward setups. However, conventional interpolation based image reconstruction methods for such sensors produce unreliable and noisy estimates of quantities such as degree of linear polarization (DoLP) or angle of linear polarization (AoLP). In this paper, we propose a polarization demosaicking algorithm by inverting the polarization image formation model for both monochrome and color DoFP cameras. Compared to previous interpolation...

Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs

Johannes Sebastian Mueller-Roemer, André Stork & Dieter W. Fellner
Large sparse matrices with compound entries, i.e., complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation, and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of...

Multi-Level-Memory Structures for Adaptive SPH Simulations

Rene Winchenbach & Andreas Kolb
In this paper we introduce a novel hash map-based sparse data structure for highly adaptive Smoothed Particle Hydrodynamics (SPH) simulations on GPUs. Our multi-level-memory structure is based on stacking multiple independent data structures, which can be created efficiently from the same particle data by utilizing self-similar particle orderings. Furthermore, we propose three neighbor list algorithms that improve performance, or significantly reduce memory requirements, when compared to Verlet-lists for the overall simulation. Overall, our proposed method...

Visual Analytics of Simulation Ensembles for Network Dynamics

Quynh Quang Ngo, Marc-Thorsten Hütt & Lars Linsen
A central question in the field of Network Science is to analyze the role of a given network topology on the dynamical behavior captured by time-varying simulations executed on the network. These dynamical systems are also influenced by global simulation parameters. We present a visual analytics approach that supports the investigation of the impact of the parameter settings, i.e., how parameter choices change the role of network topology on the simulations' dynamics. To answer this...

Clustering Ensembles of 3D Jet-Stream Core Lines

Michael Kern & Rüdiger Westermann
The extraction of a jet-stream core line in a wind field results in many disconnected line segments of arbitrary topology. In an ensemble of wind fields, these structures show high variation, coincide only partly, and almost nowhere agree in all ensemble members. In this paper, we shed light on the use of clustering for visualizing an ensemble of jet-stream core lines. Since classical approaches for clustering 3D line sets fail due to the mentioned properties,...

Cluster-based Analysis of Multi-Parameter Distributions in Cloud Simulation Ensembles

Alexander Kumpf, Josef Stumpfegger & Rüdiger Westermann
The proposed approach enables a comparative visual exploration of multi-parameter distributions in time-varying 3D ensemble simulations. To investigate whether dominant trends in such distributions occur, we consider the simulation elements in each dataset-per ensemble member and time step-as elements in the multi-dimensional parameter space, and use t-SNE to project these elements into 2D space. To find groups of elements with similar parameter values in each time step, the resulting projections are clustered via k-Means. Since...

Learning a Perceptual Quality Metric for Correlation in Scatterplots

Leslie Wöhler, Yuxin Zou, Moritz Mühlhausen, Georgia Albuquerque & Marcus Magnor
Visual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts' expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15,000 pairs of...

Open-Box Training of Kernel Support Vector Machines: Opportunities and Limitations

Mohammad Khatami & Thomas Schultz
Kernel Support Vector Machines (SVMs) are widely used for supervised classification, and have achieved state-of-the-art performance in numerous applications. We aim to further increase their efficacy by allowing a human operator to steer their training process. To this end, we identify several possible strategies for meaningful human intervention in their training, propose a corresponding visual analytics workflow, and implement it in a prototype system. Initial results from two users, on data from three different domains...

Stochastic Convolutional Sparse Coding

Jinhui Xiong, Peter Richtarik & Wolfgang Heidrich
State-of-the-art methods for Convolutional Sparse Coding usually employ Fourier-domain solvers in order to speed up the convolution operators. However, this approach is not without shortcomings. For example, Fourier-domain representations implicitly assume circular boundary conditions and make it hard to fully exploit the sparsity of the problem as well as the small spatial support of the filters. In this work, we propose a novel stochastic spatial-domain solver, in which a randomized subsampling strategy is introduced during...

Reconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Network

Masheal Alghamdi, Qiang Fu, Ali Thabet & Wolfgang Heidrich
High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially-varying modulation mask in the...

Trigonometric Moments for Editable Structured Light Range Finding

Sebastian Werner, Julian Iseringhausen, Clara Callenberg & Matthias Hullin
Structured-light methods remain one of the leading technologies in high quality 3D scanning, specifically for the acquisition of single objects and simple scenes. For more complex scene geometries, however, non-local light transport (e.g. interreflections, sub-surface scattering) comes into play, which leads to errors in the depth estimation. Probing the light transport tensor, which describes the global mapping between illumination and observed intensity under the influence of the scene can help to understand and correct these...

Normal Map Bias Reduction for Many-Lights Multi-View Photometric Stereo

Jiangbin Gan, Philipp Bergen, Thorsten Thormählen, Philip Drescher & Ralf Hagens
In this paper, we improve upon an existing many-lights multi-view photometric stereo approach. Firstly, we show how to detect continuous regions for normal integration, which leads to a fully automatic reconstruction pipeline. Secondly, we compute perpixel light source visibilities using an initial biased reconstruction in order to update the estimated normal map to a solution with reduced bias. Thirdly, to further improve the normal accuracy, we compensate for interreflections of light between surface locations. Our...

Reflection Symmetry in Textured Sewing Patterns

Katja Wolff, Philipp Herholz & Olga Sorkine-Hornung
Recent work in the area of digital fabrication of clothes focuses on repetitive print patterns, specifically the 17 wallpaper groups, and their alignment along garment seams. While adjusting the underlying sewing patterns for maximized fit of wallpapers along seams, past research does not account for global symmetries that underlie almost every sewing pattern due to the symmetry of the human body. We propose an interactive tool to define such symmetries and integrate them into the...

RodMesh: Two-handed 3D Surface Modeling in Virtual Reality

Floor Verhoeven & Olga Sorkine-Hornung
User interfaces for 3D shape modeling in Virtual Reality (VR), unlike basic tasks such as text input and item selection, have been less explored in research so far. Shape modeling in 3D lends itself very well to VR, since the 3D immersion provides the user with richer spatial feedback and depth perception when compared to traditional 2D displays. That said, currently existing 3D modeling applications do not focus on optimizing the modeling interaction techniques for...

VMV 2019: Frontmatter

Hans-Jörg Schulz, Matthias Teschner & Michael Wimmer

Laparoscopic Sigmoidectomy Surgery Training System Using AR Follow-up Experience of Real Human Surgery

Akihiro Terashima, Akinobu Morishima, Kazutaka Obama, Yoshiharu Sakai, Taro Maeda & Hideyuki Ando
In our learning system (OITORE Advance), high learning effect was confirmed by using teaching materials using porcine intestines. However, because the environment inside the abdominal cavity is different between pigs and people, there are scenes with different surgical procedures and surgical skills. Therefore, not all the learned skills are applied in human surgery. Therefore, in this paper we changed teaching materials from pig to human and adjusted the system accordingly.

Do contests improve students skills in Computer Graphics? The case of API8

Jean-Pascal Palus, Farès Belhadj & Jean-Jacques Bourdin
This paper presents a contest designed to improve the skills of students in Computer Graphics. The contest is adapted to the current skills of the students and uses a public graphic library. Students then have to produce a demo, generally a program which presents an animation. The main result presented in this paper is that with an appropriate set of tools, students program interesting demos to participate in the contest and their skills in Computer...

Registration Year

  • 2019
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