DSpace Repository :: Browsing by Author "Pajarola, Renato"

Browsing by Author "Pajarola, Renato"

Now showing 1 - 14 of 14

DanceMoves: A Visual Analytics Tool for Dance Movement Analysis
(The Eurographics Association, 2021) Arpatzoglou, Vasiliki; Kardara, Artemis; Diehl, Alexandra; Flueckiger, Barbara; Helmer, Sven; Pajarola, Renato; Agus, Marco and Garth, Christoph and Kerren, Andreas
Analyzing body movement as a means of expression is of interest in diverse areas, such as dance, sports, films, as well as anthropology or archaeology. In particular, in choreography, body movements are at the core of artistic expression. Dance moves are composed of spatial and temporal structures that are difficult to address without interactive visual data analysis tools. We present a visual analytics solution that allows the user to get an overview of, compare, and visually search dance move features in video archives. With the help of similarity measures, a user can compare dance moves and assess dance poses. We illustrate our approach through three use cases and an analysis of the performance of our similarity measures. The expert feedback and the experimental results show that 75% to 80% of dance moves can correctly be categorized. Domain experts recognize great potential in this standardized analysis. Comparative and motion analysis allows them to get detailed insights into temporal and spatial development of motion patterns and poses.
Enhanced Reconstruction of Architectural Wall Surfaces for 3D Building Models
(The Eurographics Association, 2019) Michailidis, Georgios-Tsampikos; Pajarola, Renato; Fusiello, Andrea and Bimber, Oliver
The reconstruction of architectural structures from 3D building models is a challenging task and a lot of research has been done in recent years. However, most of this work is focused mainly on reconstructing accurately the architectural shape of interiors rather than the fine architectural details, such as the wall elements (e.g. windows and doors). We focus specifically on this problem and propose a method that extends current solutions to reconstruct accurately severely occluded wall surfaces.
Hornero: Thunderstorms Characterization using Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Diehl, Alexandra; Pelorosso, Rodrigo; Ruiz, Juan; Pajarola, Renato; Gröller, M. Eduard; Bruckner, Stefan; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short-term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision-making. We developed a visual analytics tool for the detection of hazardous thunderstorms and their characterization, using a visual design centered on a reformulated expert task workflow that includes visual features to overview storms and quickly identify high-impact weather events, a novel storm graph visualization to inspect and analyze the storm structure, as well as a set of interactive views for efficient identification of similar storm cells (known as analogs) in historical data and their use for nowcasting. Our tool was designed with and evaluated by meteorologists and expert forecasters working in short-term operational weather forecasting of severe weather events. Results show that our solution suits the forecasters' workflow. Our visual design is expressive, easy to use, and effective for prompt analysis and quick decision-making in the context of short-range operational weather forecasting.
Large‐Scale Pixel‐Precise Deferred Vector Maps
(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Thöny, Matthias; Billeter, Markus; Pajarola, Renato; Chen, Min and Benes, Bedrich
Rendering vector maps is a key challenge for high‐quality geographic visualization systems. In this paper, we present a novel approach to visualize vector maps over detailed terrain models in a pixel‐precise way. Our method proposes a deferred line rendering technique to display vector maps directly in a screen‐space shading stage over the 3D terrain visualization. Due to the absence of traditional geometric polygonal rendering, our algorithm is able to outperform conventional vector map rendering algorithms for geographic information systems, and supports advanced line anti‐aliasing as well as slope distortion correction. Furthermore, our deferred line rendering enables interactively customizable advanced vector styling methods as well as a tool for interactive pixel‐based editing operations.Rendering vector maps is a key challenge for high‐quality geographic visualization systems. In this paper, we present a novel approach to visualize vector maps over detailed terrain models in a pixel‐precise way. Our method proposes a deferred line rendering technique to display vector maps directly in a screen‐space shading stage over the 3D terrain visualization. Due to the absence of traditional geometric polygonal rendering, our algorithm is able to outperform conventional vector map rendering algorithms for geographic information systems, and supports advanced line anti‐aliasing as well as slope distortion correction. Furthermore, our deferred line rendering enables interactively customizable advanced vector styling methods as well as a tool for interactive pixel‐based editing operations.
LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Amiraghdam, Alireza; Diehl, Alexandra; Pajarola, Renato; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line simplification and real-time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas-Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view-dependent error metric supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti-aliasing techniques to our line rendering.
LOOPS: LOcally Optimized Polygon Simplification
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Amiraghdam, Alireza; Diehl, Alexandra; Pajarola, Renato; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Displaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed poses several challenges to the efficient and adaptive display of polygons in interactive geographic visualization applications. For linear vector data, only recently a GPU-based level-of-detail (LOD) polyline simplification and rendering approach has been presented which can perform locally-adaptive LOD visualization of large-scale line datasets interactively. However, locally optimized LOD simplification and interactive display of large-scale polygon data, consisting of filled vector line loops, remains still a challenge, specifically in 3D geographic visualizations where varying LOD over a scene is necessary. Our solution to this challenge is a novel technique for locally-optimized simplification and visualization of 2D polygons over a 3D terrain which features a parallelized point-inside-polygon testing mechanism. Our approach is capable of employing any simplification algorithm that sequentially removes vertices such as Douglas-Peucker and Wang-Müller. Moreover, we generalized our technique to also visualizing polylines in order to have a unified method for displaying both data types. The results and performance analysis show that our new algorithm can handle large datasets containing polygons composed of millions of segments in real time, and has a lower memory demand and higher performance in comparison to prior methods of line simplification and visualization.
Multi-Display Ray Tracing Framework
(The Eurographics Association, 2023) Romero Calla, Luciano Arnaldo; Mohanto, Bipul; Pajarola, Renato; Staadt, Oliver; Singh, Gurprit; Chu, Mengyu (Rachel)
We present a framework that will provide a highly efficient and scalable multi-display ray-tracing based rendering system capable of utilizing multiple GPU devices to produce high-quality images. Our system integrates advanced technologies, including MPI, CUDA, CUDA IPC, OptiX 7.6, and C++, resulting in a cutting-edge solution for interactive rendering.
A Robust Feature-aware Sparse Mesh Representation
(The Eurographics Association, 2020) Fuentes Perez, Lizeth Joseline; Romero Calla, Luciano Arnaldo; Montenegro, Anselmo Antunes; Mura, Claudio; Pajarola, Renato; Lee, Sung-hee and Zollmann, Stefanie and Okabe, Makoto and Wuensche, Burkhard
The sparse representation of signals defined on Euclidean domains has been successfully applied in signal processing. Bringing the power of sparse representations to non-regular domains is still a challenge, but promising approaches have started emerging recently. In this paper, we investigate the problem of sparsely representing discrete surfaces and propose a new representation that is capable of providing tools for solving different geometry processing problems. The sparse discrete surface representation is obtained by combining innovative approaches into an integrated method. First, to deal with irregular mesh domains, we devised a new way to subdivide discrete meshes into a set of patches using a feature-aware seed sampling. Second, we achieve good surface approximation with over-fitting control by combining the power of a continuous global dictionary representation with a modified Orthogonal Marching Pursuit. The discrete surface approximation results produced were able to preserve the shape features while being robust to over-fitting. Our results show that the method is quite promising for applications like surface re-sampling and mesh compression.
SenVis: Interactive Tensor-based Sensitivity Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Yang, Haiyan; Ballester-Ripoll, Rafael; Pajarola, Renato; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Sobol's method is one of the most powerful and widely used frameworks for global sensitivity analysis, and it maps every possible combination of input variables to an associated Sobol index. However, these indices are often challenging to analyze in depth, due in part to the lack of suitable, flexible enough, and fast-to-query data access structures as well as visualization techniques. We propose a visualization tool that leverages tensor decomposition, a compressed data format that can quickly and approximately answer sophisticated queries over exponential-sized sets of Sobol indices. This way, we are able to capture the complete global sensitivity information of high-dimensional scalar models. Our application is based on a three-stage visualization, to which variables to be analyzed can be added or removed interactively. It includes a novel hourglass-like diagram presenting the relative importance for any single variable or combination of input variables with respect to any composition of the rest of the input variables. We showcase our visualization with a range of example models, whereby we demonstrate the high expressive power and analytical capability made possible with the proposed method.
State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Pintore, Giovanni; Mura, Claudio; Ganovelli, Fabio; Fuentes-Perez, Lizeth Joseline; Pajarola, Renato; Gobbetti, Enrico; Mantiuk, Rafal and Sundstedt, Veronica
Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.
VIAN: A Visual Annotation Tool for Film Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Halter, Gaudenz; Ballester-Ripoll, Rafael; Flueckiger, Barbara; Pajarola, Renato; Gleicher, Michael and Viola, Ivan and Leitte, Heike
While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert-assessed labeling, curation, visualization and classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground-background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color-oriented analysis pipeline.
VisGuided: A Community-driven Approach for Education in Visualization
(The Eurographics Association, 2021) Diehl, Alexandra; Firat, Elif E.; Torsney-Weir, Thomas; Abdul-Rahman, Alfie; Bach, Benjamin; Laramee, Robert; Pajarola, Renato; Chen, Min; Sousa Santos, Beatriz and Domik, Gitta
We propose a novel educational approach for teaching visualization, using a community-driven and participatory methodology that extends the traditional course boundaries from the classroom to the broader visualization community.We use a visualization community project, VisGuides, as the main platform to support our educational approach. We evaluate our new methodology by means of three use cases from two different universities. Our contributions include the proposed methodology, the discussion on the outcome of the use cases, the benefits and limitations of our current approach, and a reflection on the open problems and noteworthy gaps to improve the current pedagogical techniques to teach visualization and promote critical thinking. Our findings show extensive benefits from the use of our approach in terms of the number of transferable skills to students, educational resources for educators, and additional feedback for research opportunities to the visualization community.
Visual-assisted Outlier Preservation for Scatterplot Sampling
(The Eurographics Association, 2023) Yang, Haiyan; Pajarola, Renato; Guthe, Michael; Grosch, Thorsten
Scatterplot sampling has long been an efficient and effective way to resolve the overplotting issues commonly occurring in large-scale scatterplot visualization applications. However, it is challenging to preserve the existence of low-density points or outliers after sampling for a sub-sampling algorithm if, at the same time, faithfully representing the relative data densities is of importance. In this work, we propose to address this issue in a visual-assisted manner. While the whole dataset is sub-sampled, the density of the outliers is modeled and visually integrated into the final scatterplot together with the sub-sampled point data. We showcase the effectiveness of our proposed method in various cases and user studies.
Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Mura, Claudio; Pajarola, Renato; Schindler, Konrad; Mitra, Niloy; Mitra, Niloy and Viola, Ivan
Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide a radical alternative to such data-intensive procedures by presentingWalk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data.

Browsing by Author "Pajarola, Renato"

Results Per Page

Sort Options