EG 2023 - Posters

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https://diglib.eg.org/handle/10.2312/2633299

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Posters

Creating 3D Asset Variations Through 2D Style Transfer and Generated Texture Maps

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Ivan Nikolov

Radiance-Based Blender Add-On for Physically Accurate Rendering of Cultural Heritage

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Míriam Méndez, Imanol Munoz-Pandiella, and Carlos Andujar

Parameter-Free and Improved Connectivity for Point Clouds

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Diana Marin, Stefan Ohrhallinger, and Michael Wimmer

Automatic Molecular Tour Creation: a Study

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Vincent Larroque, Maxime Maria, Stephane Mérillou, and Matthieu Montes

Multi-Display Ray Tracing Framework

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Luciano Arnaldo Romero Calla, Bipul Mohanto, Renato Pajarola, and Oliver Staadt

Synthetic Dataset for Panic Detection in Human Crowded Scenes

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Javier Calle, Peter Leskovsky, Jorge Garcia, and Marti Sanchez

DropSPH: ISPH Simulation of Droplet Interactions with a Solid Surface

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Hossein Keshtkar and Nadine Aburumman

Non-Separable Multi-Dimensional Network Flows for Visual Computing

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Viktoria Ehm, Daniel Cremers, and Florian Bernard

Sparse Ferguson-Hermite Signed Distance Fields

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Róbert Bán and Gábor Valasek

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Now showing 1 - 10 of 10

Creating 3D Asset Variations Through 2D Style Transfer and Generated Texture Maps
(The Eurographics Association, 2023) Nikolov, Ivan; Singh, Gurprit; Chu, Mengyu (Rachel)
Generating 3D object variations through style transfer models applied to their textures is an easy way for creating content for games and XR applications. Most workflows focus on either generating albedo textures only without changing the underlying surface and not touching the underlying surface or transforming the 3D object directly, which is computationally and resourceheavy. In this paper, we present an initial exploration of an in-between solution that aims to combine the style transfer of albedo textures with the generation of additional maps, such as normal, displacement, and roughness. The results show that the pipeline can generate variations based on different styles, which would enable the addition of smaller 3D-style surface features to objects without transforming their meshes. The project code and generated textures will be available at https: //github.com/IvanNik17/3D-Assets-From-2D-Style-Transfer.
Radiance-Based Blender Add-On for Physically Accurate Rendering of Cultural Heritage
(The Eurographics Association, 2023) Méndez, Míriam; Munoz-Pandiella, Imanol; Andujar, Carlos; Singh, Gurprit; Chu, Mengyu (Rachel)
Despite the Cultural Heritage and Computer Graphics communities are increasingly joining forces to strengthen their collaboration, the study of how light interacts with monuments (e.g. weathering the surfaces or affecting the visitors' experience) is still an open problem in cultural heritage. A significant limitation is the lack of easy-to-use, open-source, physically-accurate tools allowing cultural heritage experts to perform lighting simulations on the increasing collection of 3D reconstructions. In this work, we present an open-source Blender add-on to facilitate such simulations. The add-on allows art historians to configure the properties (materials, lights, and camera) of the simulation, and uses as rendering back-end the Radiance software, a validated physically accurate light simulation tool. Our tool lowers the entry barrier for the use of a highly accurate but rather complex (command-based) tool for lighting studies in cultural heritage monuments.
Parameter-Free and Improved Connectivity for Point Clouds
(The Eurographics Association, 2023) Marin, Diana; Ohrhallinger, Stefan; Wimmer, Michael; Singh, Gurprit; Chu, Mengyu (Rachel)
Determining connectivity in unstructured point clouds is a long-standing problem that is still not addressed satisfactorily. In this poster, we propose an extension to the proximity graph introduced in [MOW22] to three-dimensional models. We use the spheres-of-influence (SIG) proximity graph restricted to the 3D Delaunay graph to compute connectivity between points. Our approach shows a better encoding of the connectivity in relation to the ground truth than the k-nearest neighborhood (kNN) for a wide range of k values, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., improvements in normal estimation, surface reconstruction, motion planning, simulations, and many more.
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.
Automatic Molecular Tour Creation: a Study
(The Eurographics Association, 2023) Larroque, Vincent; Maria, Maxime; Mérillou, Stephane; Montes, Matthieu; Singh, Gurprit; Chu, Mengyu (Rachel)
Molecular system visualization is a difficult task even for experts as molecules can contain millions of atoms. Our goal is to create a tool to improve the preliminary study of molecules by automatically creating a tour of the interesting viewpoints around them. Since we noticed limited research specific to molecular visualization, we analyzed and adapted methods from the general field. Our preliminary study shows that our molecular tour is able to smoothly present key information automatically.
Synthetic Dataset for Panic Detection in Human Crowded Scenes
(The Eurographics Association, 2023) Calle, Javier; Leskovsky, Peter; Garcia, Jorge; Sanchez, Marti; Singh, Gurprit; Chu, Mengyu (Rachel)
AI is increasingly being used in public protection by using crowd anomaly detection. This is useful for identifying panic events enabling control forces to act faster. A significant challenge in this field is the lack of data for training these algorithms. Recreating panic events with big crowds can be both expensive and hazardous. To address this issue, this paper proposes the creation of a synthetic dataset for crowd panic behaviour. The process involves defining the scenario and setting up the appropriate CCTV cameras. Many scenarios are prepared, including variations in weather conditions. Next is the scene population with pedestrians and vehicles, with different crowd sizes and vehicle trajectories. To recreate panic, the behaviour of each person is programmed. The final videos show normality situations before the panic events start. Finally, we achieved 1717 simulations.
Non-Separable Multi-Dimensional Network Flows for Visual Computing
(The Eurographics Association, 2023) Ehm, Viktoria; Cremers, Daniel; Bernard, Florian; Singh, Gurprit; Chu, Mengyu (Rachel)
Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to limitations in terms of expressiveness. For example, oftentimes highdimensional data (e.g. feature descriptors) are mapped to a single scalar value (e.g. the similarity between two feature descriptors). To overcome this limitation, we propose a novel formalism for non-separable multi-dimensional network flows. By doing so, we enable an automatic and adaptive feature selection strategy - since the flow is defined on a per-dimension basis, the maximizing flow automatically chooses the best matching feature dimensions. As a proof of concept, we apply our formalism to the multi-object tracking problem and demonstrate that our approach outperforms scalar formulations on the MOT16 benchmark in terms of robustness to noise.
DropSPH: ISPH Simulation of Droplet Interactions with a Solid Surface
(The Eurographics Association, 2023) Keshtkar, Hossein; Aburumman, Nadine; Singh, Gurprit; Chu, Mengyu (Rachel)
We present a physically-based model to simulate droplet behaviours when impacted on a solid surface. Our model creates the coalescence, spreading, and break-up deformations that occur when a liquid droplet collides with a solid surface. We model the attraction-repulsion forces within an improved free surface Incompressible Smoothed Particle Hydrodynamics (ISPH) framework that includes contact force and cohesion force between particles. The results show that our model is effective in simulating several small-scale liquid phenomena.
EUROGRAPHICS 2023: Posters Frontmatter
(Eurographics Association, 2023) Singh, Gurprit; Chu, Mengyu (Rachel); Singh, Gurprit; Chu, Mengyu (Rachel)
Sparse Ferguson-Hermite Signed Distance Fields
(The Eurographics Association, 2023) Bán, Róbert; Valasek, Gábor; Singh, Gurprit; Chu, Mengyu (Rachel)
We investigate Hermite interpolation in the context of discrete signed distance field filtering. Our method uses tricubic Hermite interpolation to generate a C1 continuous approximation to the signed distance function of the input scene. Our representation is kept purely first order by setting the mixed partial derivatives to zero, similarly to how Ferguson constructed bicubic Hermite patches. Our scheme stores four scalars at each sample, the value of the signed distance function and its first three partial derivatives. We optimize storage by only storing voxels that enclose a volume boundary. We show that this provides both a significant reduction in storage and render times compared to a dense grid of Ferguson-Hermite samples. Moreover, our construct requires smaller storage than traditional zero order trilinearly filtered fields of the same visual quality, at the expense of performance.

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