Eurographics Digital Library
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Recent Submissions
Proximity-Based Point Cloud Reconstruction
(2025-02-13) Marin, Diana
Extrapolating information from incomplete data is a key human skill, enabling us to infer patterns and make predictions from limited observations. A prime example is our ability to perceive coherent shapes from seemingly random point sets, a key aspect of cognition. However, data reconstruction becomes challenging when no predefined rules exist, as it is unclear how to connect the data or infer patterns. In computer graphics, a major goal is to replicate this human ability by developing algorithms that can accurately reconstruct original structures or extract meaningful information from raw, disconnected data. The contributions of this thesis deal with point cloud reconstruction, leveraging proximity-based methods, with a particular focus on a specific proximity-encoding data structure - the spheres-of-influence graph (SIG). We discuss curve reconstruction, where we automate the game of connecting the dots to create contours, providing theoretical guarantees for our method. We obtain the best results compared to similar methods for manifold curves. We extend our curve reconstruction to manifolds, overcoming the challenges of moving to different domains, and extending our theoretical guarantees. We are able to reconstruct curves from sparser inputs compared to the state-of-the-art, and we explore various settings in which these curves can live. We investigate the properties of the SIG as a parameter-free proximity encoding structure of three-dimensional point clouds. We introduce new spatial bounds for the SIG neighbors as a theoretical contribution. We analyze how close the encoding is to the ground truth surface compared to the commonly used kNN graphs, and we evaluate our performance in the context of normal estimation as an application. Lastly, we introduce SING – a stability-incorporated neighborhood graph, a useful tool with various applications, such as clustering, and with a strong theoretical background in topological data analysis.
Efficient and Accurate Optimization in Inverse Rendering and Computer Graphics
(2025-05-09) Fischer, Michael
Efficient and accurate representation of graphic assets, a long-standing task in the graphics community, has achieved new heights with the advent of learning-based methods by representing visual appearance as neural networks. Surprisingly, such visual appearance networks are often trained from scratch – an expensive operation that ignores potentially helpful information from previous training runs. This thesis therefore introduces Metappearance, an algorithm which optimizes over optimization itself and enables orders of magnitude faster training times at indistinguishable visual quality while retaining the network’s adaptability to new, unseen data. Moreover, even a fully converged network, albeit a smooth function, does not guarantee optimization success when employed in an inverse rendering scenario. In fact, it is common for inverse rendering to exhibit plateaus – regions of zero gradient – in the cost function, which hinder gradient-based optimization from converging. Chapter 4 therefore introduces an algorithm that smooths out such plateaus by convolving the rendering equation with a Gaussian blur kernel and thus successfully optimizes scenarios where other, rigid methods fail to converge. Finally, while recent research has shown that specialized treatment of the renderer’s internals can yield correct, usable gradients, there is no unified, systematic way of differentiating through arbitrary, black-box graphics pipelines. We therefore introduce the concept of neural surrogates, which allow differentiating through arbitrary forward models without requiring access to, or making any assumptions on, the rendering pipeline’s internals. We show that our neural surrogate losses can successfully optimize various graphics tasks and scale well to high dimensions, a domain where traditional derivative-free optimizers often do not converge.
Deployable, Modular, and Reconfigurable: Computational Design of Umbrella Meshes
(EPFL, 2025-07-24) Kusupati, Uday
Deployable structures that transform from a planar assembly-friendly compact state to an expansive freeform surface state have diverse applications in robotics, medical devices, temporary installations, and architecture. Umbrella Meshes are a new class of volumetric deployable structures with extensive shape expression capabilities compared to existing plane-to-surface deployables. They are modular, made of Umbrella cells consisting of identical rigid plates and rotational joints connected by elastic beams of varying heights. Deployment is actuated by pushing the cells orthogonal to the plane, rotating the elastic beams from vertical to horizontal configurations, thus redistributing material from out of the plane into it. In contrast to rigid scissor mechanisms, the beams deform elastically, making the deployed equilibrium bending-active. Assembled in a stress-free planar configuration, an Umbrella Mesh can be programmed to deploy to a desired target shape by virtue of the optimized heights of the constituent cells. The rich design space facilitates programming a large range of target shapes, controlling the structural stiffness, and encoding extrinsic curvature.
This thesis contributes a comprehensive computational framework for the design and optimization of Umbrella Meshes. To facilitate design exploration of the deployed structure, we develop a physics-based simulation modeling the deployment process under actuation forces. We abstract the deployment transformation of an umbrella mesh using conformal geometry, providing intuitive design initializations for a specific target surface. Our inverse design algorithm leverages the simulation pipeline and numerical optimization to iteratively refine a design to approximate a target surface while minimizing the elastic energy and actuation forces involved. We build optimized physical prototypes through digital fabrication and validate our computational pipeline.
The inverse design framework exemplifies a design-driven approach to fabricating optimized physical structures. The latter half of this thesis focuses on fabrication-driven design. We develop a computational framework to rationalize bending-active structures into a sparse kit of parts, allowing cost-effective fabrication. Our method can either find an optimal kit of parts for multiple input designs or rationalize existing designs to use a pre-fabricated kit of parts. To tackle the non-trivial coupling of components in bending-active systems, we propose a relaxed continuous formulation of the combinatorial problem of grouping components to a sparse part set, allowing us to incorporate physics-based simulation that tracks multiple bending-active equilibria. We demonstrate our approach on Umbrella Meshes, C-shells, and orthogonal gridshells.
The thesis culminates with Reconfigurable Umbrella Meshes (RUMs) consisting of identical reconfigurable cells. Each reconfigurable cell can assume the form of a continuous range of parts, thus combining the benefits of pre-fabrication and precisely inverse-designed heights. Assembled from these identical mass-producible cells, the same RUM can deploy into several shapes over multiple deployment cycles. Our inverse design enables precise reconfiguration of the compact state and opens up multiple research avenues for high-fidelity shape morphing control with applications in soft robotics and sustainable architecture.
Namako 2: Unity Plugin for Stress Visualization, Deformation Prediction and Haptic Rendering of Soft Objects
(The Eurographics Association, 2025) Sase, Kazuya; Chen, Xiaoshuai; Tsujita, Teppei; Konno, Atsushi; Garro, Valeria; Young, Gareth; Elwardy, Majed
Developing real-time deformable-body simulation with physical accuracy inside game engines remains challenging due to limited official support and integration complexity. To address this, we present Namako 2, an extended version of our previous Unity plugin that enables real-time finite element simulation with automatic mesh generation, 3D model embedding, robust contact handling, and built-in stress visualization. We demonstrate several example scenes of surgical simulation using Namako 2. The plugin is publicly available at https://github.com/sasekazu/Namako
Realistic Impact Method with Force Feedback in VR Space Using a Lower Limb Exoskeleton Device
(The Eurographics Association, 2025) Kanta, Ogura; Shimizu, T.; Sugino, T.; Sawahashi, R.; Nishihama, R.; Nakamura, T.; Garro, Valeria; Young, Gareth; Elwardy, Majed
In recent years, the widespread adoption of Head-Mounted Displays (HMDs) has made Virtual Reality (VR) experiences more accessible, leading to the development of force-feedback devices to enhance immersion. Force-feedback devices enhance VR immersion but presenting large, realistic forces, such as for a soccer kick, is challenging due to safety constraints. Therefore, designing the presented reaction force is crucial to enhance realism and mitigate perceptual discrepancies. In this study, we used a lower limb-mounted force-feedback device to investigate how different torque waveforms presented during a ball kick affect user perception.