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E. Eisemann
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Line art is an illustrative technique with a wide use in education and art. In the context of image abstraction, its potential for increasing memorisation and recognition has been demonstrated, which motivates its use in scientific illustrations. While much work has focused on the conversion of 3D models into a line-art representation, there is a lack of solutions for virtual reality. Applying existing methods for each eye independently turns out to fall short due to cost constraints, distracting artifacts due to inconsistencies, or limitations regarding the input geometry. To address these limitations, we present a contour renderer for virtual reality. It operates in screen space, making it flexible, yet it relies on a local surface approximation combined with a registration error metric for robustness. Inconsistent occluding contours are continuously merged, and lines with no correspondence between both eyes are culled. The method is easy to implement, highly efficient even for high-resolution imagery, and, according to user evaluations, avoids the noticeable artifacts produced by existing work.
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Line art is an illustrative technique with a wide use in education and art. In the context of image abstraction, its potential for increasing memorisation and recognition has been demonstrated, which motivates its use in scientific illustrations. While much work has focused on the conversion of 3D models into a line-art representation, there is a lack of solutions for virtual reality. Applying existing methods for each eye independently turns out to fall short due to cost constraints, distracting artifacts due to inconsistencies, or limitations regarding the input geometry. To address these limitations, we present a contour renderer for virtual reality. It operates in screen space, making it flexible, yet it relies on a local surface approximation combined with a registration error metric for robustness. Inconsistent occluding contours are continuously merged, and lines with no correspondence between both eyes are culled. The method is easy to implement, highly efficient even for high-resolution imagery, and, according to user evaluations, avoids the noticeable artifacts produced by existing work.
Skill-Adaptive Ghost Instructors
Enhancing Retention and Reducing Over-Reliance in VR Piano Learning
Conference paper
(2026)
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Tzu Hsin Hsieh, Cassandra Michelle Stefanie Visser, Elmar Eisemann, Ricardo Marroquim
Motor-skill learning systems in XR rely on persistent cues. However, constant cueing can induce overreliance and erode memorization and skill transfer. We introduce a skill-adaptive, dynamically transparent ghost instructor whose opacity adapts in real time to learner performance. In a first-person perspective, users observe a ghost hand executing piano fingering with either a static or a performance-adaptive transparency in a VR piano training application. We conducted a within-subjects study (N=30), where learners practiced with traditional Static (fixed-transparency) and our proposed Dynamic (performance-adaptive) modes and were tested without guidance immediately and after a 10-minute retention interval. Relative to Static, the Dynamic mode yielded higher pitch and fingering accuracy and limited error increases, with comparable timing. These findings suggest that adaptive transparency helps learners internalize fingerings more effectively, reducing dependency on external cues and improving short-term skill retention within immersive learning environments. We discuss design implications for motor-skill learning and outline directions for extending this approach to longer-term retention and more complex tasks.
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Motor-skill learning systems in XR rely on persistent cues. However, constant cueing can induce overreliance and erode memorization and skill transfer. We introduce a skill-adaptive, dynamically transparent ghost instructor whose opacity adapts in real time to learner performance. In a first-person perspective, users observe a ghost hand executing piano fingering with either a static or a performance-adaptive transparency in a VR piano training application. We conducted a within-subjects study (N=30), where learners practiced with traditional Static (fixed-transparency) and our proposed Dynamic (performance-adaptive) modes and were tested without guidance immediately and after a 10-minute retention interval. Relative to Static, the Dynamic mode yielded higher pitch and fingering accuracy and limited error increases, with comparable timing. These findings suggest that adaptive transparency helps learners internalize fingerings more effectively, reducing dependency on external cues and improving short-term skill retention within immersive learning environments. We discuss design implications for motor-skill learning and outline directions for extending this approach to longer-term retention and more complex tasks.
Resampling of warped images has been a topic of research for a long time but only seldomly has focused on theoretically exact resampling. We present a resampling method for minification, applied on the texture mapping function of a 3D graphics pipeline, that is derived from sampling theory without making any approximations. Our method supports freely selectable 2D integratable prefilter (anti-aliasing) functions and uses a 2D box reconstruction filter. We have implemented our method both for CPU and GPU (OpenGL) using multiple prefilter functions defined by piece-wise polynomials. The correctness of our exact resampling method has been made plausible by comparing texture mapping results of our method with those of extreme supersampling. We additionally show how the prefilter of our method can also be applied for high quality polygon edge anti-aliasing. Since our proposed method does not use any approximations, up to numerical precision, it can be used as a reference for approximate texture mapping methods.
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Resampling of warped images has been a topic of research for a long time but only seldomly has focused on theoretically exact resampling. We present a resampling method for minification, applied on the texture mapping function of a 3D graphics pipeline, that is derived from sampling theory without making any approximations. Our method supports freely selectable 2D integratable prefilter (anti-aliasing) functions and uses a 2D box reconstruction filter. We have implemented our method both for CPU and GPU (OpenGL) using multiple prefilter functions defined by piece-wise polynomials. The correctness of our exact resampling method has been made plausible by comparing texture mapping results of our method with those of extreme supersampling. We additionally show how the prefilter of our method can also be applied for high quality polygon edge anti-aliasing. Since our proposed method does not use any approximations, up to numerical precision, it can be used as a reference for approximate texture mapping methods.
Sparse Voxel Directed Acyclic Graphs (SVDAGs) have proven to be an efficient data structure for storing sparse binary voxel scenes. The SVDAG exploits repeating geometric patterns; which can be improved when considering mirror symmetries. We extend the previous work by providing a generalized framework to efficiently involve additional types of transformations and propose a novel translation matching for even more geometry reuse. Our new data structure is stored using a novel pointer encoding scheme to achieve a practical reduction in memory usage.
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Sparse Voxel Directed Acyclic Graphs (SVDAGs) have proven to be an efficient data structure for storing sparse binary voxel scenes. The SVDAG exploits repeating geometric patterns; which can be improved when considering mirror symmetries. We extend the previous work by providing a generalized framework to efficiently involve additional types of transformations and propose a novel translation matching for even more geometry reuse. Our new data structure is stored using a novel pointer encoding scheme to achieve a practical reduction in memory usage.
Conference paper
(2025)
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Ruben Wiersma, Julien Philip, Miloš Hašan, Krishna Mullia, Fujun Luan, Elmar Eisemann, Valentin Deschaintre
This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the observations contain enough information to reconstruct the appearance properties of a captured object. We study this ambiguity, or uncertainty, using entropy and accelerate the analysis by using the frequency domain, rather than the domain of incoming and outgoing viewing angles. The result is a method that computes a map of uncertainty over an entire object within a millisecond. We find that the frequency model allows us to recover SVBRDF parameters with competitive performance, that the accelerated entropy computation matches results with a physically-based path tracer, and that there is a positive correlation between error and uncertainty. We then show that the uncertainty map can be applied to improve SVBRDF acquisition using capture guidance, sharing information on the surface, and using a diffusion model to inpaint uncertain regions. Our code is available at https://github.com/rubenwiersma/svbrdf_uncertainty.
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This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the observations contain enough information to reconstruct the appearance properties of a captured object. We study this ambiguity, or uncertainty, using entropy and accelerate the analysis by using the frequency domain, rather than the domain of incoming and outgoing viewing angles. The result is a method that computes a map of uncertainty over an entire object within a millisecond. We find that the frequency model allows us to recover SVBRDF parameters with competitive performance, that the accelerated entropy computation matches results with a physically-based path tracer, and that there is a positive correlation between error and uncertainty. We then show that the uncertainty map can be applied to improve SVBRDF acquisition using capture guidance, sharing information on the surface, and using a diffusion model to inpaint uncertain regions. Our code is available at https://github.com/rubenwiersma/svbrdf_uncertainty.
Journal article
(2025)
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F. Friederichs, C. Benthin, S. Grogorick, E. Eisemann, M. Magnor, M. Eisemann
Ray-axis aligned bounding box intersection tests play a crucial role in the runtime performance of many rendering applications, driven not by complexity but mainly by the volume of tests required. While existing solutions were believed to be pretty much optimal in terms of runtime on current hardware, our paper introduces a new intersection test requiring fewer arithmetic operations compared to all previous methods. By transforming the ray we eliminate the need for one third of the traditional bounding-slab tests and achieve a speed enhancement of approximately 13.8% or 10.9%, depending on the compiler. We present detailed runtime analyses in various scenarios.
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Ray-axis aligned bounding box intersection tests play a crucial role in the runtime performance of many rendering applications, driven not by complexity but mainly by the volume of tests required. While existing solutions were believed to be pretty much optimal in terms of runtime on current hardware, our paper introduces a new intersection test requiring fewer arithmetic operations compared to all previous methods. By transforming the ray we eliminate the need for one third of the traditional bounding-slab tests and achieve a speed enhancement of approximately 13.8% or 10.9%, depending on the compiler. We present detailed runtime analyses in various scenarios.
Many 3D tasks such as pose alignment, animation, motion transfer, and 3D reconstruction rely on establishing correspondences between 3D shapes. This challenge has recently been approached by pairwise matching of semantic features from pre-trained vision models. However, despite their power, these features struggle to differentiate instances of the same semantic class such as "left hand"versus "right hand"which leads to substantial mapping errors. To solve this, we learn a surface-aware embedding space that is robust to these ambiguities while facilitating shared mapping for an entire family of 3D shapes. Importantly, our approach is self-supervised and requires only a small number of unpaired training meshes to infer features for new possibly imperfect 3D shapes at test time. We achieve this by introducing a contrastive loss that preserves the semantic content of the features distilled from foundational models while disambiguating features located far apart on the shape's surface. We observe superior performance in correspondence matching benchmarks and enable downstream applications including 2D-to-3D and 3D-to-3D texture transfer, in-part segmentation, pose alignment, and motion transfer in low-data regimes. Unlike previous pairwise approaches, our solution constructs a joint embedding space, where both seen and unseen 3D shapes are implicitly aligned without further optimization. The code is available at https://graphics.tudelft.nl/SurfaceAware3DFeatures.
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Many 3D tasks such as pose alignment, animation, motion transfer, and 3D reconstruction rely on establishing correspondences between 3D shapes. This challenge has recently been approached by pairwise matching of semantic features from pre-trained vision models. However, despite their power, these features struggle to differentiate instances of the same semantic class such as "left hand"versus "right hand"which leads to substantial mapping errors. To solve this, we learn a surface-aware embedding space that is robust to these ambiguities while facilitating shared mapping for an entire family of 3D shapes. Importantly, our approach is self-supervised and requires only a small number of unpaired training meshes to infer features for new possibly imperfect 3D shapes at test time. We achieve this by introducing a contrastive loss that preserves the semantic content of the features distilled from foundational models while disambiguating features located far apart on the shape's surface. We observe superior performance in correspondence matching benchmarks and enable downstream applications including 2D-to-3D and 3D-to-3D texture transfer, in-part segmentation, pose alignment, and motion transfer in low-data regimes. Unlike previous pairwise approaches, our solution constructs a joint embedding space, where both seen and unseen 3D shapes are implicitly aligned without further optimization. The code is available at https://graphics.tudelft.nl/SurfaceAware3DFeatures.
SpineLoft
Interactive Spine-based 2D-to-3D Modeling
Conference paper
(2025)
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Alexandre Thiault, Telo Philippe, Amal Dev Parakkat, Elmar Eisemann, Ramanathan Muthuganapathy, Takeo Igarashi
3D artists (professionals and novices alike) often take inspiration from sketches or photos to guide their designs. Yet, existing modeling systems are not tailored to fully make use of such input. Consequently, significant effort and expertise are needed when creating model prototypes or exploring design options. In this work, we introduce a system to support the exploratory modeling process by enabling the transformation of 2D image elements into geometric 3D objects. Our solution relies on a novel d2 distance function, supporting a region-based lofting process, and delivers easily-editable 3D geometric "spine-rib" representations. The user draws a spine, and the system generates and modifies a generalized cylinder around it, considering image edges. The proposed approach, driven by simple user-defined scribble definitions, can robustly handle various image sources, ranging from photos to hand-drawn content.
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3D artists (professionals and novices alike) often take inspiration from sketches or photos to guide their designs. Yet, existing modeling systems are not tailored to fully make use of such input. Consequently, significant effort and expertise are needed when creating model prototypes or exploring design options. In this work, we introduce a system to support the exploratory modeling process by enabling the transformation of 2D image elements into geometric 3D objects. Our solution relies on a novel d2 distance function, supporting a region-based lofting process, and delivers easily-editable 3D geometric "spine-rib" representations. The user draws a spine, and the system generates and modifies a generalized cylinder around it, considering image edges. The proposed approach, driven by simple user-defined scribble definitions, can robustly handle various image sources, ranging from photos to hand-drawn content.
Puzzle Playground
Teaching VR Interactions Through a Puzzle Game
Conference paper
(2025)
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Vincent Albertsson, Patrick Hibbs, Csanád Farkas, Qinxin Ren, Amir Zaidi, Ricardo Marroquim, Elmar Eisemann, Rafael Bidarra
In recent years, it has become clear that modern education is not currently equipped with the proper tools to fully support remote teaching. Virtual reality (VR) has the potential to make remote education viable in the future. Nevertheless, many teachers and students lack experience and familiarity with this technology, which poses a challenge to its adoption in education. In this paper, we introduce Puzzle Playground, a game that builds familiarity with VR by teaching object interactions through puzzles in an interactive experience tailored for educators. Players gradually learn VR interactions by completing various puzzle levels. A preliminary user study indicated that people who learned with Puzzle Playground grasped VR interactions faster than those who learned with printed or visual methods.
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In recent years, it has become clear that modern education is not currently equipped with the proper tools to fully support remote teaching. Virtual reality (VR) has the potential to make remote education viable in the future. Nevertheless, many teachers and students lack experience and familiarity with this technology, which poses a challenge to its adoption in education. In this paper, we introduce Puzzle Playground, a game that builds familiarity with VR by teaching object interactions through puzzles in an interactive experience tailored for educators. Players gradually learn VR interactions by completing various puzzle levels. A preliminary user study indicated that people who learned with Puzzle Playground grasped VR interactions faster than those who learned with printed or visual methods.
RANRAC
Robust Neural Scene Representations via Random Ray Consensus
Conference paper
(2025)
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Benno Buschmann, Andreea Dogaru, Elmar Eisemann, Michael Weinmann, Bernhard Egger
Learning-based scene representations such as neural radiance fields or light field networks, that rely on fitting a scene model to image observations, commonly encounter challenges in the presence of inconsistencies within the images caused by occlusions, inaccurately estimated camera parameters or effects like lens flare. To address this challenge, we introduce RANdom RAy Consensus (RANRAC), an efficient approach to eliminate the effect of inconsistent data, thereby taking inspiration from classical RANSAC based outlier detection for model fitting. In contrast to the down-weighting of the effect of outliers based on robust loss formulations, our approach reliably detects and excludes inconsistent perspectives, resulting in clean images without floating artifacts. For this purpose, we formulate a fuzzy adaption of the RANSAC paradigm, enabling its application to large scale models. We interpret the minimal number of samples to determine the model parameters as a tunable hyperparameter, investigate the generation of hypotheses with data-driven models, and analyse the validation of hypotheses in noisy environments. We demonstrate the compatibility and potential of our solution for both photo-realistic robust multi-view reconstruction from real-world images based on neural radiance fields and for single-shot reconstruction based on light-field networks. In particular, the results indicate significant improvements compared to state-of-the-art robust methods for novel-view synthesis on both synthetic and captured scenes with various inconsistencies including occlusions, noisy camera pose estimates, and unfocused perspectives. The results further indicate significant improvements for single-shot reconstruction from occluded images.
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Learning-based scene representations such as neural radiance fields or light field networks, that rely on fitting a scene model to image observations, commonly encounter challenges in the presence of inconsistencies within the images caused by occlusions, inaccurately estimated camera parameters or effects like lens flare. To address this challenge, we introduce RANdom RAy Consensus (RANRAC), an efficient approach to eliminate the effect of inconsistent data, thereby taking inspiration from classical RANSAC based outlier detection for model fitting. In contrast to the down-weighting of the effect of outliers based on robust loss formulations, our approach reliably detects and excludes inconsistent perspectives, resulting in clean images without floating artifacts. For this purpose, we formulate a fuzzy adaption of the RANSAC paradigm, enabling its application to large scale models. We interpret the minimal number of samples to determine the model parameters as a tunable hyperparameter, investigate the generation of hypotheses with data-driven models, and analyse the validation of hypotheses in noisy environments. We demonstrate the compatibility and potential of our solution for both photo-realistic robust multi-view reconstruction from real-world images based on neural radiance fields and for single-shot reconstruction based on light-field networks. In particular, the results indicate significant improvements compared to state-of-the-art robust methods for novel-view synthesis on both synthetic and captured scenes with various inconsistencies including occlusions, noisy camera pose estimates, and unfocused perspectives. The results further indicate significant improvements for single-shot reconstruction from occluded images.
Journal article
(2024)
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Alexander Vieth, Thomas Kroes, Julian Thijssen, Baldur van Lew, Jeroen Eggermont, Soumyadeep Basu, Elmar Eisemann, Anna Vilanova, Thomas Höllt, Boudewijn Lelieveldt
Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio.
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Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio.
Efficient and precise texture filtering is essential in various applications. However, there is often a trade-off between coarse real-time approximations and accurate computationally-expensive supersampling. We introduce a novel efficient texture-filtering method over arbitrary quadrilateral footprints, achieving high accuracy at a low computational cost. We achieve this by pre-computing integration tables that sparsely sample the space of possible footprints. Finally, we compare the qualitative and computational performance of our method to commonly used techniques and demonstrate various applications for high-quality real-time image synthesis, including normal filtering, soft shadow mapping, and glint rendering.
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Efficient and precise texture filtering is essential in various applications. However, there is often a trade-off between coarse real-time approximations and accurate computationally-expensive supersampling. We introduce a novel efficient texture-filtering method over arbitrary quadrilateral footprints, achieving high accuracy at a low computational cost. We achieve this by pre-computing integration tables that sparsely sample the space of possible footprints. Finally, we compare the qualitative and computational performance of our method to commonly used techniques and demonstrate various applications for high-quality real-time image synthesis, including normal filtering, soft shadow mapping, and glint rendering.
The display coefficients that produce the signal emitted by a light field display are usually calculated to approximate the radiance over a set of sampled rays in the light field space. However, not all information contained in the light field signal is of equal importance to an observer. We propose a retinal pre-filtering of the light field samples that takes into account the image formation process of the observer to determine display coefficients that will ultimately produce better retinal images for a range of focus distances. We demonstrate a significant increase in image definition without changing the display resolution.
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The display coefficients that produce the signal emitted by a light field display are usually calculated to approximate the radiance over a set of sampled rays in the light field space. However, not all information contained in the light field signal is of equal importance to an observer. We propose a retinal pre-filtering of the light field samples that takes into account the image formation process of the observer to determine display coefficients that will ultimately produce better retinal images for a range of focus distances. We demonstrate a significant increase in image definition without changing the display resolution.
Recovering spatially-varying materials from a single photograph of a surface is inherently ill-posed, making the direct application of a gradient descent on the reflectance parameters prone to poor minima. Recent methods leverage deep learning either by directly regressing reflectance parameters using feed-forward neural networks or by learning a latent space of SVBRDFs using encoder-decoder or generative adversarial networks followed by a gradient-based optimization in latent space. The former is fast but does not account for the likelihood of the prediction, i.e., how well the resulting reflectance explains the input image. The latter provides a strong prior on the space of spatially-varying materials, but this prior can hinder the reconstruction of images that are too different from the training data. Our method combines the strengths of both approaches. We optimize reflectance parameters to best reconstruct the input image using a recurrent neural network, which iteratively predicts how to update the reflectance parameters given the gradient of the reconstruction likelihood. By combining a learned prior with a likelihood measure, our approach provides a maximum a posteriori estimate of the SVBRDF. Our evaluation shows that this learned gradient-descent method achieves state-of-the-art performance for SVBRDF estimation on synthetic and real images.
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Recovering spatially-varying materials from a single photograph of a surface is inherently ill-posed, making the direct application of a gradient descent on the reflectance parameters prone to poor minima. Recent methods leverage deep learning either by directly regressing reflectance parameters using feed-forward neural networks or by learning a latent space of SVBRDFs using encoder-decoder or generative adversarial networks followed by a gradient-based optimization in latent space. The former is fast but does not account for the likelihood of the prediction, i.e., how well the resulting reflectance explains the input image. The latter provides a strong prior on the space of spatially-varying materials, but this prior can hinder the reconstruction of images that are too different from the training data. Our method combines the strengths of both approaches. We optimize reflectance parameters to best reconstruct the input image using a recurrent neural network, which iteratively predicts how to update the reflectance parameters given the gradient of the reconstruction likelihood. By combining a learned prior with a likelihood measure, our approach provides a maximum a posteriori estimate of the SVBRDF. Our evaluation shows that this learned gradient-descent method achieves state-of-the-art performance for SVBRDF estimation on synthetic and real images.
BallMerge
High-quality Fast Surface Reconstruction via Voronoi Balls
We introduce a Delaunay-based algorithm for reconstructing the underlying surface of a given set of unstructured points in 3D. The implementation is very simple, and it is designed to work in a parameter-free manner. The solution builds upon the fact that in the continuous case, a closed surface separates the set of maximal empty balls (medial balls) into an interior and exterior. Based on discrete input samples, our reconstructed surface consists of the interface between Voronoi balls, which approximate the interior and exterior medial balls. An initial set of Voronoi balls is iteratively processed, merging Voronoi-ball pairs if they fulfil an overlapping error criterion. Our complete open-source reconstruction pipeline performs up to two quick linear-time passes on the Delaunay complex to output the surface, making it an order of magnitude faster than the state of the art while being competitive in memory usage and often superior in quality. We propose two variants (local and global), which are carefully designed to target two different reconstruction scenarios for watertight surfaces from accurate or noisy samples, as well as real-world scanned data sets, exhibiting noise, outliers, and large areas of missing data. The results of the global variant are, by definition, watertight, suitable for numerical analysis and various applications (e.g., 3D printing). Compared to classical Delaunay-based reconstruction techniques, our method is highly stable and robust to noise and outliers, evidenced via various experiments, including on real-world data with challenges such as scan shadows, outliers, and noise, even without additional preprocessing.
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We introduce a Delaunay-based algorithm for reconstructing the underlying surface of a given set of unstructured points in 3D. The implementation is very simple, and it is designed to work in a parameter-free manner. The solution builds upon the fact that in the continuous case, a closed surface separates the set of maximal empty balls (medial balls) into an interior and exterior. Based on discrete input samples, our reconstructed surface consists of the interface between Voronoi balls, which approximate the interior and exterior medial balls. An initial set of Voronoi balls is iteratively processed, merging Voronoi-ball pairs if they fulfil an overlapping error criterion. Our complete open-source reconstruction pipeline performs up to two quick linear-time passes on the Delaunay complex to output the surface, making it an order of magnitude faster than the state of the art while being competitive in memory usage and often superior in quality. We propose two variants (local and global), which are carefully designed to target two different reconstruction scenarios for watertight surfaces from accurate or noisy samples, as well as real-world scanned data sets, exhibiting noise, outliers, and large areas of missing data. The results of the global variant are, by definition, watertight, suitable for numerical analysis and various applications (e.g., 3D printing). Compared to classical Delaunay-based reconstruction techniques, our method is highly stable and robust to noise and outliers, evidenced via various experiments, including on real-world data with challenges such as scan shadows, outliers, and noise, even without additional preprocessing.
Journal article
(2024)
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Martin Skrodzki, Hunter van Geffen, Nicolas F. Chaves-de-Plaza, Thomas Höllt, Elmar Eisemann, Klaus Hildebrandt
The need to understand the structure of hierarchical or high-dimensional data is present in a variety of fields. Hyperbolic spaces have proven to be an important tool for embedding computations and analysis tasks as their non-linear nature lends itself well to tree or graph data. Subsequently, they have also been used in the visualization of high-dimensional data, where they exhibit increased embedding performance. However, none of the existing dimensionality reduction methods for embedding into hyperbolic spaces scale well with the size of the input data. That is because the embeddings are computed via iterative optimization schemes and the computation cost of every iteration is quadratic in the size of the input. Furthermore, due to the non-linear nature of hyperbolic spaces, euclidean acceleration structures cannot directly be translated to the hyperbolic setting. This article introduces the first acceleration structure for hyperbolic embeddings, building upon a polar quadtree. We compare our approach with existing methods and demonstrate that it computes embeddings of similar quality in significantly less time. Implementation and scripts for the experiments can be found at https://graphics.tudelft.nl/accelerating-hyperbolic-tsne .
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The need to understand the structure of hierarchical or high-dimensional data is present in a variety of fields. Hyperbolic spaces have proven to be an important tool for embedding computations and analysis tasks as their non-linear nature lends itself well to tree or graph data. Subsequently, they have also been used in the visualization of high-dimensional data, where they exhibit increased embedding performance. However, none of the existing dimensionality reduction methods for embedding into hyperbolic spaces scale well with the size of the input data. That is because the embeddings are computed via iterative optimization schemes and the computation cost of every iteration is quadratic in the size of the input. Furthermore, due to the non-linear nature of hyperbolic spaces, euclidean acceleration structures cannot directly be translated to the hyperbolic setting. This article introduces the first acceleration structure for hyperbolic embeddings, building upon a polar quadtree. We compare our approach with existing methods and demonstrate that it computes embeddings of similar quality in significantly less time. Implementation and scripts for the experiments can be found at https://graphics.tudelft.nl/accelerating-hyperbolic-tsne .
Spectral rendering has received increasing attention in recent years. Yet, solutions to define spectral reflectances are mostly limited to uplifting techniques which deterministically augment existing RGB inputs. Only recently has uplifting been able to ensure a certain surface appearance under direct illuminants. Yet, prior work in this area limits artist expressiveness and is not well suited for designing the appearance of a scene, as indirect illumination is ignored entirely.
We present an uplifting technique with fine-grained spectral appearance control under direct and indirect illumination, even enabling the placement of spectral constraints in a specific scene. Our approach allows for a flexible authoring process, and solves for the resulting spectra efficiently. Additionally, we show that our method’s memory overhead during rendering is kept small, by introducing a compact spectral texture format. ...
We present an uplifting technique with fine-grained spectral appearance control under direct and indirect illumination, even enabling the placement of spectral constraints in a specific scene. Our approach allows for a flexible authoring process, and solves for the resulting spectra efficiently. Additionally, we show that our method’s memory overhead during rendering is kept small, by introducing a compact spectral texture format. ...
Spectral rendering has received increasing attention in recent years. Yet, solutions to define spectral reflectances are mostly limited to uplifting techniques which deterministically augment existing RGB inputs. Only recently has uplifting been able to ensure a certain surface appearance under direct illuminants. Yet, prior work in this area limits artist expressiveness and is not well suited for designing the appearance of a scene, as indirect illumination is ignored entirely.
We present an uplifting technique with fine-grained spectral appearance control under direct and indirect illumination, even enabling the placement of spectral constraints in a specific scene. Our approach allows for a flexible authoring process, and solves for the resulting spectra efficiently. Additionally, we show that our method’s memory overhead during rendering is kept small, by introducing a compact spectral texture format.
We present an uplifting technique with fine-grained spectral appearance control under direct and indirect illumination, even enabling the placement of spectral constraints in a specific scene. Our approach allows for a flexible authoring process, and solves for the resulting spectra efficiently. Additionally, we show that our method’s memory overhead during rendering is kept small, by introducing a compact spectral texture format.
A Sparse Voxel Directed Acyclic Graph (SVDAG) is an efficient representation to display and store a highly-detailed voxel representation in a very compact data structure. Yet, editing such a high-resolution scene in real-time is challenging. Existing solutions are hybrid, involving the CPU, and are restricted to small local modifications. In this work, we address this bottleneck and propose a solution to perform edits fully on the graphics card, enabled by dynamic GPU hash tables. Our framework makes large editing operations possible, such as 3D painting, at real-time frame rates.
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A Sparse Voxel Directed Acyclic Graph (SVDAG) is an efficient representation to display and store a highly-detailed voxel representation in a very compact data structure. Yet, editing such a high-resolution scene in real-time is challenging. Existing solutions are hybrid, involving the CPU, and are restricted to small local modifications. In this work, we address this bottleneck and propose a solution to perform edits fully on the graphics card, enabled by dynamic GPU hash tables. Our framework makes large editing operations possible, such as 3D painting, at real-time frame rates.
Journal article
(2024)
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N. F. Chaves-de-Plaza, M. Molenaar, P. Mody, M. Staring, R. van Egmond, E. Eisemann, A. Vilanova, K. Hildebrandt
The contour depth methodology enables non-parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear-time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear-time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non-parametric multimodal analysis. To promote research and adoption, we offer the contour-depth Python package.
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The contour depth methodology enables non-parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear-time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear-time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non-parametric multimodal analysis. To promote research and adoption, we offer the contour-depth Python package.
Sparse Voxel Directed Acyclic Graphs (SVDAGs) are an efficient solution for storing high-resolution voxel geometry. Recently, algorithms for the interactive modification of SVDAGs have been proposed that maintain the compressed geometric representation. Nevertheless, voxel attributes, such as colours, require an uncompressed storage, which can result in high memory usage over the course of the application. The reason is the high cost of existing attribute-compression schemes which remain unfit for interactive applications. In this paper, we introduce two attribute compression methods (lossless and lossy), which enable the interactive editing of compressed high-resolution voxel scenes including attributes.
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Sparse Voxel Directed Acyclic Graphs (SVDAGs) are an efficient solution for storing high-resolution voxel geometry. Recently, algorithms for the interactive modification of SVDAGs have been proposed that maintain the compressed geometric representation. Nevertheless, voxel attributes, such as colours, require an uncompressed storage, which can result in high memory usage over the course of the application. The reason is the high cost of existing attribute-compression schemes which remain unfit for interactive applications. In this paper, we introduce two attribute compression methods (lossless and lossy), which enable the interactive editing of compressed high-resolution voxel scenes including attributes.