Browsing by Author "Memari, Pooran"

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    Accurate Synthesis of Multi-Class Disk Distributions
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ecormier-Nocca, Pierre; Memari, Pooran; Gain, James; Cani, Marie-Paule; Alliez, Pierre and Pellacini, Fabio
    While analysing and synthesising 2D distributions of points has been applied both to the generation of textures with discrete elements and for populating virtual worlds with 3D objects, the results are often inaccurate since the spatial extent of objects cannot be expressed.We introduce three improvements enabling the synthesis of more general distributions of elements. First, we extend continuous pair correlation function (PCF) algorithms to multi-class distributions using a dependency graph, thereby capturing interrelationships between distinct categories of objects. Second, we introduce a new normalised metric for disks, which makes the method applicable to both point and possibly overlapping disk distributions. The metric is specifically designed to distinguish perceptually salient features, such as disjoint, tangent, overlapping, or nested disks. Finally, we pay particular attention to convergence of the mean PCF as well as the validity of individual PCFs, by taking into consideration the variance of the input. Our results demonstrate that this framework can capture and reproduce real-life distributions of elements representing a variety of complex semi-structured patterns, from the interaction between trees and the understorey in a forest to droplets of water. More generally, it applies to any category of 2D object whose shape is better represented by bounding circles than points.
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    BallMerge: High-quality Fast Surface Reconstruction via Voronoi Balls
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Parakkat, Amal Dev; Ohrhallinger, Stefan; Eisemann, Elmar; Memari, Pooran; Bermano, Amit H.; Kalogerakis, Evangelos
    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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    Bio-Sketch: A New Medium for Interactive Storytelling Illustrated by the Phenomenon of Infection
    (The Eurographics Association, 2023) Olivier, Pauline; Chabrier, Renaud; Memari, Pooran; Coll, Jean-Luc; Cani, Marie-Paule; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    In the field of biology, digital illustrations play a crucial role in conveying complex phenomena, allowing for idealized shapes and motion, in contrast to data visualization. In the absence of suitable media, scientists often rely on oversimplified 2D figures or have to call in professional artists to create better illustrations, which can be limiting. We introduce Bio-Sketch, a novel progressive sketching system designed to ease the creation of animated illustrations, as exemplified here in the context of the infection phenomenon. Our solution relies on a new progressive sketching paradigm that seamlessly combines 3D modeling and pattern-based shape distribution to create background volume and temporal animation control. The elements created can be assembled into a complex scenario, enabling narrative design experiments for educational applications in biology. Our results and first feedback from experts in illustration and biology demonstrate the potential of Bio-Sketch to assist communication on the infection phenomenon, helping to bridge the gap between expert and non-expert audiences.
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    Connectivity-preserving Smooth Surface Filling with Sharp Features
    (The Eurographics Association, 2019) Lescoat, Thibault; Memari, Pooran; Thiery, Jean-Marc; Ovsjanikov, Maks; Boubekeur, Tamy; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    We present a method for constructing a surface mesh filling gaps between the boundaries of multiple disconnected input components. Unlike previous works, our method pays special attention to preserving both the connectivity and large-scale geometric features of input parts, while maintaining efficiency and scalability w.r.t. mesh complexity. Starting from an implicit surface reconstruction matching the parts' boundaries, we first introduce a modified dual contouring algorithm which stitches a meshed contour to the input components while preserving their connectivity. We then show how to deform the reconstructed mesh to respect the boundary geometry and preserve sharp feature lines, smoothly blending them when necessary. As a result, our reconstructed surface is smooth and propagates the feature lines of the input. We demonstrate on a wide variety of input shapes that our method is scalable to large input complexity and results in superior mesh quality compared to existing techniques.
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    Delaunay Painting: Perceptual Image Colouring from Raster Contours with Gaps
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Parakkat, Amal Dev; Memari, Pooran; Cani, Marie‐Paule; Hauser, Helwig and Alliez, Pierre
    We introduce Delaunay Painting, a novel and easy‐to‐use method to flat‐colour contour‐sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colours, thanks to the dynamic update of flow values calculated from colour hints. Aesthetic finish is then achieved, through energy minimisation of contour‐curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our colour diffusion framework, which automatically extends colouring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any colouring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the colouring strategy for quick segmentation of contours images, that we illustrate with applications to medical imaging and sketch segmentation.
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    Feature-Sized Sampling for Vector Line Art
    (The Eurographics Association, 2023) Ohrhallinger, Stefan; Parakkat, Amal Dev; Memari, Pooran; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    By introducing a first-of-its-kind quantifiable sampling algorithm based on feature size, we present a fresh perspective on the practical aspects of planar curve sampling. Following the footsteps of e-sampling, which was originally proposed in the context of curve reconstruction to offer provable topological guarantees [ABE98] under quantifiable bounds, we propose an arbitrarily precise e-sampling algorithm for sampling smooth planar curves (with a prior bound on the minimum feature size of the curve). This paper not only introduces the first such algorithm which provides user-control and quantifiable precision but also highlights the importance of such a sampling process under two key contexts: 1) To conduct a first study comparing theoretical sampling conditions with practical sampling requirements for reconstruction guarantees that can further be used for analysing the upper bounds of e for various reconstruction algorithms with or without proofs, 2) As a feature-aware sampling of vector line art that can be used for applications such as coloring and meshing.
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    Pair Correlation Functions with Free-Form Boundaries for Distribution Inpainting and Decomposition
    (The Eurographics Association, 2020) Nicolet, Baptiste; Ecormier-Nocca, Pierre; Memari, Pooran; Cani, Marie-Paule; Wilkie, Alexander and Banterle, Francesco
    Pair Correlation Functions (PCF) have been recently spreading as a reliable representation for distributions, enabling the efficient synthesis of point-sets, vector textures and object placement from examples. In this work we introduce a triangulationbased local filtering method to extend PCF-based analysis to exemplars with free-form boundaries. This makes PCF applicable to new problems such as the inpainting of missing parts in an input distribution, or the decomposition of complex, non-homogeneous distributions into a set of coherent classes, in which each category of points can be studied together with their intra and inter-class correlations.
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    Point-Pattern Synthesis using Gabor and Random Filters
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Huang, Xingchang; Memari, Pooran; Seidel, Hans-Peter; Singh, Gurprit; Ghosh, Abhijeet; Wei, Li-Yi
    Point pattern synthesis requires capturing both local and non-local correlations from a given exemplar. Recent works employ deep hierarchical representations from VGG-19 [SZ15] convolutional network to capture the features for both point-pattern and texture synthesis. In this work, we develop a simplified optimization pipeline that uses more traditional Gabor transform-based features. These features when convolved with simple random filters gives highly expressive feature maps. The resulting framework requires significantly less feature maps compared to VGG-19-based methods [TLH19; RGF*20], better captures both the local and non-local structures, does not require any specific data set training and can easily extend to handle multi-class and multi-attribute point patterns, e.g., disk and other element distributions. To validate our pipeline, we perform qualitative and quantitative analysis on a large variety of point patterns to demonstrate the effectiveness of our approach. Finally, to better understand the impact of random filters, we include a spectral analysis using filters with different frequency bandwidths.
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    Robust Pointset Denoising of Piecewise-Smooth Surfaces through Line Processes
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Wei, Jiayi; Chen, Jiong; Rohmer, Damien; Memari, Pooran; Desbrun, Mathieu; Myszkowski, Karol; Niessner, Matthias
    Denoising is a common, yet critical operation in geometry processing aiming at recovering high-fidelity models of piecewisesmooth objects from noise-corrupted pointsets. Despite a sizable literature on the topic, there is a dearth of approaches capable of processing very noisy and outlier-ridden input pointsets for which no normal estimates and no assumptions on the underlying geometric features or noise type are provided. In this paper, we propose a new robust-statistics approach to denoising pointsets based on line processes to offer robustness to noise and outliers while preserving sharp features possibly present in the data. While the use of robust statistics in denoising is hardly new, most approaches rely on prescribed filtering using data-independent blending expressions based on the spatial and normal closeness of samples. Instead, our approach deduces a geometric denoising strategy through robust and regularized tangent plane fitting of the initial pointset, obtained numerically via alternating minimizations for efficiency and reliability. Key to our variational approach is the use of line processes to identify inliers vs. outliers, as well as the presence of sharp features. We demonstrate that our method can denoise sampled piecewise-smooth surfaces for levels of noise and outliers at which previous works fall short.
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    SGP 2023 CGF 42-5: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Memari, Pooran; Solomon, Justin; Memari, Pooran; Solomon, Justin