Browsing by Author "Parakkat, Amal Dev"

Now showing 1 - 9 of 9
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    2D Points Curve Reconstruction Survey and Benchmark
    (The Eurographics Association, 2022) Ohrhallinger, Stefan; Peethambaran, Jiju; Parakkat, Amal Dev; Dey, Tamal K.; Muthuganapathy, R.; Hahmann, Stefanie; Patow, Gustavo A.
    Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.
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    2D Points Curve Reconstruction Survey and Benchmark
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Ohrhallinger, Stefan; Peethambaran, Jiju; Parakkat, Amal Dev; Dey, Tamal Krishna; Muthuganapathy, Ramanathan; Bühler, Katja and Rushmeier, Holly
    Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.
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    Collision Free Simplification for 2D Multi-Layered Shapes
    (The Eurographics Association, 2023) Gong, Xianjin; Parakkat, Amal Dev; Rohmer, Damien; Pelechano, Nuria; Liarokapis, Fotis; Rohmer, Damien; Asadipour, Ali
    We propose a simplification-aware untangling algorithm for 2D layered shapes stacked on each other. While the shape undergoes simplification, our approach adjusts the vertex positions to prevent collision with other layers while simultaneously maintaining the correct relative ordering and offsets between the layers. The method features a field-based representation of the shapes and extends the concept of "implicit untangling" by incorporating interleaved shape preservation through a parameterized shape-matching technique. Our approach can be plugged on top of any existing vertex-decimation approach, leveraging its localized nature to accelerate the field evaluation. Furthermore, our method can seamlessly handle an arbitrary number of stacked layers, making it a versatile solution for stacked garment simplification.
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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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    Interactive Depixelization of Pixel Art through Spring Simulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Matusovic, Marko; Parakkat, Amal Dev; Eisemann, Elmar; Myszkowski, Karol; Niessner, Matthias
    We introduce an approach for converting pixel art into high-quality vector images. While much progress has been made on automatic conversion, there is an inherent ambiguity in pixel art, which can lead to a mismatch with the artist's original intent. Further, there is room for incorporating aesthetic preferences during the conversion. In consequence, this work introduces an interactive framework to enable users to guide the conversion process towards high-quality vector illustrations. A key idea of the method is to cast the conversion process into a spring-system optimization that can be influenced by the user. Hereby, it is possible to resolve various ambiguities that cannot be handled by an automatic algorithm.
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    Interactive Flat Coloring of Minimalist Neat Sketches
    (The Eurographics Association, 2020) Parakkat, Amal Dev; Madipally, Prudhviraj; Gowtham, Hari Hara; Cani, Marie-Paule; Wilkie, Alexander and Banterle, Francesco
    We introduce a simple Delaunay-triangulation based algorithm for the interactive coloring of neat line-art minimalist sketches, ie. vector sketches that may include open contours. The main objective is to minimize user intervention and make interaction as natural as with the flood-fill algorithm while extending coloring to regions with open contours. In particular, we want to save the user from worrying about parameters such as stroke weight and size. Our solution works in two steps, 1) a segmentation step in which the input sketch is automatically divided into regions based on the underlying Delaunay structure and 2) the interactive grouping of neighboring regions based on user input. More precisely, a region adjacency graph is computed from the segmentation result, and is interactively partitioned based on user input to generate the final colored sketch. Results show that our method is as natural as a bucket fill tool and powerful enough to color minimalist sketches.
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    PointCloudSlicer: Gesture-based Segmentation of Point Clouds
    (The Eurographics Association, 2023) Gowtham, Hari Hara; Parakkat, Amal Dev; Cani, Marie-Paule; Babaei, Vahid; Skouras, Melina
    Segmentation is a fundamental problem in point-cloud processing, addressing points classification into consistent regions, the criteria for consistency being based on the application. In this paper, we introduce a simple, interactive framework enabling the user to quickly segment a point cloud in a few cutting gestures in a perceptually consistent way. As the user perceives the limit of a shape part, they draw a simple separation stroke over the current 2D view. The point cloud is then segmented without needing any intermediate meshing step. Technically, we find an optimal, perceptually consistent cutting plane constrained by user stroke and use it for segmentation while automatically restricting the extent of the cut to the closest shape part from the current viewpoint. This enables users to effortlessly segment complex point clouds from an arbitrary viewpoint with the possibility of handling self-occlusions.
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    Structuring and Layering Contour Drawings of Organic Shapes
    (ACM, 2018) Entem, Even; Parakkat, Amal Dev; Cani, Marie-Paule; Barthe, Loïc; Aydın, Tunç and Sýkora, Daniel
    Complex vector drawings serve as convenient and expressive visual representations, but they remain difficult to edit or manipulate. For clean-line vector drawings of smooth organic shapes, we describe a method to automatically extract a layered structure for the drawn object from the current or nearby viewpoints. The layers correspond to salient regions of the drawing, which are often naturally associated to `parts' of the underlying shape. We present a method that automatically extracts salient structure, organized as parts with relative depth orderings, from clean-line vector drawings of smooth organic shapes. Our method handles drawings that contain complex internal contours with T-junctions indicative of occlusions, as well as internal curves that may either be expressive strokes or substructures. To extract the structure, we introduce a new part-aware metric for complex 2D drawings, the radial variation metric, which is used to identify salient sub-parts. These sub-parts are then considered in a priority-ordered fashion, which enables us to identify and recursively process new shape parts while keeping track of their relative depth ordering. The output is represented in terms of scalable vector graphics layers, thereby enabling meaningful editing and manipulation. We evaluate the method on multiple input drawings and show that the structure we compute is convenient for subsequent posing and animation from nearby viewpoints.