Feature-Sized Sampling for Vector Line Art

International audienceBy 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 ε-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 ε-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 ε 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

Outline and Shape Reconstruction in 2D

International audienceOutline and shape reconstruction from unstructured points in a plane is a fundamental problem with many applications that have generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability, and potential extension to 3D for surface reconstruction have led to many different algorithms. This multitude of reconstruction methods with quite different strengths and focus makes it difficult for users to choose a suitable algorithm for their specific problem. In this tutorial, we present proximity graphs, graph-based algorithms, and algorithms with sampling guarantees, all in detail. Then, we show algorithms targeted at specific problem classes, such as reconstructing from noise, outliers, or sharp corners. Examples of the evaluation will show how its results can guide users in selecting an appropriate algorithm for their input data. As a special application, we show the reconstruction of lines in the context of sketch completion and sketch simplification. Shape characterization of dot patterns will be shown as an additional field closely related to boundary reconstruction

Collision Free Simplification for 2D Multi-Layered Shapes

International audienceWe 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

Outline and Shape Reconstruction in 2D

International audienceOutline and shape reconstruction from unstructured points in a plane is a fundamental problem with many applications that have generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability, and potential extension to 3D for surface reconstruction have led to many different algorithms. This multitude of reconstruction methods with quite different strengths and focus makes it difficult for users to choose a suitable algorithm for their specific problem. In this tutorial, we present proximity graphs, graph-based algorithms, and algorithms with sampling guarantees, all in detail. Then, we show algorithms targeted at specific problem classes, such as reconstructing from noise, outliers, or sharp corners. Examples of the evaluation will show how its results can guide users in selecting an appropriate algorithm for their input data. As a special application, we show the reconstruction of lines in the context of sketch completion and sketch simplification. Shape characterization of dot patterns will be shown as an additional field closely related to boundary reconstruction

Interactive Depixelization of Pixel Art through Spring Simulation

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.</p

Feature-Sized Sampling for Vector Line Art

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 ε-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 ε-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 ε 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

CT-shape: Coordinated triangle based reconstruction from dot patterns and boundary samples

International audienceGiven a set of points S ∈ R 2 , reconstruction is a process of identifying the boundary edges that best approximates the set of points. In this paper, we propose a unified algorithm for reconstruction that works for both dot patterns as well as boundary samples. The algorithm starts with computing the Delaunay triangulation of the given point set and edges are iteratively removed based on the structure of a pair of triangles. Further, we also propose additional criteria for removing edges based on characterizing a triangle and using degree constraint. Unlike the existing algorithms, the proposed approach requires only a single pass to capture both inner and outer boundaries irrespective of the number of objects/holes. Moreover, the same criterion has been employed for both inner and outer boundary detection. The experiments show that our approach works well for different kinds of inputs. We have done extensive comparisons with state-of-the-art methods for various kinds of point sets including varying the sampling density and distribution and found to perform better or on par with them

Delaunay Painting: Perceptual image coloring from raster contours with gaps

International audienceWe introduce Delaunay Painting, a novel and easy-to-use method to flat-color contour-sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colors, thanks to the dynamic update of flow values calculated from color 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 color diffusion framework which automatically extends coloring 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 coloring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the coloring strategy for quick segmentation of contours images, that we illustrate with an application to medical imaging

Layered Reconstruction of Stippling Art

International audienceIn this work, we introduce a dedicated variant of traditional 2D reconstruction in which the input point set (a stippled image) caninclude different regions corresponding to different point densities. Our method converts this input into a layered, vector representation, which includes a main shape plus internal, closed regions, defined by their boundary. Providing such structured output eases subsequent editing and processing, such as generating shaded vector images from the stippled input. To achieve this, we first apply a layered reconstruction algorithm based on the detectedregions in the input point set, and use the output for generating the outer and inner shape boundaries in the stippled drawin