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A sampling type discernment approach towards reconstruction of a point set in R2

01-01-2021, Thayyil, Safeer Babu, Peethambaran, Jiju, Muthuganapathy, Ramanathan

In this paper, we propose a Delaunay triangulation based algorithmic framework for the 2D point set type discernment and its reconstruction. The point set discernment deals with determining whether the given point set S consists of only the points sampled along the boundary of an object (referred to as boundary sample) or S is distributed across the object (referred to as dot-pattern). In general, the existing approaches deal with the reconstruction of an a priori known input type - boundary sample or dot-pattern. Our approach works on both sets of input data type by first identifying the input type that a given point set belongs to. To distinguish the point set type, we introduce the notion of Petal-Ratio(PR) which captures the ratio of the longest edge to the smallest edge emanating from a vertex in Delaunay triangulation. Further, we employ the Petal-Ratio as the essence to design simple reconstruction algorithms pertaining to each input type that unveil the shape of the object hidden in the given point set. The reconstruction algorithms start with computing the Delaunay triangulation of the given point set and for each vertex, the Petal-Ratio is calculated. Following this, the edges are removed based on the value of the PR for every vertex to arrive at the boundaries. Theoretical analysis of determining appropriate values of PRs under ϵ- sampling and minimal reach sampling models have been presented. Unlike many other algorithms, our approach is non-parametric and non-feature specific that can capture features like disconnected components, multiple holes even with the presence of outliers, self-intersection (only for boundary sample) and open curves (only for boundary sample). Moreover, our algorithms take only one pass to reconstruct the hole boundaries as well as outer boundary irrespective of the object features like the number of holes and the number of components. The comparative analysis shows that our algorithms perform equally well or better than their counterparts for the objects with contrasting features. We also demonstrate that the proposed idea can be easily extended to surface reconstruction.

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An input-independent single pass algorithm for reconstruction from dot patterns and boundary samples

01-06-2020, Thayyil, Safeer Babu, Parakkat, Amal Dev, Muthuganapathy, Ramanathan

Given a set of points S∈R2, reconstruction is a process of identifying the boundary edges that best approximates the set of points. In general, the set of points can either be derived from only the boundaries of the curves (called as boundary sample) or can be derived from both boundary and interior of the curves (called as dot pattern). Most of the existing algorithms focus towards reconstruction from only boundary samples, termed as curve reconstruction. Unfortunately many of them don't reconstruct when the input is of dot pattern type (called as shape reconstruction). In this paper, we propose an input-independent non-parametric 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. An edge between a pair of triangles is marked for removal when the circumcenters lie on the same side of the edge. Further, we also propose additional criterion for removing edges based on characterizing a triangle by the distance between its circumcenter and incenter. To maintain a manifold output, a degree constraint is employed. 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 a variety of inputs such as multiple components, multiple holes etc. Extensive comparisons with state-of-the-art methods for various kinds of point sets including varying the sampling density and distribution show that our algorithm is either better or on par with them. Theoretical discussions on the algorithm have also been presented using ϵ-sampling and r-sampling. Limitations of the algorithm are also discussed.

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A non-parametric approach to shape reconstruction from planar point sets through Delaunay filtering

01-01-2015, Peethambaran, Jiju, Muthuganapathy Ramanathan

In this paper, we present a fully automatic Delaunay based sculpting algorithm for approximating the shape of a finite set of points S in R2. The algorithm generates a relaxed Gabriel graph (RGG) that consists of most of the Gabriel edges and a few non-Gabriel edges induced by the Delaunay triangulation. Holes are characterized through a structural pattern called as body-arm formed by the Delaunay triangles in the void regions. RGG is constructed through an iterative removal of Delaunay triangles subjected to circumcenter (of triangle) and topological regularity constraints in O(nlogn) time using O(n) space. We introduce the notion of directed boundary samples which characterizes the two dimensional objects based on the alignment of their boundaries in the cavities. Theoretically, we justify our algorithm by showing that under given sampling conditions, the boundary of RGG captures the topological properties of objects having directed boundary samples. Unlike many other approaches, our algorithm does not require tuning of any external parameter to approximate the geometric shape of point set and hence human intervention is completely eliminated. Experimental evaluations of the proposed technique are done using L2 error norm measure, which is the symmetric difference between the boundaries of reconstructed shape and the original shape. We demonstrate the efficacy of our automatic shape reconstruction technique by showing several examples and experiments with varying point set densities and distributions.

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Reconstruction of water-tight surfaces through Delaunay sculpting

01-01-2015, Peethambaran, Jiju, Muthuganapathy Ramanathan

Given a finite set of points S ⊆ R2, we define a proximity graph called as shape-hull graph (SHG(S)) that contains all Gabriel edges and a few non-Gabriel edges of Delaunay triangulation of S. For any S, SHG(S) is topologically regular with its boundary (referred to as shape-hull (SH)) homeomorphic to a simple closed curve. We introduce the concept of divergent concavity for simple, closed, planar curves based on the alignment of curves in concave portions and discuss various measures to characterize curves having divergent concavity. Under sufficiently dense sampling, we prove that SH(S), where S is sampled from a divergent concave curve ΣD, represents a piece-wise linear approximation of ΣD. We extend this result to provide a sculpting algorithm for closed surface reconstruction from a set of raw samples. The surface is constructed through a repeated elimination of Delaunay tetrahedra subjected to circumcenter and topological constraints. Theoretically, we justify our algorithm by establishing a topological guarantee on the 3D shape-hull with the help of topological rules. We demonstrate the effectiveness of our approach with experimental results on models with sharp features and sparsely distributed point clouds. Compared to existing sculpting approaches for surface reconstruction that require either a parameter tuning or several stages, our approach is simple, non-parametric, single stage and reconstructs topologically correct piece-wise linear approximation for divergent concave surfaces.

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Hole detection in a planar point set: An empty disk approach

01-08-2017, Methirumangalath, Subhasree, Kannan, Shyam Sundar, Dev Parakkat, Amal, Muthuganapathy, Ramanathan

Given a planar point set S, outer boundary detection (shape reconstruction) is an extensively studied problem whereas, inner boundary (hole) detection is not a well researched one, probably because detecting the presence of a hole itself is a difficult task. Nevertheless, hole detection has wide applications in areas such as face recognition, model retrieval and pattern recognition. We present a Delaunay triangulation based strategy to detect the presence of holes and an algorithm to reconstruct them. Our algorithm is a unified one which reconstructs holes, both for a boundary sample (points sampled only from the boundary of the object) as well as for a dot pattern (points sampled from the entire object). Our method is a non-parametric one which detects holes irrespective of its shape. Assuming a sampling model, we provide theoretical analysis of the proposed algorithm, which ensures the correctness of the reconstructed holes, for specific structures. We conduct both qualitative and quantitative comparisons with existing methods and demonstrate that our method is better or comparable with them. Experiments with varying point densities and distributions demonstrate that the algorithm is independent of sampling. We also discuss the limitations of the algorithm.