Browsing by Author "Sipiran, Ivan"

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    A Benchmark Dataset for Repetitive Pattern Recognition on Textured 3D Surfaces
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Lengauer, Stefan; Sipiran, Ivan; Preiner, Reinhold; Schreck, Tobias; Bustos, Benjamin; Digne, Julie and Crane, Keenan
    In digital archaeology, a large research area is concerned with the computer-aided analysis of 3D captured ancient pottery objects. A key aspect thereby is the analysis of motifs and patterns that were painted on these objects' surfaces. In particular, the automatic identification and segmentation of repetitive patterns is an important task serving different applications such as documentation, analysis and retrieval. Such patterns typically contain distinctive geometric features and often appear in repetitive ornaments or friezes, thus exhibiting a significant amount of symmetry and structure. At the same time, they can occur at varying sizes, orientations and irregular placements, posing a particular challenge for the detection of similarities. A key prerequisite to develop and evaluate new detection approaches for such repetitive patterns is the availability of an expressive dataset of 3D models, defining ground truth sets of similar patterns occurring on their surfaces. Unfortunately, such a dataset has not been available so far for this particular problem. We present an annotated dataset of 82 different 3D models of painted ancient Peruvian vessels, exhibiting different levels of repetitiveness in their surface patterns. To serve the evaluation of detection techniques of similar patterns, our dataset was labeled by archaeologists who identified clearly definable pattern classes. Those given, we manually annotated their respective occurrences on the mesh surfaces. Along with the data, we introduce an evaluation benchmark that can rank different recognition techniques for repetitive patterns based on the mean average precision of correctly segmented 3D mesh faces. An evaluation of different incremental sampling-based detection approaches, as well as a domain specific technique, demonstrates the applicability of our benchmark. With this benchmark we especially want to address the geometry processing community, and expect it will induce novel approaches for pattern analysis based on geometric reasoning like 2D shape and symmetry analysis. This can enable novel research approaches in the Digital Humanities and related fields, based on digitized 3D Cultural Heritage artifacts. Alongside the source code for our evaluation scripts we provide our annotation tools for the public to extend the benchmark and further increase its variety.
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    Context-based Surface Pattern Completion of Ancient Pottery
    (The Eurographics Association, 2022) Lengauer, Stefan; Preiner, Reinhold; Sipiran, Ivan; Karl, Stephan; Trinkl, Elisabeth; Bustos, Benjamin; Schreck, Tobias; Ponchio, Federico; Pintus, Ruggero
    Among various ancient cultures it was common practice to adorn pottery artifacts with lavish surface decoration. While the applied painting styles, color schemes and displayed mythological content may vary greatly, the presence of simple patterns which appear in a repetitive manner can be observed across civilizations and periods. Such pattern sequences generally are arranged in a structured manner in ornament bands or columns that extend over the entire surface of the object. Due to the poor conservation state of many cultural heritage objects, parts of the surface are oftentimes badly damaged or missing altogether. Yet, if the majority of a pattern sequence is preserved, this information can be leveraged to approximate its missing parts. We present an approach that allows the fully automatic determination of the generation rule inherent to a repetitive surface pattern. Based on this generation rule and the preserved patterns from the same pattern class we propose a workflow for reconstruct missing or damaged parts of the surface painting. We evaluate our approach by applying it to a selection of pottery from ancient Peruvian and Greek cultures, showing that our automatic approach is able to handle a variety of problem cases.
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    Semi-automated Annotation of Repetitive Ornaments on 3D Painted Pottery Surfaces
    (The Eurographics Association, 2020) Lengauer, Stefan; Komar, Alexander; Karl, Stephan; Trinkl, Elisabeth; Sipiran, Ivan; Schreck, Tobias; Preiner, Reinhold; Spagnuolo, Michela and Melero, Francisco Javier
    The creation of drawings from the surface of painted pottery artifacts is an important practice in archaeological research and documentation. Traditional approaches include manual drawings using pen and paper, either directly on the physical surface, or from photographs, while more recent approaches are supported by photography or flattening of 3D digitized objects. Elaborate vase paintings, mostly showing figural scenes, often comprise ornamental decorations in secondary position or in the background, exhibiting repetitive patterns. We propose a tool supporting the creation of archaeological drawings with a semi-automatic extraction of ornamental surface sections, based on a combination of user-defined queries and self-similarity detection. Appropriate heuristics allow to detect the presence and positions of ornamental bands, a frequently occurring scheme, where ornamental primitives are evenly spaced along the tangential direction of a vessel's solid of revolution. Our interactive tool allows domain experts to efficiently select ornamental queries, and assess the quality of resulting similarity detections. First experiments with real world artifacts from ancient Greek and Peruvian cultures confirm the feasibility of the approach.
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    SHREC 2023: Detection of Symmetries on 3D Point Clouds Representing Simple Shapes
    (The Eurographics Association, 2023) Sipiran, Ivan; Romanengo, Chiara; Falcidieno, Bianca; Biasotti, Silvia; Arvanitis, Gerasimos; Chen, Chen; Fotis, Vlassis; He, Jianfang; Lv, Xiaoling; Moustakas, Konstantinos; Peng, Silong; Romanelis, Ioannis; Sun, Wenhao; Vlachos, Christoforos; Wu, Ziyu; Xie, Qiong; Fugacci, Ulderico; Lavoué, Guillaume; Veltkamp, Remco C.
    This paper presents the methods that participated in the SHREC 2023 track focused on detecting symmetries on 3D point clouds representing simple shapes. By simple shapes, we mean surfaces generated by different types of closed plane curves used as the directrix of a cylinder or a cone. This track aims to determine the reflective planes for each point cloud. The methods are evaluated in their capability of detecting the right number of symmetries and correctly identifying the reflective planes. To this end, we generated a dataset that contains point clouds representing simple shapes perturbed with different kinds of artefacts (such as noise and undersampling) to provide a thorough evaluation of the robustness of the algorithms.