EG 2025 - STARs (CGF 44-2)

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https://diglib.eg.org/handle/10.2312/3607139

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Browsing EG 2025 - STARs (CGF 44-2) by Subject "Shape analysis"

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    State-of-the-art Report in Sketch Processing
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Liu, Chenxi; Bessmeltsev, Mikhail; Memari, Pooran; Gryaditskaya, Yulia
    Sketches are a powerful and natural form of communication and are used in numerous systems for modelling, animation, shape retrieval, and editing. Despite their popularity, rough sketches - whether raster or vector, 2D or 3D - are often too complex and imprecise to be used directly and thus need special processing. For instance, many downstream applications, such as shape reconstruction, have strict requirements for cleanliness and accuracy of the input sketch. Alternatively, if a drawing is the final result, users might want to further process the sketch through tasks such as vectorization, beautification, cleanup, flat colorization, and more. In this state-of-the-art report, we identify core geometrical and topological challenges shared by many processing methods, such as identifying endpoints, strokes, and junctions. Building upon that analysis, we then survey sketch processing methods in each task category. Furthermore, we outline the commonly used sketch datasets and promising avenues for future research in sketch processing.
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    A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Lu, Jiaxin; Liang, Yongqing; Han, Hunjun; Hua, Jiacheng; Jiang, Junfeng; Li, Xin; Huang, Qixing; Memari, Pooran; Gryaditskaya, Yulia
    Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics.