Browsing by Author "Averkiou, Melinos"

Now showing 1 - 3 of 3
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    Cross-Shape Attention for Part Segmentation of 3D Point Clouds
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Loizou, Marios; Garg, Siddhant; Petrov, Dmitry; Averkiou, Melinos; Kalogerakis, Evangelos; Memari, Pooran; Solomon, Justin
    We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable interactions between a shape's point-wise features and those of other shapes. The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation. Our method also proposes a shape retrieval measure to select suitable shapes for crossshape attention operations for each test shape. Our experiments demonstrate that our approach yields state-of-the-art results in the popular PartNet dataset.
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    EUROGRAPHICS 2024: Posters Frontmatter
    (Eurographics Association, 2024) Liu, Lingjie; Averkiou, Melinos; Liu, Lingjie; Averkiou, Melinos
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    Learning Part Boundaries from 3D Point Clouds
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Loizou, Marios; Averkiou, Melinos; Kalogerakis, Evangelos; Jacobson, Alec and Huang, Qixing
    We present a method that detects boundaries of parts in 3D shapes represented as point clouds. Our method is based on a graph convolutional network architecture that outputs a probability for a point to lie in an area that separates two or more parts in a 3D shape. Our boundary detector is quite generic: it can be trained to localize boundaries of semantic parts or geometric primitives commonly used in 3D modeling. Our experiments demonstrate that our method can extract more accurate boundaries that are closer to ground-truth ones compared to alternatives. We also demonstrate an application of our network to fine-grained semantic shape segmentation, where we also show improvements in terms of part labeling performance.