Browsing by Author "Rosin, Paul L."

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    Eurographics Workshop on 3D Object Retrieval: Short Papers Frontmatter
    (The Eurographics Association, 2021) Biasotti, Silvia; Dyke, Roberto M.; Lai, Yukun; Rosin, Paul L.; Veltkamp, Remco C.; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
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    An Image-based Approach for Detecting Faces Carved in Heritage Monuments
    (The Eurographics Association, 2018) Lai, Yu-Kun; Echavarria, Karina Rodriguez; Song, Ran; Rosin, Paul L.; Sablatnig, Robert and Wimmer, Michael
    Heritage monuments such as columns, memorials and buildings are typically carved with a variety of visual features, including figural content, illustrating scenes from battles or historical narratives. Understanding such visual features is of interest to heritage professionals as it can facilitate the study of such monuments and their conservation. However, this visual analysis can be challenging due to the large-scale size, the amount of carvings and difficulty of access to monuments across the world. This paper makes a contribution towards this goal by presenting work-in-progress for developing image-based approaches for detecting visual features in 3D models, in particular of human faces. The motivation for focusing on faces is the prominence of human figures throughout monuments in the world. The methods are tested on a 3D model of a section of the Trajan Column cast at the Victoria and Albert (V&A) Museum in London, UK. The initial results suggest that methods based on machine learning can provide useful tools for heritage professionals to deal with the large-scale challenges presented by such large monuments.
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    An Image-based Model for 3D Shape Quality Measure
    (The Eurographics Association, 2023) Alhamazani, Fahd; Rosin, Paul L.; Lai, Yu-Kun; Vangorp, Peter; Hunter, David
    In light of increased research on 3D shapes and the increased processing capability of GPUs, there has been a significant increase in available 3D applications. In many applications, assessment of perceptual quality of 3D shapes is required. Due to the nature of 3D representation, this quality assessment may take various forms. While it is straightforward to measure geometric distortions directly on the 3D shape geometry, such measures are often inconsistent with human perception of quality. In most cases, human viewers tend to perceive 3D shapes from their 2D renderings. It is therefore plausible to measure shape quality using their 2D renderings. In this paper, we present an image-based quality metric for evaluating 3D shape quality given the original and distorted shapes. To provide a good coverage of 3D geometry from different views, we render each shape from 12 equally spaced views, along with a variety of rendering styles to capture different aspects of visual characteristics. Image-based metrics such as SSIM (Structure Similarity Index Measure) are then used to measure the quality of 3D shapes. Our experiments show that by effectively selecting a suitable combination of rendering styles and building a neural network based model, we achieve significantly better prediction for subjective perceptual quality than existing methods.
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    SHREC 2020 Track: Non-rigid Shape Correspondence of Physically-Based Deformations
    (The Eurographics Association, 2020) Dyke, Roberto M.; Zhou, Feng; Lai, Yu-Kun; Rosin, Paul L.; Guo, Daoliang; Li, Kun; Marin, Riccardo; Yang, Jingyu; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Commonly, novel non-rigid shape correspondence techniques focus on particular matching challenges. This can lead to the potential trade-off of poorer performance in other scenarios. An ideal dataset would provide a granular means for degrees of evaluation. In this paper, we propose a novel dataset of real scans that contain challenging non-isometric deformations to evaluate non-rigid point-to-point correspondence and registration algorithms. The deformations included in our dataset cover extreme types of physically-based contortions of a toy rabbit. Furthermore, shape pairs contain incrementally different types and amounts of deformation, this enables performance to be systematically evaluated with respect to the nature of the deformation. A brief investigation into different methods for initialising correspondence was undertaken, and a series of experiments were subsequently conducted to investigate the performance of state-of-the-art methods on the proposed dataset. We find that methods that rely on initial correspondences and local descriptors that are sensitive to local surface changes perform poorly in comparison to other strategies, and that a template-based approach performs the best.