3DOR 19

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https://diglib.eg.org/handle/10.2312/2632755

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Browsing 3DOR 19 by Subject "Computational geometry"

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    CMH: Coordinates Manifold Harmonics for Functional Remeshing
    (The Eurographics Association, 2019) Marin, Riccardo; Melzi, Simone; Musoni, Pietro; Bardon, Filippo; Tarini, Marco; Castellani, Umberto; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    In digital world reconstruction, 2-dimensional surface of real objects are often obtained as polygonal meshes after an acquisition procedure using 3D sensors. However, such representation requires several manual efforts from highly experts to correct the irregularity of tessellation and make it suitable for professional applications, such as those in the gaming or movie industry. Moreover, for modelling and animation purposes it is often required that the same connectivity is shared among two or more different shapes. In this paper we propose a new method that exploits a remeshing-by-matching approach where the observed noisy shape inherits a regular tessellation from a target shape which already satisfies the professional constraints. A fully automatic pipeline is introduced based on a variation of the functional mapping framework. In particular, a new set of basis functions, namely the Coordinates Manifold Harmonics (CMH), is properly designed for this tessellation transfer task. In our experiments an exhaustive quantitative and quality evaluation is reported for human body shapes in T-pose where the effectiveness of the proposed functional remeshing is clearly shown in comparison with other methods.
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    Matching Humans with Different Connectivity
    (The Eurographics Association, 2019) Melzi, S.; Marin, R.; Rodolà, E.; Castellani, U.; Ren, J.; Poulenard, A.; Wonka, P.; Ovsjanikov, M.; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    Objects Matching is a ubiquitous problem in computer science with particular relevance for many applications; property transfer between 3D models and statistical study for learning are just some remarkable examples. The research community spent a lot of effort to address this problem, and a large and increased set of innovative methods has been proposed for its solution. In order to provide a fair comparison among these methods, different benchmarks have been proposed. However, all these benchmarks are domain specific, e.g., real scans coming from the same acquisition pipeline, or synthetic watertight meshes with the same triangulation. To the best of our knowledge, no cross-dataset comparisons have been proposed to date. This track provides the first matching evaluation in terms of large connectivity changes between models that come from totally different modeling methods. We provide a dataset of 44 shapes with dense correspondence as obtained by a highly accurate shape registration method (FARM). Our evaluation proves that connectivity changes lead to Objects Matching difficulties and we hope this will promote further research in matching shapes with wildly different connectivity.
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    Shape Correspondence with Isometric and Non-Isometric Deformations
    (The Eurographics Association, 2019) Dyke, R. M.; Stride, C.; Lai, Y.-K.; Rosin, P. L.; Aubry, M.; Boyarski, A.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.; Fisher, M.; Groueix, T.; Guo, D.; Kim, V. G.; Kimmel, R.; Lähner, Z.; Li, K.; Litany, O.; Remez, T.; Rodolà, E.; Russell, B. C.; Sahillioglu, Y.; Slossberg, R.; Tam, G. K. L.; Vestner, M.; Wu, Z.; Yang, J.; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    The registration of surfaces with non-rigid deformation, especially non-isometric deformations, is a challenging problem. When applying such techniques to real scans, the problem is compounded by topological and geometric inconsistencies between shapes. In this paper, we capture a benchmark dataset of scanned 3D shapes undergoing various controlled deformations (articulating, bending, stretching and topologically changing), along with ground truth correspondences. With the aid of this tiered benchmark of increasingly challenging real scans, we explore this problem and investigate how robust current state-of- the-art methods perform in different challenging registration and correspondence scenarios. We discover that changes in topology is a challenging problem for some methods and that machine learning-based approaches prove to be more capable of handling non-isometric deformations on shapes that are moderately similar to the training set.