Italian Chapter Conference 2022 - Smart Tools and Apps in Graphics
Permanent URI for this collection
Browse
Browsing Italian Chapter Conference 2022 - Smart Tools and Apps in Graphics by Author "Melzi, Simone"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item GIM3D: A 3D Dataset for Garment Segmentation(The Eurographics Association, 2022) Musoni, Pietro; Melzi, Simone; Castellani, Umberto; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, RiccardoThe 3D cloth segmentation task is particularly challenging due to the extreme variation of shapes, even among the same category of clothes. Several data-driven methods try to cope with this problem but they have to face the lack of available data capable to generalize to the variety of real-world data. For this reason, we present GIM3D (Garments In Motion 3D), a synthetic dataset of clothed 3D human characters in different poses. The over 4000 3D models in this dataset are produced by a physical simulation of clothes with different fabrics, sizes, and tightness, using animated human avatars having a large variety of shapes. Our dataset is composed of single meshes created to simulate 3D scans, with labels for the separate clothes and the visible body parts. We also provide an evaluation of the use of GIM3D as a training set on garment segmentation tasks using state-of-the-art data-driven methods for both meshes and point clouds.Item PC-GAU: PCA Basis of Scattered Gaussians for Shape Matching via Functional Maps(The Eurographics Association, 2022) Colombo, Michele; Boracchi, Giacomo; Melzi, Simone; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, RiccardoShape matching is a central problem in geometry processing applications, ranging from texture transfer to statistical shape analysis. The functional maps framework provides a compact representation of correspondences between discrete surfaces, which is then converted into point-wise maps required by real-world applications. The vast majority of methods based on functional maps involve the eigenfunctions of the Laplace-Beltrami Operator (LB) as the functional basis. A primary drawback of the LB basis is that its energy does not uniformly cover the surface. This fact gives rise to regions where the estimated correspondences are inaccurate, typically at tiny parts and protrusions. For this reason, state-of-the-art procedures to convert the functional maps (represented in the LB basis) into point-wise correspondences are often error-prone. We propose PCGAU, a new functional basis whose energy spreads on the whole shape more evenly than LB. As such, PC-GAU can replace the LB basis in existing shape matching pipelines. PC-GAU consists of the principal vectors obtained by applying Principal Component Analysis (PCA) to a dictionary of sparse Gaussian functions scattered on the surfaces. Through experimental evaluation of established benchmarks, we show that our basis produces more accurate point-wise maps —- compared to LB - when employed in the same shape-matching pipeline.