1,325 research outputs found
Robust Inside-Outside Segmentation Using Generalized Winding Numbers
Solid shapes in computer graphics are often represented with boundary descriptions, e.g. triangle meshes, but animation, physicallybased simulation, and geometry processing are more realistic and accurate when explicit volume representations are available. Tetrahedral meshes which exactly contain (interpolate) the input boundary description are desirable but difficult to construct for a large class of input meshes. Character meshes and CAD models are often composed of many connected components with numerous selfintersections, non-manifold pieces, and open boundaries, precluding existing meshing algorithms. We propose an automatic algorithm handling all of these issues, resulting in a compact discretization of the input’s inner volume. We only require reasonably consistent orientation of the input triangle mesh. By generalizing the winding number for arbitrary triangle meshes, we define a function that is a perfect segmentation for watertight input and is well-behaved otherwise. This function guides a graphcut segmentation of a constrained Delaunay tessellation (CDT), providing a minimal description that meets the boundary exactly and may be fed as input to existing tools to achieve element quality. We highlight our robustness on a number of examples and show applications of solving PDEs, volumetric texturing and elastic simulation
Consistently Orienting Facets in Polygon Meshes by Minimizing the Dirichlet Energy of Generalized Winding Numbers
Jacobson et al. [JKSH13] hypothesized that the local coherency of the
generalized winding number function could be used to correctly determine
consistent facet orientations in polygon meshes. We report on an approach to
consistently orienting facets in polygon meshes by minimizing the Dirichlet
energy of generalized winding numbers. While the energy can be concisely
formulated and efficiently computed, we found that this approach is
fundamentally flawed and is unfortunately not applicable for most handmade
meshes shared on popular mesh repositories such as Google 3D Warehouse.Comment: 6 pages, 4 figure
Layered-Garment Net: Generating Multiple Implicit Garment Layers from a Single Image
Recent research works have focused on generating human models and garments
from their 2D images. However, state-of-the-art researches focus either on only
a single layer of the garment on a human model or on generating multiple
garment layers without any guarantee of the intersection-free geometric
relationship between them. In reality, people wear multiple layers of garments
in their daily life, where an inner layer of garment could be partially covered
by an outer one. In this paper, we try to address this multi-layer modeling
problem and propose the Layered-Garment Net (LGN) that is capable of generating
intersection-free multiple layers of garments defined by implicit function
fields over the body surface, given the person's near front-view image. With a
special design of garment indication fields (GIF), we can enforce an implicit
covering relationship between the signed distance fields (SDF) of different
layers to avoid self-intersections among different garment surfaces and the
human body. Experiments demonstrate the strength of our proposed LGN framework
in generating multi-layer garments as compared to state-of-the-art methods. To
the best of our knowledge, LGN is the first research work to generate
intersection-free multiple layers of garments on the human body from a single
image.Comment: 16th Asian Conference on Computer Vision (ACCV2022
NASA: Neural Articulated Shape Approximation
Efficient representation of articulated objects such as human bodies is an
important problem in computer vision and graphics. To efficiently simulate
deformation, existing approaches represent 3D objects using polygonal meshes
and deform them using skinning techniques. This paper introduces neural
articulated shape approximation (NASA), an alternative framework that enables
efficient representation of articulated deformable objects using neural
indicator functions that are conditioned on pose. Occupancy testing using NASA
is straightforward, circumventing the complexity of meshes and the issue of
water-tightness. We demonstrate the effectiveness of NASA for 3D tracking
applications, and discuss other potential extensions.Comment: ECCV 202
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