20,413 research outputs found
Simple and Robust Boolean Operations for Triangulated Surfaces
Boolean operations of geometric models is an essential issue in computational
geometry. In this paper, we develop a simple and robust approach to perform
Boolean operations on closed and open triangulated surfaces. Our method mainly
has two stages: (1) We firstly find out candidate intersected-triangles pairs
based on Octree and then compute the inter-section lines for all pairs of
triangles with parallel algorithm; (2) We form closed or open
intersection-loops, sub-surfaces and sub-blocks quite robustly only according
to the cleared and updated topology of meshes while without coordinate
computations for geometric enti-ties. A novel technique instead of
inside/outside classification is also proposed to distinguish the resulting
union, subtraction and intersection. Several examples have been given to
illus-trate the effectiveness of our approach.Comment: Novel method for determining Union, Subtraction and Intersectio
Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes
We present a novel approach to reconstruct RGB-D indoor scene with plane
primitives. Our approach takes as input a RGB-D sequence and a dense coarse
mesh reconstructed by some 3D reconstruction method on the sequence, and
generate a lightweight, low-polygonal mesh with clear face textures and sharp
features without losing geometry details from the original scene. To achieve
this, we firstly partition the input mesh with plane primitives, simplify it
into a lightweight mesh next, then optimize plane parameters, camera poses and
texture colors to maximize the photometric consistency across frames, and
finally optimize mesh geometry to maximize consistency between geometry and
planes. Compared to existing planar reconstruction methods which only cover
large planar regions in the scene, our method builds the entire scene by
adaptive planes without losing geometry details and preserves sharp features in
the final mesh. We demonstrate the effectiveness of our approach by applying it
onto several RGB-D scans and comparing it to other state-of-the-art
reconstruction methods.Comment: in International Conference on 3D Vision 2018; Models and Code: see
https://github.com/chaowang15/plane-opt-rgbd. arXiv admin note: text overlap
with arXiv:1905.0885
Efficient moving point handling for incremental 3D manifold reconstruction
As incremental Structure from Motion algorithms become effective, a good
sparse point cloud representing the map of the scene becomes available
frame-by-frame. From the 3D Delaunay triangulation of these points,
state-of-the-art algorithms build a manifold rough model of the scene. These
algorithms integrate incrementally new points to the 3D reconstruction only if
their position estimate does not change. Indeed, whenever a point moves in a 3D
Delaunay triangulation, for instance because its estimation gets refined, a set
of tetrahedra have to be removed and replaced with new ones to maintain the
Delaunay property; the management of the manifold reconstruction becomes thus
complex and it entails a potentially big overhead. In this paper we investigate
different approaches and we propose an efficient policy to deal with moving
points in the manifold estimation process. We tested our approach with four
sequences of the KITTI dataset and we show the effectiveness of our proposal in
comparison with state-of-the-art approaches.Comment: Accepted in International Conference on Image Analysis and Processing
(ICIAP 2015
Animating Human Muscle Structure
Graphical simulations of human muscle motion and deformation are of great interest to
medical education. In this article, the authors present a technique for simulating muscle
deformations by combining physically and geometrically based computations to reduce
computation cost and produce fast, accurate simulations
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Among the many possible approaches for the parallelization of self-organizing
networks, and in particular of growing self-organizing networks, perhaps the
most common one is producing an optimized, parallel implementation of the
standard sequential algorithms reported in the literature. In this paper we
explore an alternative approach, based on a new algorithm variant specifically
designed to match the features of the large-scale, fine-grained parallelism of
GPUs, in which multiple input signals are processed at once. Comparative tests
have been performed, using both parallel and sequential implementations of the
new algorithm variant, in particular for a growing self-organizing network that
reconstructs surfaces from point clouds. The experimental results show that
this approach allows harnessing in a more effective way the intrinsic
parallelism that the self-organizing networks algorithms seem intuitively to
suggest, obtaining better performances even with networks of smaller size.Comment: 17 page
Real-time diffuse optical tomography using reduced-order light propagation models based on a priori anatomical and functional information
This paper proposes a new fast 3D image reconstruction
algorithm for Diffuse Optical Tomography using reduced
order polynomial mappings from the space of optical
tissue parameters into the space of flux measurements at
the detector locations. The polynomial mappings are
constructed through an iterative estimation process
involving structure detection, parameter estimation and
cross-validation using data generated by simulating a
diffusion approximation of the radiative transfer equation
incorporating a priori anatomical and functional
information provided by MR scans and prior psychological
evidence. Numerical simulation studies demonstrate that
reconstructed images are remarkably similar in quality as
those obtained using the standard approach, but obtained at
a fraction of the time
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