95,956 research outputs found
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
We propose a data-driven method for recovering miss-ing parts of 3D shapes.
Our method is based on a new deep learning architecture consisting of two
sub-networks: a global structure inference network and a local geometry
refinement network. The global structure inference network incorporates a long
short-term memorized context fusion module (LSTM-CF) that infers the global
structure of the shape based on multi-view depth information provided as part
of the input. It also includes a 3D fully convolutional (3DFCN) module that
further enriches the global structure representation according to volumetric
information in the input. Under the guidance of the global structure network,
the local geometry refinement network takes as input lo-cal 3D patches around
missing regions, and progressively produces a high-resolution, complete surface
through a volumetric encoder-decoder architecture. Our method jointly trains
the global structure inference and local geometry refinement networks in an
end-to-end manner. We perform qualitative and quantitative evaluations on six
object categories, demonstrating that our method outperforms existing
state-of-the-art work on shape completion.Comment: 8 pages paper, 11 pages supplementary material, ICCV spotlight pape
Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres
Interactions between clouds and radiation are at the root of many
difficulties in numerically predicting future weather and climate and in
retrieving the state of the atmosphere from remote sensing observations. The
large range of issues related to these interactions, and in particular to
three-dimensional interactions, motivated the development of accurate radiative
tools able to compute all types of radiative metrics, from monochromatic, local
and directional observables, to integrated energetic quantities. In the
continuity of this community effort, we propose here an open-source library for
general use in Monte Carlo algorithms. This library is devoted to the
acceleration of path-tracing in complex data, typically high-resolution
large-domain grounds and clouds. The main algorithmic advances embedded in the
library are those related to the construction and traversal of hierarchical
grids accelerating the tracing of paths through heterogeneous fields in
null-collision (maximum cross-section) algorithms. We show that with these
hierarchical grids, the computing time is only weakly sensitivive to the
refinement of the volumetric data. The library is tested with a rendering
algorithm that produces synthetic images of cloud radiances. Two other examples
are given as illustrations, that are respectively used to analyse the
transmission of solar radiation under a cloud together with its sensitivity to
an optical parameter, and to assess a parametrization of 3D radiative effects
of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
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