3,253 research outputs found
Smooth Interpolation of Curve Networks with Surface Normals
International audienceRecent surface acquisition technologies based on microsensors produce three-space tangential curve data which can be transformed into a network of space curves with surface normals. This paper addresses the problem of surfacing an arbitrary closed 3D curve network with given surface normals.Thanks to the normal vector input, the patch finding problem can be solved unambiguously and an initial piecewise smooth triangle mesh is computed. The input normals are propagated throughout the mesh and used to compute mean curvature vectors. We then introduce a new variational optimization method in which the standard bi-Laplacian is penalized by a term based on the mean curvature vectors. The intuition behind this original approach is to guide the standard Laplacian-based variational methods by the curvature information extracted from the input normals. The normal input increases shape fidelity and allows to achieve globally smooth and visually pleasing shapes
Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches
We present Strokes2Surface, an offline geometry reconstruction pipeline that
recovers well-connected curve networks from imprecise 4D sketches to bridge
concept design and digital modeling stages in architectural design. The input
to our pipeline consists of 3D strokes' polyline vertices and their timestamps
as the 4th dimension, along with additional metadata recorded throughout
sketching. Inspired by architectural sketching practices, our pipeline combines
a classifier and two clustering models to achieve its goal. First, with a set
of extracted hand-engineered features from the sketch, the classifier
recognizes the type of individual strokes between those depicting boundaries
(Shape strokes) and those depicting enclosed areas (Scribble strokes). Next,
the two clustering models parse strokes of each type into distinct groups, each
representing an individual edge or face of the intended architectural object.
Curve networks are then formed through topology recovery of consolidated Shape
clusters and surfaced using Scribble clusters guiding the cycle discovery. Our
evaluation is threefold: We confirm the usability of the Strokes2Surface
pipeline in architectural design use cases via a user study, we validate our
choice of features via statistical analysis and ablation studies on our
collected dataset, and we compare our outputs against a range of
reconstructions computed using alternative methods.Comment: 15 pages, 14 figure
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
We propose a method for reconstructing 3D shapes from 2D sketches in the form
of line drawings. Our method takes as input a single sketch, or multiple
sketches, and outputs a dense point cloud representing a 3D reconstruction of
the input sketch(es). The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder network. The encoder
converts the sketch into a compact representation encoding shape information.
The decoder converts this representation into depth and normal maps capturing
the underlying surface from several output viewpoints. The multi-view maps are
then consolidated into a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on our experiments,
compared to other methods, such as volumetric networks, our architecture offers
several advantages, including more faithful reconstruction, higher output
surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral
Collective motion, sensor networks, and ocean sampling
Author Posting. © IEEE, 2007. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in Proceedings of the IEEE 95 (2007): 48-74, doi:10.1109/jproc.2006.887295.This paper addresses the design of mobile sensor
networks for optimal data collection. The development is
strongly motivated by the application to adaptive ocean
sampling for an autonomous ocean observing and prediction
system. A performance metric, used to derive optimal paths for
the network of mobile sensors, defines the optimal data set as
one which minimizes error in a model estimate of the sampled
field. Feedback control laws are presented that stably coordinate
sensors on structured tracks that have been optimized
over a minimal set of parameters. Optimal, closed-loop solutions
are computed in a number of low-dimensional cases to
illustrate the methodology. Robustness of the performance to
the influence of a steady flow field on relatively slow-moving
mobile sensors is also explored
Pull and Push: Strengthening Demand for Innovation in Education
Examines policy, information, and cultural barriers that minimize the "demand pull" for educational innovation. Calls for encouraging early adopters, bolstering smart adoption, providing better information, and rewarding productivity improvements
A Note on Ribbon-based Biharmonic Surface Patches
In this short note we describe a simple adaptation of biharmonic surfaces to
interpolate boundary cross-derivatives given in ribbon form, and compare with
the recently proposed Generalized B-spline patches
PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing
Surface reconstruction with preservation of geometric features is a
challenging computer vision task. Despite significant progress in implicit
shape reconstruction, state-of-the-art mesh extraction methods often produce
aliased, perceptually distorted surfaces and lack scalability to
high-resolution 3D shapes. We present a data-driven approach for automatic
feature detection and remeshing that requires only a coarse, aliased mesh as
input and scales to arbitrary resolution reconstructions. We define and learn a
collection of surface-based fields to (1) capture sharp geometric features in
the shape with an implicit vertexwise model and (2) approximate improvements in
normals alignment obtained by applying edge-flips with an edgewise model. To
support scaling to arbitrary complexity shapes, we learn our fields using local
triangulated patches, fusing estimates on complete surface meshes. Our feature
remeshing algorithm integrates the learned fields as sharp feature priors and
optimizes vertex placement and mesh connectivity for maximum expected surface
improvement. On a challenging collection of high-resolution shape
reconstructions in the ABC dataset, our algorithm improves over
state-of-the-art by 26% normals F-score and 42% perceptual
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