27,743 research outputs found
View subspaces for indexing and retrieval of 3D models
View-based indexing schemes for 3D object retrieval are gaining popularity
since they provide good retrieval results. These schemes are coherent with the
theory that humans recognize objects based on their 2D appearances. The
viewbased techniques also allow users to search with various queries such as
binary images, range images and even 2D sketches. The previous view-based
techniques use classical 2D shape descriptors such as Fourier invariants,
Zernike moments, Scale Invariant Feature Transform-based local features and 2D
Digital Fourier Transform coefficients. These methods describe each object
independent of others. In this work, we explore data driven subspace models,
such as Principal Component Analysis, Independent Component Analysis and
Nonnegative Matrix Factorization to describe the shape information of the
views. We treat the depth images obtained from various points of the view
sphere as 2D intensity images and train a subspace to extract the inherent
structure of the views within a database. We also show the benefit of
categorizing shapes according to their eigenvalue spread. Both the shape
categorization and data-driven feature set conjectures are tested on the PSB
database and compared with the competitor view-based 3D shape retrieval
algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications
(Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M.
Baskurt ISBN: 9780819479198 Date: 2 February 201
Dynamic 3D shape measurement based on the phase-shifting moir\'e algorithm
In order to increase the efficiency of phase retrieval,Wang proposed a
high-speed moire phase retrieval method.But it is used only to measure the tiny
object. In view of the limitation of Wang method,we proposed a dynamic
three-dimensional (3D) measurement based on the phase-shifting moire
algorithm.First, four sinusoidal fringe patterns with a pi/2 phase-shift are
projected on the reference plane and acquired four deformed fringe patterns of
the reference plane in advance. Then only single-shot deformed fringe pattern
of the tested object is captured in measurement process.Four moire fringe
patterns can be obtained by numerical multiplication between the the AC
component of the object pattern and the AC components of the reference patterns
respectively. The four low-frequency components corresponding to the moire
fringe patterns are calculated by the complex encoding FT (Fourier transform)
,spectrum filtering and inverse FT.Thus the wrapped phase of the object can be
determined in the tangent form from the four phase-shifting moire fringe
patterns using the four-step phase shifting algorithm.The continuous phase
distribution can be obtained by the conventional unwrapping algorithm. Finally,
experiments were conducted to prove the validity and feasibility of the
proposed method. The results are analyzed and compared with those of Wang
method, demonstrating that our method not only can expand the measurement
scope, but also can improve accuracy.Comment: 14 pages,5 figures. ams.or
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatically obtained by a structure-from-motion pipeline. The learned
representation is evaluated on a range of different tasks, providing
improvements on challenging cases of domain generalization, generic
sketch-based image retrieval or its fine-grained counterpart. In contrast to
other methods that learn a different model per task, object category, or
domain, we use the same network throughout all our experiments, achieving
state-of-the-art results in multiple benchmarks.Comment: ECCV 201
From 3D Point Clouds to Pose-Normalised Depth Maps
We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)
Spherical harmonics coeffcients for ligand-based virtual screening of cyclooxygenase inhibitors
Background: Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. Methodology/Principal Findings: We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. Conclusions/Significance: 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort
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
One shot profilometry using iterative two-step temporal phase-unwrapping
This paper reviews two techniques that have been recently published for 3D
profilometry and proposes one shot profilometry using iterative two-step
temporal phase-unwrapping by combining the composite fringe projection and the
iterative two-step temporal phase unwrapping algorithm. In temporal phase
unwrapping, many images with different frequency fringe pattern are needed to
project which would take much time. In order to solve this problem, Ochoa
proposed a phase unwrapping algorithm based on phase partitions using a
composite fringe, which only needs projecting one composite fringe pattern with
four kinds of frequency information to complete the process of 3D profilometry.
However, we found that the fringe order determined through the construction of
phase partitions tended to be imprecise. Recently, we proposed an iterative
two-step temporal phase unwrapping algorithm, which can achieve high
sensitivity and high precision shape measurement. But it needs multiple frames
of fringe images which would take much time. In order to take into account both
the speed and accuracy of 3D shape measurement, we get a new, and more accurate
unwrapping method based on composite fringe pattern by combining these two
techniques. This method not only retains the speed advantage of Ochoa's
algorithm, but also greatly improves its measurement accuracy. Finally, the
experimental evaluation is conducted to prove the validity of the proposed
method, and the experimental results show that this method is feasible.Comment: 14 pages, 15 figure
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