8,571 research outputs found
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping framework
for image registration to the indirect setting where a template is registered
against a target that is given through indirect noisy observations. The
registration uses diffeomorphisms that transform the template through a (group)
action. These diffeomorphisms are generated by solving a flow equation that is
defined by a velocity field with certain regularity. The theoretical analysis
includes a proof that indirect image registration has solutions (existence)
that are stable and that converge as the data error tends so zero, so it
becomes a well-defined regularization method. The paper concludes with examples
of indirect image registration in 2D tomography with very sparse and/or highly
noisy data.Comment: 43 pages, 4 figures, 1 table; revise
Active Image-based Modeling with a Toy Drone
Image-based modeling techniques can now generate photo-realistic 3D models
from images. But it is up to users to provide high quality images with good
coverage and view overlap, which makes the data capturing process tedious and
time consuming. We seek to automate data capturing for image-based modeling.
The core of our system is an iterative linear method to solve the multi-view
stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our
fast MVS algorithm enables online model reconstruction and quality assessment
to determine the NBVs on the fly. We test our system with a toy unmanned aerial
vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that
our system improves the efficiency of data acquisition and ensures the
completeness of the final model.Comment: To be published on International Conference on Robotics and
Automation 2018, Brisbane, Australia. Project Page:
https://huangrui815.github.io/active-image-based-modeling/ The author's
personal page: http://www.sfu.ca/~rha55
Learning Shape Priors for Single-View 3D Completion and Reconstruction
The problem of single-view 3D shape completion or reconstruction is
challenging, because among the many possible shapes that explain an
observation, most are implausible and do not correspond to natural objects.
Recent research in the field has tackled this problem by exploiting the
expressiveness of deep convolutional networks. In fact, there is another level
of ambiguity that is often overlooked: among plausible shapes, there are still
multiple shapes that fit the 2D image equally well; i.e., the ground truth
shape is non-deterministic given a single-view input. Existing fully supervised
approaches fail to address this issue, and often produce blurry mean shapes
with smooth surfaces but no fine details.
In this paper, we propose ShapeHD, pushing the limit of single-view shape
completion and reconstruction by integrating deep generative models with
adversarially learned shape priors. The learned priors serve as a regularizer,
penalizing the model only if its output is unrealistic, not if it deviates from
the ground truth. Our design thus overcomes both levels of ambiguity
aforementioned. Experiments demonstrate that ShapeHD outperforms state of the
art by a large margin in both shape completion and shape reconstruction on
multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://shapehd.csail.mit.edu
Semantic 3D Reconstruction with Finite Element Bases
We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Starting from the popular formulation of labeling as a convex relaxation
by functional lifting, we show that FEM discretisation is valid for the most
general case, where the regulariser is anisotropic and non-metric. While our
findings are generic and applicable to different vision problems, we
demonstrate their practical implementation in the context of semantic 3D
reconstruction, where such regularisers have proved particularly beneficial.
The proposed FEM approach leads to a smaller memory footprint as well as faster
computation, and it constitutes a very simple way to enable variable, adaptive
resolution within the same model
Coarse-grained entropy and causal holographic information in AdS/CFT
We propose bulk duals for certain coarse-grained entropies of boundary
regions. The `one-point entropy' is defined in the conformal field theory by
maximizing the entropy in a domain of dependence while fixing the one-point
functions. We conjecture that this is dual to the area of the edge of the
region causally accessible to the domain of dependence (i.e. the `causal
holographic information' of Hubeny and Rangamani). The `future one-point
entropy' is defined by generalizing this conjecture to future domains of
dependence and their corresponding bulk regions. We show that the future
one-point entropy obeys a nontrivial second law. If our conjecture is true,
this answers the question "What is the field theory dual of Hawking's area
theorem?"Comment: 43 pages, 9 figures. v3: minor changes suggested by referee v2: added
a few additional reference
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