256 research outputs found
3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks
Standard 3D convolution operations require much larger amounts of memory and
computation cost than 2D convolution operations. The fact has hindered the
development of deep neural nets in many 3D vision tasks. In this paper, we
investigate the possibility of applying depthwise separable convolutions in 3D
scenario and introduce the use of 3D depthwise convolution. A 3D depthwise
convolution splits a single standard 3D convolution into two separate steps,
which would drastically reduce the number of parameters in 3D convolutions with
more than one order of magnitude. We experiment with 3D depthwise convolution
on popular CNN architectures and also compare it with a similar structure
called pseudo-3D convolution. The results demonstrate that, with 3D depthwise
convolutions, 3D vision tasks like classification and reconstruction can be
carried out with more light-weighted neural networks while still delivering
comparable performances.Comment: Work in progres
Effect of Temperature & Concentration on Dissolution Potentials of Sodium Chloride, Bromide & Iodide
344-34
Establishing the impact of luminous AGN with multi-wavelength observations and simulations
Cosmological simulations fail to reproduce realistic galaxy populations
without energy injection from active galactic nuclei (AGN) into the
interstellar medium (ISM) and circumgalactic medium (CGM); a process called
`AGN feedback'. Consequently, observational work searches for evidence that
luminous AGN impact their host galaxies. Here, we review some of this work.
Multi-phase AGN outflows are common, some with potential for significant
impact. Additionally, multiple feedback channels can be observed
simultaneously; e.g., radio jets from `radio quiet' quasars can inject
turbulence on ISM scales, and displace CGM-scale molecular gas. However,
caution must be taken comparing outflows to simulations (e.g., kinetic coupling
efficiencies) to infer feedback potential, due to a lack of comparable
predictions. Furthermore, some work claims limited evidence for feedback
because AGN live in gas-rich, star-forming galaxies. However, simulations do
not predict instantaneous, global impact on molecular gas or star formation.
The impact is expected to be cumulative, over multiple episodes.Comment: Accepted for publication in IAU Symposium 378 Conference Proceedings
"Black Hole Winds at all Scales
Dissolution Potentials of Sodium & Potassium Chlorides, Bromides & Iodides & of Ammonium Halides
561-56
Discrete Point Flow Networks for Efficient Point Cloud Generation
Generative models have proven effective at modeling 3D shapes and their
statistical variations. In this paper we investigate their application to point
clouds, a 3D shape representation widely used in computer vision for which,
however, only few generative models have yet been proposed. We introduce a
latent variable model that builds on normalizing flows with affine coupling
layers to generate 3D point clouds of an arbitrary size given a latent shape
representation. To evaluate its benefits for shape modeling we apply this model
for generation, autoencoding, and single-view shape reconstruction tasks. We
improve over recent GAN-based models in terms of most metrics that assess
generation and autoencoding. Compared to recent work based on continuous flows,
our model offers a significant speedup in both training and inference times for
similar or better performance. For single-view shape reconstruction we also
obtain results on par with state-of-the-art voxel, point cloud, and mesh-based
methods.Comment: In ECCV'2
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
The impressive performance of deep convolutional neural networks in
single-view 3D reconstruction suggests that these models perform non-trivial
reasoning about the 3D structure of the output space. However, recent work has
challenged this belief, showing that complex encoder-decoder architectures
perform similarly to nearest-neighbor baselines or simple linear decoder models
that exploit large amounts of per category data in standard benchmarks. On the
other hand settings where 3D shape must be inferred for new categories with few
examples are more natural and require models that generalize about shapes. In
this work we demonstrate experimentally that naive baselines do not apply when
the goal is to learn to reconstruct novel objects using very few examples, and
that in a \emph{few-shot} learning setting, the network must learn concepts
that can be applied to new categories, avoiding rote memorization. To address
deficiencies in existing approaches to this problem, we propose three
approaches that efficiently integrate a class prior into a 3D reconstruction
model, allowing to account for intra-class variability and imposing an implicit
compositional structure that the model should learn. Experiments on the popular
ShapeNet database demonstrate that our method significantly outperform existing
baselines on this task in the few-shot setting
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
We introduce an unsupervised feature learning approach that embeds 3D shape
information into a single-view image representation. The main idea is a
self-supervised training objective that, given only a single 2D image, requires
all unseen views of the object to be predictable from learned features. We
implement this idea as an encoder-decoder convolutional neural network. The
network maps an input image of an unknown category and unknown viewpoint to a
latent space, from which a deconvolutional decoder can best "lift" the image to
its complete viewgrid showing the object from all viewing angles. Our
class-agnostic training procedure encourages the representation to capture
fundamental shape primitives and semantic regularities in a data-driven
manner---without manual semantic labels. Our results on two widely-used shape
datasets show 1) our approach successfully learns to perform "mental rotation"
even for objects unseen during training, and 2) the learned latent space is a
powerful representation for object recognition, outperforming several existing
unsupervised feature learning methods.Comment: To appear at ECCV 201
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
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