4,812 research outputs found
Compensation of Beer-Lambert attenuation using non-diffracting Bessel beams
We report on a versatile method to compensate the linear attenuation in a
medium, independently of its microscopic origin. The method exploits
diffraction-limited Bessel beams and tailored on-axis intensity profiles which
are generated using a phase-only spatial light modulator. This technique for
compensating one of the most fundamental limiting processes in linear optics is
shown to be efficient for a wide range of experimental conditions (modifying
the refractive index and the attenuation coefficient). Finally, we explain how
this method can be advantageously exploited in applications ranging from
bio-imaging light sheet microscopy to quantum memories for future quantum
communication networks
Diffusion is All You Need for Learning on Surfaces
We introduce a new approach to deep learning on 3D surfaces such as meshes or
point clouds. Our key insight is that a simple learned diffusion layer can
spatially share data in a principled manner, replacing operations like
convolution and pooling which are complicated and expensive on surfaces. The
only other ingredients in our network are a spatial gradient operation, which
uses dot-products of derivatives to encode tangent-invariant filters, and a
multi-layer perceptron applied independently at each point. The resulting
architecture, which we call DiffusionNet, is remarkably simple, efficient, and
scalable. Continuously optimizing for spatial support avoids the need to pick
neighborhood sizes or filter widths a priori, or worry about their impact on
network size/training time. Furthermore, the principled, geometric nature of
these networks makes them agnostic to the underlying representation and
insensitive to discretization. In practice, this means significant robustness
to mesh sampling, and even the ability to train on a mesh and evaluate on a
point cloud. Our experiments demonstrate that these networks achieve
state-of-the-art results for a variety of tasks on both meshes and point
clouds, including surface classification, segmentation, and non-rigid
correspondence
Deep Point Correlation Design
Designing point patterns with desired properties can require substantial
effort, both in hand-crafting coding and mathematical derivation. Retaining
these properties in multiple dimensions or for a substantial number of points
can be challenging and computationally expensive. Tackling those two issues,
we suggest to automatically generate scalable point patterns from design
goals using deep learning. We phrase pattern generation as a deep composition of weighted distance-based unstructured filters. Deep point pattern
design means to optimize over the space of all such compositions according to
a user-provided point correlation loss, a small program which measures a pattern’s fidelity in respect to its spatial or spectral statistics, linear or non-linear
(e. g., radial) projections, or any arbitrary combination thereof. Our analysis
shows that we can emulate a large set of existing patterns (blue, green, step,
projective, stair, etc.-noise), generalize them to countless new combinations
in a systematic way and leverage existing error estimation formulations to
generate novel point patterns for a user-provided class of integrand functions.
Our point patterns scale favorably to multiple dimensions and numbers of
points: we demonstrate nearly 10 k points in 10-D produced in one second
on one GPU. All the resources (source code and the pre-trained networks)
can be found at https://sampling.mpi-inf.mpg.de/deepsampling.html
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