2,963 research outputs found
Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for addressing
computer vision problems that use `Analysis by Synthesis'. Analysis by
synthesis involves the minimization of the reconstruction error which is
typically a non-convex function of the latent target variables.
State-of-the-art methods adopt a hybrid scheme where discriminatively trained
predictors like Random Forests or Convolutional Neural Networks are used to
initialize local search algorithms. While these methods have been shown to
produce promising results, they often get stuck in local optima. Our method
goes beyond the conventional hybrid architecture by not only proposing multiple
accurate initial solutions but by also defining a navigational structure over
the solution space that can be used for extremely efficient gradient-free local
search. We demonstrate the efficacy of our approach on the challenging problem
of RGB Camera Relocalization. To make the RGB camera relocalization problem
particularly challenging, we introduce a new dataset of 3D environments which
are significantly larger than those found in other publicly-available datasets.
Our experiments reveal that the proposed method is able to achieve
state-of-the-art camera relocalization results. We also demonstrate the
generalizability of our approach on Hand Pose Estimation and Image Retrieval
tasks
OutCast: Outdoor Single-image Relighting with Cast Shadows
We propose a relighting method for outdoor images. Our method mainly focuses
on predicting cast shadows in arbitrary novel lighting directions from a single
image while also accounting for shading and global effects such the sun light
color and clouds. Previous solutions for this problem rely on reconstructing
occluder geometry, e.g. using multi-view stereo, which requires many images of
the scene. Instead, in this work we make use of a noisy off-the-shelf
single-image depth map estimation as a source of geometry. Whilst this can be a
good guide for some lighting effects, the resulting depth map quality is
insufficient for directly ray-tracing the shadows. Addressing this, we propose
a learned image space ray-marching layer that converts the approximate depth
map into a deep 3D representation that is fused into occlusion queries using a
learned traversal. Our proposed method achieves, for the first time,
state-of-the-art relighting results, with only a single image as input. For
supplementary material visit our project page at:
https://dgriffiths.uk/outcast.Comment: Eurographics 2022 - Accepte
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
Camera pose estimation is an important problem in computer vision. Common
techniques either match the current image against keyframes with known poses,
directly regress the pose, or establish correspondences between keypoints in
the image and points in the scene to estimate the pose. In recent years,
regression forests have become a popular alternative to establish such
correspondences. They achieve accurate results, but have traditionally needed
to be trained offline on the target scene, preventing relocalisation in new
environments. Recently, we showed how to circumvent this limitation by adapting
a pre-trained forest to a new scene on the fly. The adapted forests achieved
relocalisation performance that was on par with that of offline forests, and
our approach was able to estimate the camera pose in close to real time. In
this paper, we present an extension of this work that achieves significantly
better relocalisation performance whilst running fully in real time. To achieve
this, we make several changes to the original approach: (i) instead of
accepting the camera pose hypothesis without question, we make it possible to
score the final few hypotheses using a geometric approach and select the most
promising; (ii) we chain several instantiations of our relocaliser together in
a cascade, allowing us to try faster but less accurate relocalisation first,
only falling back to slower, more accurate relocalisation as necessary; and
(iii) we tune the parameters of our cascade to achieve effective overall
performance. These changes allow us to significantly improve upon the
performance our original state-of-the-art method was able to achieve on the
well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional
contributions, we present a way of visualising the internal behaviour of our
forests and show how to entirely circumvent the need to pre-train a forest on a
generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin
assert joint first authorshi
Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
We introduce a neural relighting algorithm for captured indoors scenes, that
allows interactive free-viewpoint navigation. Our method allows illumination to
be changed synthetically, while coherently rendering cast shadows and complex
glossy materials. We start with multiple images of the scene and a 3D mesh
obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is
well-explained as the sum of a view-independent diffuse component and a
view-dependent glossy term concentrated around the mirror reflection direction.
We design a convolutional network around input feature maps that facilitate
learning of an implicit representation of scene materials and illumination,
enabling both relighting and free-viewpoint navigation. We generate these input
maps by exploiting the best elements of both image-based and physically-based
rendering. We sample the input views to estimate diffuse scene irradiance, and
compute the new illumination caused by user-specified light sources using path
tracing. To facilitate the network's understanding of materials and synthesize
plausible glossy reflections, we reproject the views and compute mirror images.
We train the network on a synthetic dataset where each scene is also
reconstructed with MVS. We show results of our algorithm relighting real indoor
scenes and performing free-viewpoint navigation with complex and realistic
glossy reflections, which so far remained out of reach for view-synthesis
techniques
How chains and rings affect the dynamic magnetic susceptibility of a highly clustered ferrofluid
The dynamic magnetic susceptibility, χ(ω), of a model ferrofluid at a very low concentration (volume fraction, approximately 0.05%), and with a range of dipolar coupling constants (1≤λ≤8), is examined using Brownian dynamics simulations. With increasing λ, the structural motifs in the system change from unclustered particles, through chains, to rings. This gives rise to a nonmonotonic dependence of the static susceptibility χ(0) on λ and qualitative changes to the frequency spectrum. The behavior of χ(0) is already understood, and the simulation results are compared to an existing theory. The single-particle rotational dynamics are characterized by the Brownian time, τB, which depends on the particle size, carrier-liquid viscosity, and temperature. With λ≤5.5, the imaginary part of the spectrum, χ′′(ω), shows a single peak near ω∼τB-1, characteristic of single particles. With λ≥5.75, the spectrum is dominated by the low-frequency response of chains. With λ≥7, new features appear at high frequency, which correspond to intracluster motions of dipoles within chains and rings. The peak frequency corresponding to these intracluster motions can be computed accurately using a simple theory. © 2021 American Physical Society.A.O.I. gratefully acknowledges research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Mathematical Center Project No. 075-02-2021-1387)
Effects of nanoparticle heating on the structure of a concentrated aqueous salt solution
The effects of a rapidly heated nanoparticle on the structure of a concentrated aqueous salt solution are studied using molecular dynamics simulations. A diamond-like nanoparticle of radius 20 Å is immersed in a sodium-chloride solution at 20% above the experimental saturation concentration and equilibrated at T = 293 K and P = 1 atm. The nanoparticle is then rapidly heated to several thousand degrees Kelvin, and the system is held under isobaric-isoenthalpic conditions. It is observed that after 2-3 ns, the salt ions are depleted far more than water molecules from a proximal zone 15-25 Å from the nanoparticle surface. This leads to a transient reduction in molality in the proximal zone and an increase in ion clustering in the distal zone. At longer times, ions begin to diffuse back into the proximal zone. It is speculated that the formation of proximal and distal zones, and the increase in ion clustering, plays a role in the mechanism of nonphotochemical laser-induced nucleation. © 2017 Author(s)
An apodizing phase plate coronagraph for VLT/NACO
We describe a coronagraphic optic for use with CONICA at the VLT that
provides suppression of diffraction from 1.8 to 7 lambda/D at 4.05 microns, an
optimal wavelength for direct imaging of cool extrasolar planets. The optic is
designed to provide 10 magnitudes of contrast at 0.2 arcseconds, over a
D-shaped region in the image plane, without the need for any focal plane
occulting mask.Comment: 9 pages, 5 figures, to appear in Proc. SPIE Vol. 773
Exploiting uncertainty in regression forests for accurate camera relocalization
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure. In these methods, each tree associates one pixel with a point in the scene's 3D world coordinate frame. In previous work, these predictions were point estimates and the subsequent camera pose optimization implicitly assumed an isotropic distribution of these estimates. In this paper, we train a regression forest to predict mixtures of anisotropic 3D Gaussians and show how the predicted uncertainties can be taken into account for continuous pose optimization. Experiments show that our proposed method is able to relocalize up to 40% more frames than the state of the art
Quantum Monte Carlo calculations of electronic excitation energies: the case of the singlet (CO) transition in acrolein
We report state-of-the-art quantum Monte Carlo calculations of the singlet (CO) vertical excitation energy in the acrolein molecule, extending
the recent study of Bouab\c{c}a {\it et al.} [J. Chem. Phys. {\bf 130}, 114107
(2009)]. We investigate the effect of using a Slater basis set instead of a
Gaussian basis set, and of using state-average versus state-specific
complete-active-space (CAS) wave functions, with or without reoptimization of
the coefficients of the configuration state functions (CSFs) and of the
orbitals in variational Monte Carlo (VMC). It is found that, with the Slater
basis set used here, both state-average and state-specific CAS(6,5) wave
functions give an accurate excitation energy in diffusion Monte Carlo (DMC),
with or without reoptimization of the CSF and orbital coefficients in the
presence of the Jastrow factor. In contrast, the CAS(2,2) wave functions
require reoptimization of the CSF and orbital coefficients to give a good DMC
excitation energy. Our best estimates of the vertical excitation energy are
between 3.86 and 3.89 eV.Comment: 6 pages, 1 figure, 2 tables, to appear in Progress in Theoretical
Chemistry and Physic
- …