402 research outputs found
Visual-Inertial Mapping with Non-Linear Factor Recovery
Cameras and inertial measurement units are complementary sensors for
ego-motion estimation and environment mapping. Their combination makes
visual-inertial odometry (VIO) systems more accurate and robust. For globally
consistent mapping, however, combining visual and inertial information is not
straightforward. To estimate the motion and geometry with a set of images large
baselines are required. Because of that, most systems operate on keyframes that
have large time intervals between each other. Inertial data on the other hand
quickly degrades with the duration of the intervals and after several seconds
of integration, it typically contains only little useful information.
In this paper, we propose to extract relevant information for visual-inertial
mapping from visual-inertial odometry using non-linear factor recovery. We
reconstruct a set of non-linear factors that make an optimal approximation of
the information on the trajectory accumulated by VIO. To obtain a globally
consistent map we combine these factors with loop-closing constraints using
bundle adjustment. The VIO factors make the roll and pitch angles of the global
map observable, and improve the robustness and the accuracy of the mapping. In
experiments on a public benchmark, we demonstrate superior performance of our
method over the state-of-the-art approaches
Symbolically Analyzing Security Protocols Using Tamarin
During the last three decades, there has been considerable research devoted to the symbolic analysis of security protocols and existing tools have had considerable success both in detecting attacks on protocols and showing their absence. Nevertheless, there is still a large discrepancy between the symbolic models that one specifies on paper and the models that can be effectively analyzed by tools.
In this paper, we present the Tamarin prover for the symbolic analysis of security protocols. Tamarin takes as input a security protocol model, specifying the actions taken by the agents running the protocol in different roles (e.g., the protocol initiator, the responder, and the trusted key server), a specification of the adversary, and a specification of the protocol’s desired properties. Tamarin can then be used to automatically construct a proof that the protocol fulfills its specified properties, even when arbitrarily many instances of the protocol’s roles are interleaved in parallel, together with the actions of the adversary
Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography
Historical buildings are a treasure and milestone of human cultural heritage.
Reconstructing the 3D models of these building hold significant value. The
rapid development of neural rendering methods makes it possible to recover the
3D shape only based on archival photographs. However, this task presents
considerable challenges due to the limitations of such datasets. Historical
photographs are often limited in number and the scenes in these photos might
have altered over time. The radiometric quality of these images is also often
sub-optimal. To address these challenges, we introduce an approach to
reconstruct the geometry of historical buildings, employing volumetric
rendering techniques. We leverage dense point clouds as a geometric prior and
introduce a color appearance embedding loss to recover the color of the
building given limited available color images. We aim for our work to spark
increased interest and focus on preserving historical buildings. Thus, we also
introduce a new historical dataset of the Hungarian National Theater, providing
a new benchmark for the reconstruction method
Efficient Derivative Computation for Cumulative B-Splines on Lie Groups
Continuous-time trajectory representation has recently gained popularity for
tasks where the fusion of high-frame-rate sensors and multiple unsynchronized
devices is required. Lie group cumulative B-splines are a popular way of
representing continuous trajectories without singularities. They have been used
in near real-time SLAM and odometry systems with IMU, LiDAR, regular, RGB-D and
event cameras, as well as for offline calibration. These applications require
efficient computation of time derivatives (velocity, acceleration), but all
prior works rely on a computationally suboptimal formulation. In this work we
present an alternative derivation of time derivatives based on recurrence
relations that needs instead of matrix
operations (for a spline of order ) and results in simple and elegant
expressions. While producing the same result, the proposed approach
significantly speeds up the trajectory optimization and allows for computing
simple analytic derivatives with respect to spline knots. The results presented
in this paper pave the way for incorporating continuous-time trajectory
representations into more applications where real-time performance is required.Comment: First two authors contributed equall
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry
We present a direct visual-inertial odometry (VIO) method which estimates the
motion of the sensor setup and sparse 3D geometry of the environment based on
measurements from a rolling-shutter camera and an inertial measurement unit
(IMU).
The visual part of the system performs a photometric bundle adjustment on a
sparse set of points. This direct approach does not extract feature points and
is able to track not only corners, but any pixels with sufficient gradient
magnitude. Neglecting rolling-shutter effects in the visual part severely
degrades accuracy and robustness of the system. In this paper, we incorporate a
rolling-shutter model into the photometric bundle adjustment that estimates a
set of recent keyframe poses and the inverse depth of a sparse set of points.
IMU information is accumulated between several frames using measurement
preintegration, and is inserted into the optimization as an additional
constraint between selected keyframes. For every keyframe we estimate not only
the pose but also velocity and biases to correct the IMU measurements. Unlike
systems with global-shutter cameras, we use both IMU measurements and
rolling-shutter effects of the camera to estimate velocity and biases for every
state.
Last, we evaluate our system on a novel dataset that contains global-shutter
and rolling-shutter images, IMU data and ground-truth poses for ten different
sequences, which we make publicly available. Evaluation shows that the proposed
method outperforms a system where rolling shutter is not modelled and achieves
similar accuracy to the global-shutter method on global-shutter data
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
We present NeuroMorph, a new neural network architecture that takes as input
two 3D shapes and produces in one go, i.e. in a single feed forward pass, a
smooth interpolation and point-to-point correspondences between them. The
interpolation, expressed as a deformation field, changes the pose of the source
shape to resemble the target, but leaves the object identity unchanged.
NeuroMorph uses an elegant architecture combining graph convolutions with
global feature pooling to extract local features. During training, the model is
incentivized to create realistic deformations by approximating geodesics on the
underlying shape space manifold. This strong geometric prior allows to train
our model end-to-end and in a fully unsupervised manner without requiring any
manual correspondence annotations. NeuroMorph works well for a large variety of
input shapes, including non-isometric pairs from different object categories.
It obtains state-of-the-art results for both shape correspondence and
interpolation tasks, matching or surpassing the performance of recent
unsupervised and supervised methods on multiple benchmarks.Comment: Published at the IEEE/CVF Conference on Computer Vision and Pattern
Recognition 202
Design, Analysis, and Implementation of ARPKI: An Attack-Resilient Public-Key Infrastructure
The current Transport Layer Security (TLS) Public-Key Infrastructure (PKI) is based on a weakest-link security model that depends on over a thousand trust roots. The recent history of malicious and compromised Certification Authorities has fueled the desire for alternatives. Creating a new, secure infrastructure is, however, a surprisingly challenging task due to the large number of parties involved and the many ways that they can interact. A principled approach to its design is therefore mandatory, as humans cannot feasibly consider all the cases that can occur due to the multitude of interleavings of actions by legitimate parties and attackers, such as private key compromises (e.g., domain, Certification Authority, log server, other trusted entities), key revocations, key updates, etc.
We present ARPKI, a PKI architecture that ensures that certificate-related operations, such as certificate issuance, update, revocation, and validation, are transparent and accountable. ARPKI efficiently supports these operations, and gracefully handles catastrophic events such as domain key loss or compromise. Moreover ARPKI is the first PKI architecture that is co-designed with a formal model, and we verify its core security property using the T AMARIN prover. We prove that ARPKI offers extremely strong security guarantees, where compromising even n-1 trusted signing and verifying entities is insufficient to launch a man-in-the-middle attack. Moreover, ARPKI’s use deters misbehavior as all operations are publicly visible. Finally, we present a proof-of-concept implementation that provides all the features required for deployment. Our experiments indicate that ARPKI efficiently handles the certification process with low overhead. It does not incur additional latency to TLS, since no additional round trips are required
Quality Control at Your Fingertips: Quality-Aware Translation Models
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy
for neural machine translation (NMT) models. The underlying assumption is that
model probability correlates well with human judgment, with better translations
being more likely. However, research has shown that this assumption does not
always hold, and decoding strategies which directly optimize a utility
function, like Minimum Bayes Risk (MBR) or Quality-Aware decoding can
significantly improve translation quality over standard MAP decoding. The main
disadvantage of these methods is that they require an additional model to
predict the utility, and additional steps during decoding, which makes the
entire process computationally demanding. In this paper, we propose to make the
NMT models themselves quality-aware by training them to estimate the quality of
their own output. During decoding, we can use the model's own quality estimates
to guide the generation process and produce the highest-quality translations
possible. We demonstrate that the model can self-evaluate its own output during
translation, eliminating the need for a separate quality estimation model.
Moreover, we show that using this quality signal as a prompt during MAP
decoding can significantly improve translation quality. When using the internal
quality estimate to prune the hypothesis space during MBR decoding, we can not
only further improve translation quality, but also reduce inference speed by
two orders of magnitude
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