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 $\mathcal{O}(k)$ instead of $\mathcal{O}(k^2)$ matrix operations (for a spline of order $k$) 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

Aggregate jump and volatility risk in the cross-section of stock returns

peer reviewe

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