arXiv research

2D CNNs approximate Korobov functions with near-optimal rates.

Proposes a model to generate 3D-aware images from 2D images.

Derive bihamiltonian structure for rational reduction of 2D-Toda hierarchy

Enhances 2D face recognition with 3D features using active illumination.

For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to de…

New method reconstructs 3D shapes from 2D images using Kendall's shape space.

The paper establishes T-duality for 2D σ-models with H-flux.

New theorem shows embedding restrictions for manifold skeletons.

Computes a new metric quantity Y(M) for Riemannian 2d-manifolds.

Transformer-M learns molecular data in 2D or 3D formats.

Study 2D viscoelastic equations using Lie group theory.

DISPR uses diffusion models to predict 3D cell shapes from 2D images.

iSTFTNet2 improves iSTFTNet's speed and lightness with 1D-2D CNN.

Paper extends 2D ZSAD to 3D MRI without training, achieving robust anomaly detection.

Optimizes master faces for 2D and 3D face verification using evolutionary algorithms and neural networks.

In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect a…

3D shape instantiation which reconstructs the 3D shape of a target from limited 2D images or projections is an emerging technique for surgical intervention. It improves the currently less-informative and insufficient 2D navigation schemes for robot-assisted Minimally Invasive Surgery (MIS) to 3D navigation. Previously,…

Universal Gaussian parity proven for 2D knots.

There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physi…

Study classifies equidistant decompositions in 2D spaces.

We describe rules for building 2d theories labeled by 4-manifolds. Using the proposed dictionary between building blocks of 4-manifolds and 2d N=(0,2) theories, we obtain a number of results, which include new 3d N=2 theories T[M_3] associated with rational homology spheres and new results for Vafa-Witten partition fun…

The paper introduces various canonical parameterizations for 2D-curved shapes.

Study Ricci vector fields on 2D space with diagonal metrics.

This paper is devoted to obtain the one-dimensional group invariant solutions of the two-dimensional Ricci flow ((2D) Rf) equation. By classifying the orbits of the adjoint representation of the symmetry group on its Lie algebra, the optimal system of one-dimensional subalgebras of the ((2D) Rf) equation is obtained. F…

Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent advances in point cloud representation learning. Point clouds can be interpreted as 2D manifolds in 3D space. Specifically, we fold a 2D grid on…

Survey on matrix hydrodynamics, a 2D fluid model.

Curvature of 2D subsets preserved in their space.

Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…

New neural model processes 2D data with long-range dependencies efficiently.

In this work we reduce undersampling artefacts in two-dimensional ($2D$) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on $2D$ spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two $2D$ and a $3D$ Deep Lear…

New model preserves symmetry in multivariate time series, improving performance.

Modeling financial market dynamics with 2D Levy flights.

New method for optimizing risk in financial models using Fourier transforms.

This paper studies both the conductance and charge transport on 2D orbifolds in a strong magnetic field. We consider a family of Landau Hamiltonians on a complex, compact 2D orbifold $Y$ that are parametrised by the Jacobian torus $J(Y)$ of $Y$. We calculate the degree of the associated stable holomorphic spectral orbi…

When using Convolutional Neural Networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice (2D) or whole volumes (3D). One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as in…

CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.

In this note we study the distribution of real inflection points among the ovals of a real non-singular hyperbolic curve of even degree. Using Hilbert's method we show that for any integers $d$ and $r$ such that $4\leq r \leq 2d^2-2d$, there is a non-singular hyperbolic curve of degree $2d$ in $\mathbb R^2$ with exactl…

Study bihamiltonian structures and Frobenius manifolds for specific Toda hierarchies.

Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring. In this work, instead of estimating the distance…

SECRM-2D improves RL-based autonomous driving with safety guarantees.

Embeds complex into higher-dimensional pseudomanifold.

Self attention mechanisms have become a key building block in many state-of-the-art language understanding models. In this paper, we show that the self attention operator can be formulated in terms of 1x1 convolution operations. Following this observation, we propose several novel operators: First, we introduce a 2D ve…

2d GLSM connects Berry connections to Coulomb branch via difference equations.

A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods…

We apply the OSCAR (octagonal selection and clustering algorithms for regression) in recovering group-sparse matrices (two-dimensional---2D---arrays) from compressive measurements. We propose a 2D version of OSCAR (2OSCAR) consisting of the $\ell_1$ norm and the pair-wise $\ell_{\infty}$ norm, which is convex but non-d…

We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining par…

The study classifies discrete pseudomanifolds with up to 2d+7 vertices.

We propose that a simple, Lagrangian 2d $\mathcal{N}=(0, 2)$ duality interface between the 3d $\mathcal{N}=2$ XYZ model and 3d $\mathcal{N}=2$ SQED can be associated to the simplest triangulated 4-manifold: the 4-simplex. We then begin to flesh out a dictionary between more general triangulated 4-manifolds with boundar…