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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for 3D metrics

Study periodic geodesics on contact 3D manifolds, proving existence and precise properties.

problem Existence and properties of periodic geodesics in contact sub-Riemannian metrics.
method Develops two independent subjects: existence of spiraling geodesics and precise study of geodesics on quotient of SL2(R).
result Proves existence and precise properties of periodic geodesics.

Detect spacetime curvature without rulers and clocks in 3D.

problem Detecting spacetime curvature without traditional measurement tools.
method Generalized results from 2D to 3D spacetime, proving well-stitched spacetime for conformally flat cases.
result A 3D spacetime is well-stitched if and only if it is conformally flat, providing a tool for curvature detection.

3D models vulnerable to adversarial attacks, new method improves success rate and naturalness.

problem Vulnerability of 3D deep learning models to adversarial examples in the physical world.
method ε-isometric (εε-ISO) attack considering geometric properties and invariance to physical transformations.
result Significantly improved attack success rate and naturalness of 3D adversarial examples.

Study 3D manifolds with specific curvature conditions.

problem Characterize 3D generalized (κ,μκ,μ)-contact metric manifolds.
method Analyze manifolds with ildeWR=0 ilde{W}\cdot R=0 and ildeWH=0 ilde{W}\cdot H=0.
result Cover all eight equivalent classes of 3D manifolds.

The paper classifies 3D paracontact and almost paracosymplectic spaces.

problem Classifying 3D paracontact and almost paracosymplectic spaces.
method Detailed structure analysis and local classification for all possible values of κ.
result Local classification of paracontact metric and almost paracosymplectic (κ,μ)-spaces for every possible value of κ.

The study classifies minimal translation surfaces in 3D and 3D_1.

problem Classifying minimal translation surfaces in specific geometric settings.
method Defined and classified minimal translation surfaces with semi-symmetric connections.
result New classification of minimal translation surfaces in R3\mathbb{R}^3 and R13\mathbb{R}^3_1.

The paper proves conjectures and classifies metrics on 3D manifolds.

problem Proving conjectures and classifying metrics on 3D manifolds with specific curvature conditions.
method Analytical proofs and classification theorems.
result Critical metrics on 3D manifolds are isometric to geodesic balls in space forms.

3D object detection improved using energy-based models.

problem Accurate 3D object detection in cluttered environments from sparse LiDAR data.
method Designing a differentiable pooling operator for 3D bounding boxes integrated into a state-of-the-art 3D object detector.
result Our approach consistently outperforms the SA-SSD baseline across all 3DOD metrics on the KITTI dataset.

The Fefferman metric connects CR manifolds to conformal geodesics in 3D.

problem Understanding the Fefferman metric on CR manifolds.
method Explicit description of the Fefferman metric and variational characterization of conformal geodesics.
result Conformal geodesics have lifts to chains and null chains, and are characterized by total torsion.

Paper shows non-CSC HCMU metrics can't be isometrically immersed into 3D space forms.

problem Non-CSC HCMU metrics cannot be isometrically immersed into 3D space forms.
method Using moving frames to demonstrate the impossibility of isometric minimal immersion.
result Non-CSC HCMU metrics cannot be isometrically immersed into 3D space forms.

Study on 3D Lie groups finds all generalized Einstein metrics.

problem Classifying generalized Einstein metrics on 3D Lie groups.
method Developed theory of left-invariant generalized pseudo-Riemannian metrics, computed Ricci tensor, determined all metrics.
result Determined all generalized Einstein metrics on three-dimensional Lie groups.

We consider 3D flow equations inspired by the renormalization group (RG) equations of string theory with a three dimensional target space. By modifying the flow equations to include a U(1) gauge field, and adding carefully chosen De Turck terms, we are able to extend recent 2D results of Bakas to the case of a 3D Riema…

2005-09-13abs ↗pdf ↗

The polynomial affine model of gravity is explored in 3D, focusing on cosmological solutions.

problem Exploring deviations from general relativity in a 3D context.
method Developed a polynomial affine model of gravity, applied to homogeneous isotropic cosmological models, and classified solutions.
result Explicit solutions derived from the connection allow the definition of alternative/emergent metrics.

PolyGen models 3D meshes directly, predicting vertices and faces sequentially.

problem Efficiently modeling 3D geometry for computer graphics, robotics, and games.
method Transformer-based autoregressive model for predicting mesh vertices and faces.
result PolyGen produces high-quality, usable 3D meshes and competitive conditional performance.

New techniques solve Riccati equations on 3D manifolds, finding 4th order metric obstructions.

problem Solving Riccati-type equations with algebraic constraints on 3D Riemannian manifolds.
method Real algebraic geometry techniques, focusing on connection coefficients and Hessian equations.
result Obstruction to solving Riccati equations has order 4 in metric coefficients.

A 3D almost-Riemannian manifold is a generalized Riemannian manifold defined locally by 3 vector fields that play the role of an orthonormal frame, but could become collinear on some set $\Zz$ called the singular set. Under the Hormander condition, a 3D almost-Riemannian structure still has a metric space structure, wh…

2014-07-02abs ↗pdf ↗

The paper constructs and classifies 3D Walker manifolds with specific structures.

problem Classifying 3D Walker manifolds with specific paracontact structures.
method Constructing structures using a unit space-like vector field and a function, characterizing the Lorentzian metric.
result Necessary and sufficient conditions for the manifold to belong to specific classes of almost paracontact metric manifolds.

The paper studies rigid sphere packings on 3D manifolds with boundary.

problem Investigating rigid sphere packings on 3D manifolds with boundary.
method Introducing generalized sphere packings, proving rigidity, introducing combinatorial curvature flows.
result Generalized sphere packing metrics are determined by combinatorial scalar curvature.

Study of null φ-slant curves in specific 3D manifolds.

problem Characterizing null φ-slant curves in 3D normal almost contact B-metric manifolds.
method Analyzing the geometric properties and Frenet frames of φ-slant null curves.
result Existence of a unique Frenet frame for non-geodesic φ-slant null curves.

Study investigates induced geometry on surfaces in 3D contact manifolds.

problem Understanding the metric structure on surfaces embedded in 3D contact sub-Riemannian manifolds.
method Defined a coefficient to characterize characteristic points and identified global conditions for finite induced distance.
result Proved induced distance finite for certain surfaces with isolated characteristic points.

Improves AI agents' 3D navigation by learning from failures and 3D spatial relationships.

problem Challenges in data efficiency, obstacle avoidance, and generalization in 3D visual navigation.
method Incorporates attention on 3D spatial relationships and a target skill extension module into DRL framework.
result Significantly improves navigation performance and generalization across targets and scenes.

SCENE-Net improves 3D point cloud segmentation with low resource usage and transparency.

problem Lack of resources and transparency in 3D semantic segmentation models.
method SCENE-Net uses signature shapes identified via GENEOs to achieve semantic segmentation with minimal resources.
result SCENE-Net achieves comparable IoU to state-of-the-art methods with less data and computational resources.

This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.

problem Creating large datasets for deep learning in computer vision.
method 3D facial modelling techniques and deep learning methodologies.
result Synthetic 3D thermal facial data created for deep learning applications.