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.
Algorithm aligns 3D density maps using Wasserstein distance.
The paper proposes methods for volumetric parameterization of 3D solid manifolds.
Physics-constrained neural nets solve EM fields of charged particle beams.
LFlows model fluid densities and velocities using invertible maps that satisfy the continuity equation.
Defines a map connecting 3d-index and skein module.
The article explains Rao distances and conformal mappings for 3D objects.
A novel method compares 3D point clouds using information geometry.
Propose a new 3d quantum trace map that agrees with Garoufalidis and Yu's construction and extends to certain manifolds with ideal triangulated boundaries.
Paper connects 3D gravity averages to 2D CFT correlators.
3D quantum trace map connects 3-manifold quantizations.
Study mapping class groups of specific 3D shapes.
Graph Neural Networks improve 3D object detection in LiDAR point clouds.
3D adversarial logos can fool object detectors in real-world settings.
DreamFusion uses text-to-image diffusion models to create 3D images efficiently.
Determining the 3D structures of biological molecules is a key problem for both biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising technique for structure estimation which relies heavily on computational methods to reconstruct 3D structures from 2D images. This paper introduces the challenging Cryo-…
Deep learning speeds spectral density estimation for large 2D/3D grids.
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep con…
Automated rock fragmentation assessment using deep learning and spatial statistics.
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objec…
Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely comput…
A method improves Cryo-EM 3D map refinement by regularizing rotation estimation.
By studying the group of rigid motions, , in the 3D-Heisenberg group , we define the density and the measure for the sets of horizontal lines. We show that the volume of a convex domain is equal to the integral of length of chord over all horizontal lines intersecting . As the classical r…
3D dust map of the Milky Way improves resolution and accuracy.
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…
One of the main challenges in 3d-3d correspondence is that no existent approach offers a complete description of 3d SCFT --- or, rather, a "collection of SCFTs" as we refer to it in the paper --- for all types of 3-manifolds that include, for example, a 3-torus, Brieskorn spheres, and hyperbolic surgerie…
The paper studies decay near singularities of 3d Yang-Mills-Higgs fields.
Improved 3D ECG feature attributions for clinical interpretation.
Agent learns to navigate uncertain 3D maps using a hybrid planner.
Quasi-conformal (QC) theory is an important topic in complex analysis, which studies geometric patterns of deformations between shapes. Recently, computational QC geometry has been developed and has made significant contributions to medical imaging, computer graphics and computer vision. Existing computational QC theor…
Develops spherical density-equalizing maps for closed surfaces.
New method uses SoS densities and α-divergences for efficient sequential transport maps.
The tangential map is a map on the set of smooth planar curves. It satisfies the 3D-consistency property and is closely related to some well-known integrable equations.
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of these 3D-CNNs is still an infeasible task. In this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D…
We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a …
Mapper-GIN simplifies 3D point cloud classification with lightweight structure.
Symmetrizes 4d and 3d BPS quivers for Argyres-Douglas theories.
We solve 6-DoF localisation and 3D reconstruction using deep state-space models.
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual chec…
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…
Developed an ellipsoidal density-equalizing map for genus-0 closed surfaces.
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks termed squeeze and excitation (SE) have been introduced to recalibrate F-CNN feature maps both channel- and spatial-wise, boosting segmentation …
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network. To capture the similarity between point clouds we rely o…
This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.
This paper shows stable mappings are never dense on non-compact manifolds.
3D Axial-Attention improves lung nodule classification accuracy.
The paper proposes a method to learn 3D object pose manifolds using GANs and elasticae.
Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. Function is dictated by structure, since it is by spatial interactions that molecules interact with each other, both in terms of steric complementarity, as well …
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to captu…