arXiv research

Graph Neural Networks model 3D granular flow simulations.

A framework for generating 3D shapes by sequentially assembling primitives.

A machine learning method predicts rock permeability from 3D images.

Generative model calibrates 3D battery cathode morphologies from 2D images.

BGNNs model particle-boundary interactions efficiently.

Bayesian segmentation and uncertainty estimation improve 3D model accuracy for factory planning.

Geometric GNNs model 3D atomic systems with rotations and translations.

Analyze and predict complex 3D shape deformations using LSTM autoencoders and oriented bounding boxes.

We present a test platform for visual in-cabin scene analysis and occupant monitoring functions. The test platform is based on a driving simulator developed at the DFKI, consisting of a realistic in-cabin mock-up and a wide-angle projection system for a realistic driving experience. The platform has been equipped with …

A neural atlas simplifies 3D geometry simulation by avoiding meshing.

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions f…

Modeling 3D continua with singular points using Yin sets.

SE(3)-Transformers maintain equivariance for 3D data under rotations and translations.

Computes elastic grids that approximate 3D surfaces without physical simulations.

The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive expe…

New algorithms improve vascular flow simulations in aortic aneurysms.

3D RadViz improves 3D data visualization of multidimensional datasets.

We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, withou…

An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understanding and reasoning from an egocentric perspective. When trained from simulations, optimal environments should satisfy a currently unobtainable…

Study examines CSO algorithm for 3D swarming and tracking multiple targets.

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generat…

Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization. However, due to their high dimensionality and structure complexity, modeling and analysis of point…

In this work we simulate null geodesics for the Bonnor massive dipole metric by implementing a symbolic-numerical algorithm in Sage and Python. This program is also capable of visualizing in 3D, in principle, the geodesics for any given metric. Geodesics are launched from a common point, collectively forming a cone of …

TURB-Rot provides a large database of turbulent rotating flow snapshots for research.

WILD-SCAV benchmarks AI in complex 3D FPS environments.

Agent learns to navigate uncertain 3D maps using a hybrid planner.

Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be …

Neural network Kalman filtering improves 3D ultrasound object tracking.

CARV reduces compute cost for downstream pipelines using diffusion models.

We develop an adversarial-reinforcement learning scheme for microswimmers in statistically homogeneous and isotropic turbulent fluid flows, in both two (2D) and three dimensions (3D). We show that this scheme allows microswimmers to find non-trivial paths, which enable them to reach a target on average in less time tha…

We study knots in 3d Chern-Simons theory with complex gauge group $SL(N,\mathbb{C})$, in the context of its relation with 3d $\mathcal{N}=2$ theory (the so-called 3d-3d correspondence). The defect has either co-dimension 2 or co-dimension 4 inside the 6d $(2,0)$ theory, which is compactified on a 3-manifold $\hat{M}$. …

Framework for efficient defect classification and inspection.

In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a simulated environment where a robot equipped with a vacuum gripper has the aim of reaching blocks with planar surfaces. These blocks can have …

Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate differen…

Study of 3d-3d correspondence involving $q$-Weyl algebra and 3d-index.

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observa…

Bayesian framework optimizes 3D view selection for specific tasks.

3D dual field theories for Virasoro minimal models constructed using Seifert fiber spaces.

3D flying wings created for any angle asymptotic cones.

Model nonstationary spatial processes using normalizing flows.

Proposes a new effective central charge for 3d N=2 theories.

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…

Smooth 3D flows from non-smooth starting points.

This paper considers the single factor Heath-Jarrow-Morton model for the interest rate curve with stochastic volatility. Its natural formulation, described in terms of stochastic differential equations, is solved through Monte Carlo simulations, that usually involve rather large computation time, inefficient from a pra…

3D Axial-Attention improves lung nodule classification accuracy.

We have carried out simulations of a financial model of the firm to analyse the validity of the concept of Trade on Equity in dynamics. The results exhibit the ability of the borrowing policy connected to a cautious dividend distribution to inject chaos into the profit motion. The 3D system built with the van der Pol's…

3D steady gradient Ricci solitons are all O(2)-symmetric.

DreamFusion uses text-to-image diffusion models to create 3D images efficiently.