Next-generation deep learning based on simulators and synthetic data
Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …
learn in a most artificial way: they require large quantities of labeled data to grasp even …
Deep generative models in engineering design: A review
L Regenwetter, AH Nobari… - Journal of …, 2022 - asmedigitalcollection.asme.org
Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …
design process and improve access to highly optimized and customized products across …
Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models
Recent CLIP-guided 3D optimization methods, such as DreamFields and PureCLIPNeRF,
have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch …
have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch …
Vectormapnet: End-to-end vectorized hd map learning
Autonomous driving systems require High-Definition (HD) semantic maps to navigate
around urban roads. Existing solutions approach the semantic mapping problem by offline …
around urban roads. Existing solutions approach the semantic mapping problem by offline …
Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization
H Su, D Zhao, H Elmannai, AA Heidari… - Computers in Biology …, 2022 - Elsevier
COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It
usually is diagnosed by examining pathological photographs of the patient's lungs. There is …
usually is diagnosed by examining pathological photographs of the patient's lungs. There is …
Let 2d diffusion model know 3d-consistency for robust text-to-3d generation
Text-to-3D generation has shown rapid progress in recent days with the advent of score
distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural …
distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural …
Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection
The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …
combinatorial optimization problem, which effectively combines the memetic algorithm …
Learning implicit fields for generative shape modeling
We advocate the use of implicit fields for learning generative models of shapes and
introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving …
introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving …
Local deep implicit functions for 3d shape
The goal of this project is to learn a 3D shape representation that enables accurate surface
reconstruction, compact storage, efficient computation, consistency for similar shapes …
reconstruction, compact storage, efficient computation, consistency for similar shapes …
Learning gradient fields for shape generation
In this work, we propose a novel technique to generate shapes from point cloud data. A point
cloud can be viewed as samples from a distribution of 3D points whose density is …
cloud can be viewed as samples from a distribution of 3D points whose density is …