What's the Situation with Intelligent Mesh Generation: A Survey and
Perspectives
- URL: http://arxiv.org/abs/2211.06009v3
- Date: Tue, 23 May 2023 14:37:48 GMT
- Title: What's the Situation with Intelligent Mesh Generation: A Survey and
Perspectives
- Authors: Na Lei, Zezeng Li, Zebin Xu, Ying Li, and Xianfeng Gu
- Abstract summary: Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes.
Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques.
This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape.
- Score: 13.081274167488843
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Intelligent Mesh Generation (IMG) represents a novel and promising field of
research, utilizing machine learning techniques to generate meshes. Despite its
relative infancy, IMG has significantly broadened the adaptability and
practicality of mesh generation techniques, delivering numerous breakthroughs
and unveiling potential future pathways. However, a noticeable void exists in
the contemporary literature concerning comprehensive surveys of IMG methods.
This paper endeavors to fill this gap by providing a systematic and thorough
survey of the current IMG landscape. With a focus on 113 preliminary IMG
methods, we undertake a meticulous analysis from various angles, encompassing
core algorithm techniques and their application scope, agent learning
objectives, data types, targeted challenges, as well as advantages and
limitations. We have curated and categorized the literature, proposing three
unique taxonomies based on key techniques, output mesh unit elements, and
relevant input data types. This paper also underscores several promising future
research directions and challenges in IMG. To augment reader accessibility, a
dedicated IMG project page is available at
\url{https://github.com/xzb030/IMG_Survey}.
Related papers
- Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
Ontologies are widely used for representing domain knowledge and meta data.
One straightforward solution is to integrate statistical analysis and machine learning.
Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
arXiv Detail & Related papers (2024-06-16T14:49:19Z) - A Comprehensive Survey on Machine Learning Driven Material Defect Detection: Challenges, Solutions, and Future Prospects [6.559194485550409]
Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products.
The rapid and accurate identification and localization of MD constitute crucial research endeavours in addressing contemporary challenges associated with MD.
In recent years, propelled by the swift advancement of machine learning (ML) technologies, deep learning has swiftly emerged as the core technology and a prominent research direction for material defect detection (MDD)
We systematically survey the ML techniques applied in MDD into five categories: unsupervised learning, supervised learning, semi-supervised learning, reinforcement learning, and generative learning.
arXiv Detail & Related papers (2024-06-12T05:19:55Z) - Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects [84.36935309169567]
We present a broad review of recent advances for fine-grained analysis in zero-shot learning (ZSL)
We first provide a taxonomy of existing methods and techniques with a thorough analysis of each category.
Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library.
arXiv Detail & Related papers (2024-01-31T11:51:24Z) - Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
Information extraction aims to extract structural knowledge from plain natural language texts.
generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation.
LLMs offer viable solutions for IE tasks based on a generative paradigm.
arXiv Detail & Related papers (2023-12-29T14:25:22Z) - Embedding in Recommender Systems: A Survey [67.67966158305603]
A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors.
Applying embedding techniques captures complex entity relationships and has spurred substantial research.
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
arXiv Detail & Related papers (2023-10-28T06:31:06Z) - Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques [3.265458968159693]
The review is based on 220 scientific articles published between January 2019 and March 2024.
The authors adopt a classifying framework to interpret and highlight research similarities and peculiarities.
arXiv Detail & Related papers (2023-09-27T19:22:19Z) - Retrieval-Enhanced Machine Learning [110.5237983180089]
We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases.
REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization.
REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
arXiv Detail & Related papers (2022-05-02T21:42:45Z) - Scene Graph Generation: A Comprehensive Survey [35.80909746226258]
Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding.
Scene Graph Generation (SGG) refers to the task of automatically mapping an image into a semantic structural scene graph.
We review 138 representative works that cover different input modalities, and systematically summarize existing methods of image-based SGG.
arXiv Detail & Related papers (2022-01-03T00:55:33Z) - A Survey on Heterogeneous Graph Embedding: Methods, Techniques,
Applications and Sources [79.48829365560788]
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios.
HG embedding aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks.
arXiv Detail & Related papers (2020-11-30T15:03:47Z) - A Systematic Literature Review on the Use of Deep Learning in Software
Engineering Research [22.21817722054742]
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL)
This paper presents a systematic literature review of research at the intersection of SE & DL.
We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques to a given problem domain.
arXiv Detail & Related papers (2020-09-14T15:28:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.