With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medicine. The rapid development of 3D reconstruction is becoming inevitable. This survey categorizes the various methods and technologies used in 3D reconstruction. It explores and classifies them based on three aspects: traditional static, dynamic, and machine learning. Furthermore, it compares and discusses these methods. At the end of the survey, which includes a detailed analysis of the trends and challenges in 3D reconstruction development, we aim to provide a comprehensive introduction for individuals who are currently engaged in or planning to conduct research on 3D reconstruction. Our goal is to help them gain a comprehensive understanding of the relevant knowledge related to 3D reconstruction.

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions. 

From dense point clouds to semantic digital models: End-to-end AI-based automation procedure for Manhattan-world structures

Combined robotic arms and mobile platforms, mobile construction robots(MCRs) are providing an energizing choice for the digitalization of the building industry. To enhance the comprehension of the research trajectory towards MCR applications and technologies in building construction, we focus on the following aspect: Current representative applications of MCRs in built environments and critical technologies involved. This comprehensive review identified 184 publications in the last 15 years to unravel MCRs in construction applications, scrutinized the crucial technologies involved, and deliberated on challenges and opportunities. Results indicate that MCRs are a growing application field, although the majority are still confined to laboratory settings. To further expand the application of MCR in construction scenarios, this paper proposes corresponding research roadmaps to address the challenges identified. The findings of this review provide an in-depth insight into digital construction and robotics, benefiting researchers and constructors in advancing robotic commercialization. 

Autonomous mobile construction robots in built environment: A comprehensive review

Deep learning applications for point clouds in the construction industry

In the context of fire safety inspections, Building Information Modeling (BIM) models enriched with Fire Safety Equipment (FSE) components can be used to complete compliance checks and other analyses. However, BIM models often lack the required FSE information. To address this issue, escape plans are a convenient source of data, as they show the position and type of FSE on floor plans. Therefore, this study proposes an automated method to analyze escape plans and extract FSE component information to enrich existing BIM models. The method employs the deep learning model Keypoint R-CNN for symbol detection. Symbol locations are then translated into physical positions within the BIM model. Through a real-building case study, the method demonstrates promising results. Future research may focus on improving the symbol detection performance and the registration between the BIM models and fire escape plans, as well as utilizing the extracted information for actual fire safety analyses. 

Enriching BIM models with fire safety equipment using keypoint-based symbol detection in escape plans

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Difeng Hu

Author Tools

Chat about Author

Papers

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor