Back to Wiki

[39] 3D Virtual Modeling Realizations of Building Construction Scenes via Deep Learning Technique

EBSCO – Screened Structural Analysis & Infrastructure Monitoring 2022

Paper Description extracted from PDF · intro excerpt · 130 words

to Weihong Li; liweihong@xaufe.edu.cn Received 8 January 2022; Revised 4 February 2022; Accepted 17 February 2022; Published 31 March 2022 Academic Editor: Baiyuan Ding Copyright© 2022WeihongLi.qZ_hisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. qZ_hearchitecturaldrawingsoftraditionalbuildingconstructionsgenerallyrequiresomedesignknowledgeofthearchitecturalplan to be understood. With the continuous development of the construction industry, the use of three-dimensional (3D) virtual models of buildings is quickly increased. Using three-dimensional models can give people a more convenient and intuitive understanding of the model of the building, and it is necessary for the painter to manually draw the 3D model. By analyzing the common design rules of architectural drawing, this project designed and realized a building three-dimensional reconstruction system that can automatically generate a stereogram (3ds format) from a building plan (dxf format).

AuthorsLi, Weihong
Year2022
VenueComputational Intelligence & Neuroscience
DOI10.1155/2022/6286420
Source DatabaseEBSCO (Academic Search Ultimate)
Bridge-to-GNNCategory Borderline
GNN ArchitectureNot yet extracted from PDF
Graph EncodingNot yet extracted from PDF
AEC TaskNot yet extracted from PDF
CohortPeak Adoption (2022)

Extracted Methodology heuristic — verify before citing

Implementation FrameworkTopologic, Revit / Dynamo (heuristic — verify)
Key Hyperparameters1 layers (heuristic — verify)
Primary MetricAccuracy (heuristic — verify)
Quality Assessment1/6 · rigor: Low ·
Baseline: ✓ (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedKnowledge Graph Embedding (TransE/RotatE/DistMult)
Frameworks / librariesTopologic / Topologicpy · Revit / Dynamo
Hyperparameters (regex-detected)layers=1, 2
Quality (heuristic, 6-flag)0/6 · rigor: Low
Code/Data: —Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: —

Connected Papers Verification bibliometric (data/connected_papers_matches.json)

Bibliometrically related to other seedsThis paper appeared in the Connected Papers neighbourhood of these corpus seeds:

[77] 2020 · Chang · Learning to Simulate and Design for Structural Engineering

Lineage Families

None assigned

Enabled By / Precursors

None tracked

Extended By

None in corpus

Infrastructure Dependencies

Topologic (auto-suggested) Topologic / Topologicpy (detected from PDF) Revit / Dynamo (detected from PDF)

Research Frontier Tags

graph-ml-aec

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026