ReviewSensors (Basel, Switzerland)2023
An Overview on Image-Based and Scanner-Based 3D Modeling Technologies.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 84 citations in OpenAlex.
- Multi-Sensor Geometric Documentation of Cultural Heritage at Risk Across Inland, Coastal and Shallow-Water Environments.Sensors (Basel, Switzerland) · 2026Article
- The Application of VR Technology in Engineering Issues: Geodesy and Geomatics, Mining, Environmental Protection and Occupational Safety.Sensors (Basel, Switzerland) · 2025Review
- Improved 3D reconstruction pipeline for enhancing the quality of 3D model generation from multi-view images.Scientific reports · 2025Article
- The Accuracy of an Optical White Light Desktop 3D Scanner and Cone Beam CT Scanner Compared to a Multi-Slice CT Scanner to Digitize Anatomical 3D Models: A Pilot Study.Craniomaxillofacial trauma & reconstruction · 2025Article
- Digital neurosurgery in the era of intelligent medicine: a scoping review.Frontiers in medicine · 2025Review
- The Role of Cone Beam Computed Tomography in Periodontology: From 3D Models of Periodontal Defects to 3D-Printed Scaffolds.Journal of personalized medicine · 2024Article
- Automation in Dentistry with Mechanical Drills and Lasers for Implant Osteotomy: A Narrative-Scoping Review.Dentistry journal · 2023Article
- Part2Point: A Part-Oriented Point Cloud Reconstruction Framework.Sensors (Basel, Switzerland) · 2023Article
- CBCT-Based Design of Patient-Specific 3D Bone Grafts for Periodontal Regeneration.Journal of clinical medicine · 2023Article
- Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure.Sensors (Basel, Switzerland) · 2023Review
- Three-Dimensional Immersion Scanning Technique: A Scalable Low-Cost Solution for 3D Scanning Using Water-Based Fluid.Sensors (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Advances in the scientific fields of photogrammetry and computer vision have led to the development of automated multi-image methods that solve the problem of 3D reconstruction. Simultaneously, 3D scanners have become a common source of data acquisition for 3D modeling of real objects/scenes/human bodies. This article presents a comprehensive overview of different 3D modeling technologies that may be used to generate 3D reconstructions of outer or inner surfaces of different kinds of targets. In this context, it covers the topics of 3D modeling using images via different methods, it provides a detailed classification of 3D scanners by additionally presenting the basic operating principles of each type of scanner, and it discusses the problem of generating 3D models from scans. Finally, it outlines some applications of 3D modeling, beyond well-established topographic ones.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.