In this repository we provide 3D scan projects and the 3D models processed from them with their metadata using the example of a wood sample. The metadata was generated using our metadata generation script, which is described in the referenced publication.

The 3D scan projects were created in different software (atos v6.2, atos 2016 and zeiss 2023). For each there is a scan project, a 3D model and the generated metadata with and without uri in this repository.

The publication in which this application case is included: Homburg, T., Cramer, A., Raddatz, L. et al. Metadata schema and ontology for capturing and processing of 3D cultural heritage objects. Herit Sci 9, 91 (2021). https://doi.org/10.1186/s40494-021-00561-w

Python scripts for exporting metadata can be found here: GitHub - i3mainz/3dcap-md-gen at 0.1.3

the project's GitHub repository: https://github.com/WangYuLin-SEU/KASAL

  Google Scanned Objects (GSO) Symmetry Axis Dataset





  1. Dataset Description

This dataset is an extension of the Google Scanned Objects (GSO) dataset, enriched with symmetry axis annotations for each object. It is designed to assist in pose estimation tasks by providing explicit symmetry information for objects with both geometric and texture symmetries.

  Key Features:

Objects: 3D scanned… See the full description on the dataset page: https://huggingface.co/datasets/SEU-WYL/GSO-SAD.

Hand contact data

Document Scanner

## Overview

Document Scanner is a dataset for object detection tasks - it contains Objects annotations for 1,760 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.

📂 A Comprehensive OCR Dataset for Document Understanding

This diverse dataset consists of scanned images and corresponding annotations from various document types, including invoices, forms, ID cards, and real-life photographs. It is specifically designed to support research in Optical Character Recognition (OCR), providing both structured and unstructured document images. With a total of 8531 files, the dataset includes images and annotations across different categories: DOCUMENT, FORM, INVOICE, and REAL_LIFE.

🗂️ Dataset Breakdown DOCUMENT:

Train: 1231 images, 1231 annotations

Test: 153 images, 153 annotations

Validation: 155 images, 155 annotations

FORM:

Train: 159 images, 159 annotations

Test: 21 images, 21 annotations

Validation: 19 images, 19 annotations

INVOICE:

Train: 778 images, 778 annotations

Test: 98 images, 98 annotations

Validation: 97 images, 97 annotations

REAL_LIFE:

Train: 1244 images, 1243 annotations

Test: 155 images, 155 annotations

Validation: 156 images, 156 annotations

🎯 Key Features: 📑 Categories: Includes diverse document types (invoices, forms, ID cards, real-life photographs).

📝 Annotations: Precise bounding box annotations for OCR tasks.

📊 Total Files: 8531 images and annotations across four categories.

💼 Use Cases: Document digitization, identity verification, financial record processing, and more.

🔍 Ideal For: Training, fine-tuning, and evaluating OCR models (both pretrained and from scratch).

Benchmarking OCR systems in real-world applications such as document digitization, identity verification, and financial record processing.

This dataset is a valuable tool for researchers, developers, and practitioners working on OCR systems and document understanding. Whether you're developing an OCR model for scanning documents, recognizing ID cards, or processing invoices, this dataset has the variety and complexity needed for robust training and evaluation.

Dataset from experiment on infill and printing orientation for its influence on surface quality, object stability and geometrical fidelity. FDM 3D printed specimens ("dogbone") with ABS plastics, scanned on a Canon LiDE 210 scanning device with resolutions between 600 and 4800 DPI. Supplemental Material.

Hand contact data, reflecting the intricate behaviours of human hands during object operation, exhibits significant potential for analysing hand operation patterns to guide the design of hand-related sensors and robots, and predicting object properties. However, these potential applications are hindered by the constraints of low resolution and incomplete capture of the hand contact data. Leveraging a non-contact and high-precision 3D scanning method for surface capture, a high-resolution and whole-body hand contact dataset, named as Ti3D-contact, is constructed in this work. The dataset, with an average resolution of 0.72 mm, contains 1872 sets of texture images and 3D models. The contact area during hand operation is whole-body painted on gloves, which are captured as the high-resolution original hand contact data through a 3D scanner. Reliability validation on Ti3D-contact is conducted and hand movement classification with 95% precision is achieved using the acquired hand contact data..., This work compiles a high-resolution and whole-body human hand contact dataset named as Ti3D-contact. First, participants wear cotton textile gloves to grasp and manipulate different types and sizes of objects, onto which high-adhesion paint is sprayed to paint whole-body hand contact areas on the gloves. Then, the painted gloves are scanned by a 3D scanner to capture the original hand contact data in the form of texture images and 3D models. After extracting the painted areas on the obtained 3D models in the form of point cloud, the processed hand contact data recording the contact areas between hands and objects is further obtained. A coordinate conversion method is then employed by unifying the coordinates of the processed hand contact data. Through unifying the coordinate systems, the consistency of all the hand contact data is improved, which benefits the analyses of hand operation patterns. Furthermore, a method to calculate the Euclidean distance between the adjacent points in th..., , # A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method

https://doi.org/10.5061/dryad.2v6wwq003

Description of the data and file structure

The dataset comprises three folders: "Scan Data", "Digital Data" and “Meta Data†, the “Scan Data†folder contains the original hand contact data in the form of 3D models and texture images, the processed hand contact data after separating and unifying is saved in the form of point cloud documents under the “Digital Data†folder, and the “Meta Data†folder contains the participant data. The structure of the Ti3D-contact dataset is presented in Figure 8.

The privacy of participants is rigorously safeguarded in our Ti3D contact dataset. Each participant is anonymized and identified with labels the same as in Table 1. Under the Digital Data folder, the data of each participant is saved in a separate folder. There are 52 sub-folders within the separate folder and...,

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database. Task description: In response to a given set of range scan queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. Data set: The query set is composed of at least 180 range images, which are acquired by capturing 3 or 4 range scans of 60 models from arbitrary view directions. The range images are captured using a Minolta Laser Scanner. The file format is in the ASCII Object File Format (.off) representing the scan in a triangular mesh. The target database contains 1200 complete 3D models, which are categorized into 60 classes. In each class there are 20 models. The file format to represent the 3D models is the ASCII Object File Format (.off). Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2). Please Cite the Paper: Godil A, Dutagaci H, Bustos B, Choi S, Dong S, Furuya T, Li H, Link N, Moriyama A, Meruane R, Ohbuchi R. SHREC'15: range scans based 3D shape retrieval. In Proceedings of the Eurographics Workshop on 3D Object Retrieval, Zurich, Switzerland 2015 May 3 (pp. 2-3). https://doi.org/10.5555/2852282.2852312

Dataset

This dataset was created by Doctor M

Contents

Professional Grade 3D Scanner Market Outlook

The global market size for professional grade 3D scanners was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 7.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% over the forecast period. The primary growth factors driving this market include the increasing adoption of 3D scanning technologies across various industries, advancements in scanner technology, and the rising demand for high precision and accuracy in industrial applications.

The surge in demand for professional grade 3D scanners is largely fueled by their extensive applications in industrial sectors such as automotive, aerospace, and manufacturing. These industries require high precision and accuracy in designing and prototyping, which 3D scanners efficiently provide. Moreover, the integration of 3D scanning technology with other advanced manufacturing technologies such as additive manufacturing and CNC machining has further bolstered its adoption. The ability of 3D scanners to reduce time and costs in the product development cycle is another significant growth driver for the market.

Advancements in technology, such as the development of handheld and portable 3D scanners, have made these devices more accessible and user-friendly. This innovation has expanded their use beyond traditional industrial applications to areas like healthcare, where they are used for patient diagnostics and treatment planning, and architecture, where they assist in creating precise building models. The continual improvement in the accuracy, speed, and usability of 3D scanners is expected to further drive market growth.

The rise in Industry 4.0 and the increasing trend toward digitalization in manufacturing processes have also contributed significantly to the growth of the professional grade 3D scanner market. As industries strive for greater efficiency and automation, the demand for technologies that can provide precise data for modeling and analysis has increased. 3D scanners play a crucial role in this transformation by providing detailed and accurate 3D models that are essential for various digital manufacturing processes.

The development of Industrial Handheld 3D Scanner technology has revolutionized the way industries approach complex scanning tasks. These devices are designed to be portable and easy to use, allowing operators to capture detailed 3D data in challenging environments. With their ability to scan large objects and intricate details with high precision, industrial handheld 3D scanners are becoming indispensable tools in sectors such as automotive, aerospace, and manufacturing. They facilitate rapid prototyping, quality control, and reverse engineering, thereby enhancing productivity and reducing operational costs. As the demand for flexible and efficient scanning solutions grows, industrial handheld 3D scanners are expected to see increased adoption across various industries.

Regionally, North America holds a significant share of the global professional grade 3D scanner market, driven by the presence of major technology companies and high adoption rates in the automotive and aerospace sectors. However, the Asia Pacific region is expected to experience the highest growth rate due to rapid industrialization, increasing investments in manufacturing and infrastructure, and the growing adoption of advanced technologies.

Product Type Analysis

The professional grade 3D scanner market can be segmented by product type into handheld 3D scanners, desktop 3D scanners, and industrial 3D scanners. Handheld 3D scanners have gained popularity due to their portability, ease of use, and ability to scan objects in hard-to-reach places. These scanners are widely used in various fields such as healthcare for scanning body parts, in manufacturing for quality control, and in the automotive industry for reverse engineering.

Desktop 3D scanners, on the other hand, are typically used for smaller objects that require high precision and detail. These scanners are often utilized in industries such as jewelry design, dental practices, and small-scale manufacturing. The growing demand for customized products and the need for precise detailing in these applications are driving the demand for desktop 3D scanners.

Industrial 3D scanners are designed for scanning large objects such as automotive parts, aerospace components,

3D Digital Scanners Market Outlook

The 3D digital scanners market size was valued at approximately USD 4.5 billion in 2023 and is projected to reach around USD 12.8 billion by 2032, growing at a CAGR of 12.4% during the forecast period. This robust growth can be attributed to the increasing adoption of advanced technologies across various sectors, such as healthcare, automotive, and aerospace, as well as the rising demand for high-precision and efficient scanning solutions.

The growth of the 3D digital scanners market is primarily driven by technological advancements and the increasing preference for digital solutions in design and manufacturing processes. Industries are rapidly integrating 3D digital scanning technology to improve accuracy, reduce production times, and enhance overall efficiency. For instance, in the healthcare sector, 3D scanners are being extensively used for creating accurate and customized prosthetics, dental devices, and implants, which in turn is propelling the market demand.

Another significant growth factor is the burgeoning application of 3D digital scanners in the automotive and aerospace industries. These sectors are leveraging the technology for tasks such as reverse engineering, quality control, and rapid prototyping. The ability of 3D digital scanners to provide detailed and precise measurements is helping manufacturers maintain stringent quality standards and accelerate product development cycles. Additionally, the increasing investments in research and development are expected to further fuel market expansion.

The architecture and construction industry is also witnessing a surge in the adoption of 3D digital scanners. The technology is being utilized for creating accurate 3D models of existing structures, which aids in renovation and restoration projects. Furthermore, the growing trend of Building Information Modeling (BIM) is driving the demand for 3D scanning solutions, as they provide critical data needed for precise planning and execution. Consequently, the continuous advancements in the scanning technology are anticipated to open new avenues for market growth.

The integration of 3D Vision Scanner technology is revolutionizing industries by providing unparalleled precision and efficiency. These scanners are particularly beneficial in sectors where accuracy is critical, such as aerospace and automotive, where they are used for detailed inspections and quality assurance. The ability of 3D Vision Scanners to capture intricate details and complex geometries makes them invaluable for reverse engineering and rapid prototyping. As industries continue to demand higher standards of precision, the adoption of 3D Vision Scanners is expected to rise, further driving market growth and innovation.

Regionally, North America currently holds a dominant share in the 3D digital scanners market, attributed to the presence of leading market players and the early adoption of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid industrialization and increasing investments in infrastructure development. The expanding manufacturing sector in countries like China and India is also contributing to the market growth, as industries in these regions are increasingly adopting 3D scanning technology to enhance their production capabilities.

Product Type Analysis

The 3D digital scanners market by product type is segmented into Laser 3D Scanners, Structured Light 3D Scanners, and Optical 3D Scanners. Each of these types offers distinct advantages and caters to different application needs, contributing to the overall market growth. Laser 3D scanners, known for their high precision and ability to scan large objects, are widely used in industrial and manufacturing applications. They are particularly beneficial for reverse engineering and quality control processes, where accuracy is paramount. The robustness and reliability of laser 3D scanners make them a preferred choice in automotive and aerospace sectors, where precision is critical.

Structured Light 3D Scanners, on the other hand, are gaining popularity due to their speed and versatility. These scanners use a pattern of light to capture the objectÂ’s geometry, making them ideal for applications that require rapid scanning and detailed texture mapping. In the healthcare industry, structured light 3D scanners are extensively used for creating detailed mode

Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.

Relevant computer vision tasks:

bounding box detection

classification

instance segmentation

keypoint estimation

3D bounding box estimation

3D voxel reconstruction

3D reconstruction

The dataset is for academic research use only, since it uses resources with restrictive licenses. For a detailed description of how the resources are used, we refer to our paper and project page.

Licenses of the resources in detail:

Google Scanned Objects: CC BY 4.0 (for details on which files are used, see the respective meta folder)

Cardboard Dataset: CC BY 4.0

Shipping Label Dataset: CC BY-NC 4.0

Other Labels: See file misc/source_urls.json

LDR Dataset: License for Non-Commercial Use

Large Logo Dataset (LLD): Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.

You can use our textureless models (i.e. the obj files) of damaged parcels under CC BY 4.0 (note that this does not apply to the textures).

If you use this resource for scientific research, please consider citing

@inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }

This data set contains single channel seismic reflection profiles as provided to NGDC by Lamont Doherty Earh Observatory (LDEO). The profiles were originally provided as 8x10 negatives, but were scanned and now available as digital images. Images were scanned at 400 dpi using 8-bit grayscale and are in jpg format.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database. Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of query consists of range images. Data set: The query set is composed of 120 range images, which are acquired by capturing 3 range scans of 40 models from arbitrary view directions. The range images are captured using a Minolta Laser Scanner. The file format is in the ASCII Object File Format (.off) representing the scan in a triangular mesh. The target database contains 800 complete 3D models, which are categorized into 40 classes. In each class there are 20 models. The file format to represent the 3D models is the ASCII Object File Format (.off). Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2). Please Cite the Paper: Dutagaci H, Godil A, Cheung CP, Furuya T, Hillenbrand U, Ohbuchi R. SHREC'10 Track: Range Scan Retrieval. In3DOR 2010 May 2 (pp. 109-115). http://dx.doi.org/10.2312/3DOR/3DOR10/109-115

This dataset includes 3D colour images from scanned insect (Diptera and Hymenoptera) specimens, as well as some modified intermediate images which combine traits from two different specimens. The intermediate versions are in a format suitable for 3D printing.

This dataset contains scanning data from Project 25499. The files are in pairs, one contains the raw output from ZMap and the second conains JSON objects with any collected data ; questons@project25499.com

The global 3D scanning market was valued at USD 4.96 billion in 2019 and is anticipated to expand at a CAGR of nearly 8.1% during the forecast period, 2020–2027. The growth of the market is attributed to growing technological advancements and rising R&D investment in the production of innovative 3D scanning devices.
3D scanner can be described as a technology that creates three-dimensional representation of real-world objects which can be viewed from different angles on electronic devices. The scanner produces the exact shape and form of the targeted object or body, which help in the study or analyzing the observed materials. Some advanced devices such as laser-based scanner are also widely used to enhance the observation of objects and their surrounding environment.

Market Drivers, Restraints, Trends, and Opportunities

• Rapid growth of the electronic industry and wide availability of advanced 3D technologies for various applications are expected to drive the market growth during the forecast period.
• Rising demand for 3D products from industries including automotive, healthcare, and aerospace industries are anticipated to boost the overall market development over the analysis years.
• Technological advancement in the production of innovative 3D devices is estimated to propel the market expansion in the coming years.
• High cost of 3D scanning devices and lack of skilled practitioners to handle the devices are projected to hamper the market development during the targeted years.
• The market growth between 2019 and 2020 has been affected due to the adverse impact of the COVID-19 pandemic and the trend is expected to continue till mid-2021.
• Large R&D investment from several key electronic manufacturing companies are anticipated to offer immense opportunities for the market expansion during the projected period.

Scope of the Report

OCR Text Detection in the Documents Object Detection dataset

The dataset is a collection of images that have been annotated with the location of text in the document. The dataset is specifically curated for text detection and recognition tasks in documents such as scanned papers, forms, invoices, and handwritten notes. The dataset contains a variety of document types, including different layouts, font sizes, and styles. The images come from diverse sources, ensuring a representative… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/ocr-text-detection-in-the-documents.

Scanned images of primary Geological Data resulting from deep underground coal exploration and exploitation. Collection of data includes reports, interpretations and records of research in British coalfield areas deposited by the Coal Authority. Data for past and current collieries and for future prospects. The majority of the collection was deposited with the National Geological Records Centre by the Coal Authority in July 2001. The collection includes borehole site plans, borehole logs , analyses and geophysical data etc. A large percentage of this data will eventually be linked to existing collections.