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This benchmark aims to provide tools to evaluate 3D Interest Point Detection Algorithms with respect to human generated ground truth. Using a web-based subjective experiment, human subjects marked 3D interest points on a set of 3D models. The models were organized in two datasets: Dataset A and Dataset B. Dataset A consists of 24 models which were hand-marked by 23 human subjects. Dataset B is larger with 43 models, and it contains all the models in Dataset B. The number of human subjects who marked all the models in this larger set is 16. Some of the models are standard models that are widely used in 3D shape research; and they have been used as test objects by researchers working on the best view problem. We have compared five 3D Interest Point Detection algorithms. The interest points detected on the 3D models of the dataset can be downloaded from the link below. Please refer to README for details in the download. Mesh saliency [Lee et al. 2005] : Interest points by mesh saliency Salient points [Castellani et al. 2008] : Interest points by salient points 3D-Harris [Sipiran and Bustos, 2010] : Interest points by 3D-Harris 3D-SIFT [Godil and Wagan, 2011] : Interest points by 3D-SIFT (Please note that some models in the dataset are not watertight, hence their volumetric representations could not be generated. Therefore, 3D-SIFT algorithm wasn't able to detect interest points for those models.) Scale-dependent corners [Novatnack and Nishino, 2007] : Interest points by SD corners HKS-based interest points [Sun et al. 2009] : Interest points by HKS method Please Cite the Paper: Helin Dutagaci, Chun Pan Cheung, Afzal Godil, ?Evaluation of 3D interest point detection techniques via human-generated ground truth?, The Visual Computer, 2012. References: [Lee et al. 2005] Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: ACM SIGGRAPH 2005, pp. 659?666 (2005) [Castellani et al. 2008] Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Comput. Graph. Forum 27(2), 643?652 (2008) [Sipiran and Bustos, 2010] Sipiran, I., Bustos, B.: A robust 3D interest points detector based on Harris operator. In: Eurographics 2010 Workshop on 3D Object Retrieval (3DOR?10), pp. 7?14 (2010) [Godil and Wagan, 2011] Godil, A., Wagan, A.I.: Salient local 3D features for 3D shape retrieval. In: 3D Image Processing (3DIP) and Applications II, SPIE (2011) [Novatnack and Nishino, 2007] Novatnack, J., Nishino, K.: Scale-dependent 3D geometric features. In: ICCV, pp. 1?8, (2007) [Sun et al. 2009] Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Eurographics Symposium on Geometry Processing (SGP), pp. 1383?1392 (2009)

Polygons: 51 Vertices: 32

The EGDI3d database is a storage for geologic 3D models. Geometries from a variety of modelling tools are imported and stored in a common format, together with model metadata and geological descriptions in a GeoSciML inspired relational database.

The Landsat Program is a series of Earth-observing satellite missions jointly managed by NASA and the U.S. Geological Survey. Since 1972, Landsat satellites have collected information about Earth from space.

The 3D printed brain model industry is expected to reach USD 44.5 billion in 2024 and is projected to reach a total of USD 242.9 billion by 2034. Between 2024 and 2034, the industry is expected to register a CAGR of 18.5%. The 3D printed brain model market was worth USD 37.5 billion in 2023.

Attributes	Key Insights
Historical Size, 2023	USD 37.5 billion
Estimated Size, 2024	USD 44.5 billion
Projected Size, 2034	USD 242.9 billion
Value-based CAGR (2024 to 2034)	18.5%

Semi Annual Industry Update

Particular	Value CAGR
H1	19.7% (2023 to 2033)
H2	19.2% (2023 to 2033)
H1	18.5% (2024 to 2034)
H2	18.1% (2024 to 2034)

Country-wise Insights

Countries	Value CAGR (2024 to 2034)
United States	7.8%
Canada	13.6%
United Kingdom	9%
Germany	7.8%
India	28.4%
China	25.1%
South Korea	19.7%

Category-wise Insights

Type	Fused Deposition Modelling
Value Share (2023)	28.7%

Material	Plastics
Value Share (2023)	43.7%

3D model budov centra Ostravy. Stav k r. 2001.

3D model based on a 3D scan of a 11-centimeter-tall figure of a Bull from the Iron Age found in the Býčí skála cave (“Bull Rock Cave”) near Brno, Czech Republic. The 2,500-year-old bull in bronze is one of the most artistically sophisticated and valuable figures from the Hallstatt Culture.

3D Model Platforms Market Outlook

The global 3D Model Platforms market size was estimated at USD 2.5 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a CAGR of 11.2% during the forecast period. This significant growth can be attributed to the increasing demand for advanced design tools across various industries, including gaming, film and animation, architecture, and healthcare, among others. The rising adoption of cloud-based solutions and continuous advancements in 3D modeling technology further drive the market's growth.

One of the primary growth factors for the 3D Model Platforms market is the increasing adoption of 3D modeling in the gaming and film industries. The gaming industry, in particular, has seen a substantial surge in demand for high-quality, realistic graphics, which necessitates the use of advanced 3D modeling tools. Similarly, the film and animation industry relies heavily on 3D models for creating lifelike characters and scenes. These industries' continuous quest for more immersive and engaging content is expected to drive the demand for 3D modeling platforms significantly.

Another crucial factor propelling the growth of this market is the advancement in augmented reality (AR) and virtual reality (VR) technologies. AR and VR are becoming increasingly prevalent in various sectors, including healthcare, architecture, and retail. These technologies rely on 3D models to create realistic simulations and environments, thereby driving the demand for sophisticated 3D modeling tools. For instance, in healthcare, 3D models are used for surgical simulations and medical training, enhancing the precision and effectiveness of medical procedures.

The growing trend towards digitization and Industry 4.0 is also contributing to the market's expansion. Industries such as automotive and construction are increasingly adopting 3D modeling platforms to enhance their design processes and improve their product development cycles. In the automotive sector, 3D models are used for designing vehicle components and prototypes, which helps in reducing the overall development time and cost. Similarly, in architecture and construction, 3D models are used for creating detailed building plans and visualizations, improving project accuracy and efficiency.

From a regional perspective, North America holds a significant share of the 3D Model Platforms market, driven by the presence of major technology companies and a high adoption rate of advanced technologies. The Asia Pacific region is expected to witness substantial growth during the forecast period, supported by increasing investments in digital infrastructure and the rapid development of the gaming and entertainment industries. Europe also represents a significant market due to the strong presence of automotive and architectural firms that extensively use 3D modeling tools.

Component Analysis

The 3D Model Platforms market is segmented by component into software and services. The software segment dominates the market, driven by the extensive use of various 3D modeling software tools across different industries. These software solutions are essential for creating detailed and accurate 3D models, which are crucial for design, simulation, and visualization purposes. The continuous advancements in software capabilities, including improved rendering speeds, better user interfaces, and enhanced integration with other digital tools, are expected to sustain the growth of this segment.

Services, on the other hand, also play a critical role in the 3D Model Platforms market. These services include consulting, implementation, training, and support, which are crucial for ensuring that organizations can effectively utilize 3D modeling tools. As the complexity of 3D modeling software increases, so does the demand for professional services that can help businesses maximize the return on their software investments. Additionally, the rise of cloud-based solutions has led to an increase in demand for managed services, where third-party providers handle the maintenance and updates of 3D modeling platforms.

The software segment can be further divided into various types of 3D modeling tools, including CAD (Computer-Aided Design) software, 3D rendering software, and animation software. CAD software, in particular, is widely used in industries such as architecture, automotive, and manufacturing for designing and prototyping. 3D rendering software is essential for creating realistic images and animations, making it highly valuable in the gaming and film industries. Anim

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments.

Part of the DEEPEN project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps.

The DEEPEN PFA Methodology is based on the method proposed by Poux et al. (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values.

The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type: 1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability 2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir) 3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid)

More information on these components and their development can be found in Kolker et al., 2022.

For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. More information on this process is described in the following sections. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component.

Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty.

There are two different versions of the Leapfrog model and associated favorability models: - v1.0: The first release in June 2023 - v2.1: The second release, with improvements made to the earthquake catalog (included additional identified events, removed duplicate events), to the temperature model (fixed a deep BHT), and to the index models (updated the seismicity-heat source index models for supercritical and EGS, and the resistivity-insulation index models for all three play types). Also uses the jet color map rather than the magma color map for improved interpretability. - v2.1.1: Updated to include v2.0 uncertainty results (see below for uncertainty model versions)

There are two different versions of the associated uncertainty models: - v1.0: The first release in June 2023 - v2.0: The second release, with improvements made to the temperature and fault uncertainty models.

** Note that this submission is deprecated and that a newer submission, linked below and titled "DEEPEN Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano" contains the final versions of these resources. **

A very high resolution model of the International Space Station in many parts. The download includes an image of the final configuration. This model is provided in its original Lightwave format, which preserves the configuration of the component parts. This model dates from February 2011.

The 3D Printed Surgical Models Market is projected to grow from USD 607.5 million in 2025 to USD 2,589.0 million by 2035, registering a strong compound annual growth rate (CAGR) of 15.6%.

Attributes	Key Insights
Historical Size, 2024	USD 524.6 million
Estimated Size, 2025	USD 607.5 million
Projected Size, 2035	USD 2,589.0 million
Value-based CAGR (2025 to 2035)	15.6%

Semi-annual Market Update for 3D Printed Surgical Models Market

Particular	Value CAGR
H1	16.5% (2024 to 2034)
H2	16.2% (2024 to 2034)
H1	15.6% (2025 to 2035)
H2	15.2% (2025 to 2035)

Analysis of Top Countries Adopting 3D Printed Surgical Models

Countries	Value CAGR (2025 to 2035)
USA	11.5%
Germany	10.8%
France	16.9%
UK	9.8%
Spain	13.9%
China	21.3%
India	22.6%
Japan	13.8%

3D Printed Surgical Models Industry Analysis by Top Investment Segments

By Product	Organ Models
Value Share (2025)	43.1%

By Technology	Stereolithography 3D Printing
Value Share (2025)	29.8%

Snapshot img

3D Mapping And Modeling Market Size 2025-2029

The 3d mapping and modeling market size is forecast to increase by USD 35.78 billion, at a CAGR of 21.5% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing adoption in smart cities and urban planning projects. This trend is attributed to the ability of 3D mapping and modeling technologies to provide accurate and detailed visualizations of complex urban environments, enabling more efficient planning and management. Another key driver is the emergence of digital twin technology, which allows for real-time monitoring and simulation of physical assets in a digital environment. However, the market also faces challenges, most notably privacy and security concerns. With the increasing use of 3D mapping and modeling in various applications, there is a growing risk of data breaches and unauthorized access to sensitive information. As such, companies must prioritize robust security measures to protect customer data and maintain trust. Additionally, the high cost of implementing and maintaining these technologies remains a barrier to entry for some organizations. Despite these challenges, the market's potential for innovation and growth is substantial, with opportunities for companies to capitalize on emerging trends and navigate challenges effectively.

What will be the Size of the 3D Mapping And Modeling Market during the forecast period?

Request Free SampleThe market continues to evolve, driven by advancements in spatial data acquisition, project management, and navigation systems. BIM software, artificial intelligence (AI), and 3D visualization services are increasingly integrated into infrastructure management, real estate development, and cultural heritage preservation. Image recognition and 3D scanning are revolutionizing asset management and virtual reality (VR) applications. In precision agriculture, AI development and machine learning enable object detection and scene understanding, while data analysis and processing facilitate more efficient crop management. Autonomous vehicles and remote sensing rely on 3D modeling software and point cloud processing for accurate environmental monitoring. Additive manufacturing and 3D printing services are transforming industries, from construction to healthcare, with advancements in 3D modeling software, materials, and processing techniques. Urban planning benefits from AI-driven data analytics and 3D model optimization for smarter city design. Deep learning and computer vision are enhancing object tracking and data visualization services, while software development and spatial analysis are improving facility management and location-based services. The ongoing integration of these technologies is shaping a dynamic and innovative market landscape.

How is this 3D Mapping And Modeling Industry segmented?

The 3d mapping and modeling industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Product Type3D modeling3D mappingComponentSoftwareServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Product Type Insights

The 3d modeling segment is estimated to witness significant growth during the forecast period.The 3D modeling market encompasses the creation of three-dimensional digital representations of objects, environments, and surfaces, finding extensive applications in industries such as architecture, gaming, film production, product design, and medical imaging. This technology's integration has revolutionized these sectors, fostering more precise and efficient design, planning, and analysis. Key technologies driving this market include computer-aided design (CAD) software, 3D scanning and rendering, simulation and animation tools, and geospatial data. CAD is a cornerstone technology in architecture, engineering, and manufacturing, enabling professionals to create intricate and accurate designs. 3D scanning and rendering technologies convert physical objects into digital models, crucial for industries where exact replicas are required. Artificial intelligence (AI) and machine learning algorithms are increasingly integrated into 3D modeling, enhancing object detection, computer vision, and data analysis capabilities. Virtual reality (VR) and augmented reality (AR) technologies are transforming 3D modeling by providing immersive experiences for design visualization, enabling better scene understanding and spatial analysis. Precision agriculture employs 3D modeling for terrain modeling and crop monitoring, while infrastructure management uses it for asset management and urban planning. 3D modeling is also instrumental in heritage preservation, environmental monitoring, and ad

Data files for a high resolution three dimensional (3D) structure of the rat hippocampus reconstructed from histological sections. The data files (supplementary data for Ropireddy et al., Neurosci., 2012 Mar 15;205:91-111) are being shared on the Windows Live cloud space provided by Microsoft. Downloadable data files include the Nissl histological images, the hippocampus layer tracings that can be visualized alone or superimposed to the corresponding Nissl images, the voxel database coordinates, and the surface rendering VRML files. * Hippocampus Nissl Images: The high resolution histological Nissl images obtained at 16 micrometer inter-slice distance for the Long-Evans rat hippocampus can be downloaded or directly viewed in a browser. This dataset consists of 230 jpeg images that cover the hippocampus from rostral to caudal poles. This image dataset is uploaded in seven parts as rar files. * Hippocampus Layer Tracings: The seven hippocampus layers ''ML, ''GC'', ''HILUS'' in DG and ''LM'', ''RAD'', ''PC'', ''OR'' in CA were segmented (traced) using the Reconstruct tool which can be downloaded from Synapse web. This tool outputs all the tracings for each image in XML format. The XML tracing files for all these seven layers for each of the above Nissl images are zipped into one file and can be downloaded. * Hippocampus VoxelDB: The 3D hippocampus reconstructed is volumetrically transformed into 16 micrometer sized voxels for all the seven layers. Each voxel is reported according to multiple coordinate systems, namely in Cartesian, along the natural hippocampal dimensions, and in reference to the canonical brain planes. The voxel database file is created in ascii format. The single voxel database file was split into three rar archive files. Please note that the three rar archive files should be downloaded and decompressed in a single directory in order to obtain the single voxel data file (Hippocampus-VoxelDB.txt). * 3D Surface Renderings: This is a rar archive file with a single VRML file containing the surface rendering of DG and CA layers. This VRML file can be opened and visualized in any VRML viewer, e.g. the open source software view3dscene. * 3D Hippocampus Movie: This movie contains visualization of the 3D surface renderings of CA (blue) and DG (red) inner and outer boundaries; neuronal embeddings of DG granule and CA pyramidal dendritic arbors; potential synapses between CA3b interneuron axon and pyramidal dendrite, and between CA2 pyramidal axon and CA pyramidal dendrites.

This .stl file was produced by scaling the original model and converting it directly to .stl format.

The dataset includes 10 object categories from the MVTEC INDUSTRIAL 3D OBJECT DETECTION DATASET as input CAD objects. The selected objects include a diverse range of industrial products:

S.No	Object Class
1	adapter plate triangular
2	bracket big
3	clamp small
4	engine part cooler round
5	engine part cooler square
6	injection pump
7	screw
8	star
9	tee connector
10	thread

The dataset contains a total of 100,000 RGB images of each object category, divided into three sets: 70,000 for training, 20,000 for testing, and 10,000 for validation. Each image has a resolution of 224 x 224 and is in JPEG format.

To ensure the suitability of our dataset for various computer vision tasks, we included not only the class labels but also generated bounding boxes and semantic masks for each image, which are stored in COCO annotation format. Each image contains one instance of the ten selected objects.

Throughout the 10,000 images for each class, we randomly varied the position of the object in x-y-z direction and the object’s rotation to provide a diverse range of images. Additionally, we changed the object’s surface to a smooth metallic texture, imitating real industrial components. Lastly, we varied the lighting conditions within each image, including the position of the light sources, their energy, and emission strength.

Find out more about our Data Generation Tool:

Schuerrle, B., Sankarappan, V., & Morozov, A. (2023). SynthiCAD: Generation of Industrial Image Data Sets for Resilience Evaluation of Safety-Critical Classifiers. In Proceeding of the 33rd European Safety and Reliability Conference. 33rd European Safety and Reliability Conference. Research Publishing Services. https://doi.org/10.3850/978-981-18-8071-1_p400-cd

This is a 3D STL dataset of human airways ready for a CFD or CFPD simulation. It is suitable for both OpenFOAM and StarCCM+. This dataset is a comprehensive representation of a larger database that comprises 420 human respiratory tract 3D models. data.txt is a list with the details of what represents each group in the folders.

3D Model Sharing Platform Market Outlook

The 3D Model Sharing Platform market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 4.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.3% during the forecast period. The growth of this market is driven by the increasing demand for collaborative tools in design, gaming, and manufacturing sectors, as well as advancements in cloud computing technologies.

One of the primary growth factors in the 3D model sharing platform market is the rising demand for real-time collaboration tools among professionals in various industries. The need for efficient and streamlined communication during the design and development phases is pushing companies to adopt advanced 3D model sharing platforms. This trend is especially prevalent in the architecture, engineering, and construction (AEC) sectors, where multiple stakeholders need to work together seamlessly, regardless of their geographical locations. As the complexity of projects increases, so does the need for robust platforms that can handle intricate designs and facilitate effective collaboration.

Another significant factor contributing to market growth is the expansion of the gaming and entertainment industries. The rapid advancement of virtual reality (VR) and augmented reality (AR) technologies has led to a surge in demand for high-quality 3D models, which are essential for creating immersive experiences. Game developers and filmmakers are increasingly relying on 3D model sharing platforms to design detailed and realistic environments, characters, and objects. This not only enhances the user experience but also speeds up the development process, thereby giving companies a competitive edge in the market.

The education sector is also playing a crucial role in driving the growth of the 3D model sharing platform market. Educational institutions are incorporating 3D modeling into their curricula to provide students with hands-on experience and better prepare them for careers in design, engineering, and other related fields. The availability of user-friendly 3D model sharing platforms enables educators to easily share resources and collaborate with students in real-time, making learning more interactive and engaging. This trend is particularly strong in higher education institutions, where advanced design and engineering courses are prevalent.

From a regional perspective, North America holds a significant share of the 3D model sharing platform market, driven by the presence of major technology companies, a highly developed IT infrastructure, and a strong focus on innovation. The Asia Pacific region is anticipated to witness the highest growth during the forecast period, fueled by increasing investments in digital technologies, the rapid adoption of cloud computing, and the expanding gaming and entertainment industries in countries like China, Japan, and South Korea. Europe also represents a substantial market, with robust demand from the architecture and manufacturing sectors.

The emergence of Online 3D Printing Service has further revolutionized the way 3D models are utilized across various industries. These services provide users with the flexibility to bring their digital designs to life without the need for owning expensive 3D printing equipment. By leveraging online platforms, users can upload their 3D models and choose from a wide range of materials and finishes, making it easier to produce prototypes, custom parts, and even finished products. This accessibility has opened up new opportunities for small businesses and individual creators, enabling them to compete with larger companies by offering unique and personalized products. The convenience and cost-effectiveness of online 3D printing services are driving their adoption, particularly in sectors such as consumer goods, healthcare, and education.

Type Analysis

The 3D model sharing platform market can be segmented by type into cloud-based and on-premises solutions. The cloud-based segment is expected to dominate the market owing to the numerous advantages it offers, such as scalability, flexibility, and cost-effectiveness. Cloud-based platforms allow users to access 3D models from anywhere, at any time, which is particularly beneficial for teams working remotely or across different locations. This segment is also being driven by the increasing adoption of Software-as-a-Service (SaaS) models, which eliminate the need for companies to invest in

This is currently saved as a Scene Layer Package, which can be used by ArcGIS. To download the 3D Scene Layer, you have to first click the yellow "Open Content" button above, then this will take you to a separate screen where you can then click the blue "Download" button.If you are interested in a free trial of this software, you can visit the free trial section ESRI's Website HERE.

The NYC 3D Building Model is a publicly available model consisting of every building in New York City present in 2014. The model is subdivided by community district and includes base layers such as lots, streets, parks, and rail lines.