AndreaBozzo/ceres-open-data-index huggingface dataset:
Atom-Based Device Navigator (ABDNavigator)

ABDNavigator is software written for scanning probe (e.g. scanning tunneling microscope) control and sample navigation of atom-based devices.  Here atom-based devices refer to devices whose components span from the micron scale range down to sub nanometer and are probed, and typically fabricated by scanning tunneling microscope.

Tags: atom-based-fabrication, hydrogen-lithography, scanning-tunneling-microscope, stm
Organization: National Institute of Standards and Technology
SHREC'12 Track: Sketch-Based 3D Shape Retrieval

The objective of this SHREC'12 track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using both hand-drawn and standard line drawings sketch queries on a watertight 3D model dataset.

Sketch-based 3D model retrieval is to retrieve 3D models using a 2D sketch as input. This scheme is intuitive and convenient for users to search for relevant 3D models and also important for several applications including sketch-based modeling and sketch-based shape recognition. However, most existing 3D model retrieval algorithms target the Query-by-Model framework, that is, using existing 3D models as queries. Much less research work has been done regarding the Query-by-Sketch framework. In addition, until now there was no comprehensive evaluation or comparison for available sketch-based retrieval algorithms. Considering of this, we organized this track to foster this challenging research area by providing a common sketch-based retrieval benchmark and soliciting retrieval results from current state-of-the-art retrieval methods for comparison. We also provide corresponding evaluation code for computing a set of performance metrics similar to those used in the Query-by-Model retrieval technique.

Dataset: 3D target Models is 400, 2D query set comprises two subsets: (1) Hand-drawn sketches, and (2) Standard line drawings

Please cite the paper:
[1] B. Li, T. Schreck, A. Godil, M. Alexa, T. Boubekeur, B. Bustos, J. Chen, M. Eitz, T. Furuya, K. Hildebrand, S. Huang, H. Johan, A. Kuijper, R. Ohbuchi, R. Richter, J. M. Saavedra, M. Scherer, T. Yanagimachi, G. J. Yoon, S. M. Yoon, In: M. Spagnuolo, M. Bronstein, A. Bronstein, and A. Ferreira (eds.), SHREC'12 Track: Sketch-Based 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012), 2012.

Tags: 3d-models, 3d-shape-retrieval, evaluation, hand-drawn-sketch-data, standard-line-drawings
Organization: National Institute of Standards and Technology
Speed of Sound Measurements of Binary Mixtures of 1,1,1,2-Tetrafluoroethane (R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants

Speed of sound data measured in the search for a Low-GWP Alternative Refrigerant Blends for HFC-134a. Speed of sound data are being used to improve current REFPROP mixture models for binary mixtures of R-134a, R-1234yf, and R-1234ze(E).

Tags: r-1234yf, r-1234zee, r-134a, refrigerant-blends, speed-of-sound
Organization: National Institute of Standards and Technology
Effect of temperature and strain rate on ultra high molar mass polyethylene (UHMMPE) fibers

This dataset consists of tensile tests performed by directly gripping single UHMMPE fibers. The tests were conducted at 11 different temperatures, and at both quasi-static and dynamic loading rates. The testing procedures are described in NISTIR 8265.

Tags: mechanical-properties, polyethylene, rate-effects, single-fiber, strength, temperature-effects, tensile-testing
Organization: National Institute of Standards and Technology
SHREC'12 Track: Generic 3D Shape Retrieval

Objective: The objective of this track is to evaluate the performance of 3D shape retrieval approaches on Generic 3D Dataset.

Introduction: With the increasing number of 3D models are created every day and stored in databases, effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of the results of different 3D shape retrieval approaches.

Task description:
The task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix.

Dataset:
All the 3D models in the generic 3D dataset will be based on the combination of models from our previous generic 3D benchmarks. In this generic 3D dataset, there will be 1200 3D models, classified into 60 object categories based mainly on visual similarity. The file format used to represent the 3D models will be the ASCII Object File Format (*.off).

Evaluation Methodology:
We will employ the following evaluation measures: Precision-Recall curve (PR), Nearest Neighbor (NN), First-Tier (FT), Second-Tier (ST), E-Measure (E), Discounted Cumulative Gain (DCG) and Average Precision (AP).

Please cite the paper:
B. Li, A. Godil, M. Aono, X. Bai, T. Furuya, L. Li, R. Lopez-Sastre, H. Johan, R. Ohbuchi, C. Redondo-Cabrera, A. Tatsuma, T. Yanagimachi, S. Zhang, In: M. Spagnuolo, M. Bronstein, A. Bronstein, and A. Ferreira (eds.): SHREC'12 Track: Generic 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012), 2012. http://dx.doi.org/10.2312/3DOR/3DOR12/119-126

Tags: 3d-models, 3d-shape-retrieval, evaluation-and-measurement-science
Organization: National Institute of Standards and Technology
SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval

Objective: 
The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset for querying from a generic 3D model dataset.

Introduction:
Sketch-based 3D model retrieval is focusing on retrieving relevant 3D models using sketch(es) as input. This scheme is intuitive and convenient for users to learn and search for 3D models. It is also popular and important for related applications such as sketch-based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard.

Please cite the papers:

[1] B. Li, Y. Lu, Afzal Godil, Tobias Schreck, Masaki Aono, Henry Johan, Jose M. Saavedra, S. Tashiro, In: S. Biasotti, I. Pratikakis, U. Castellani, T. Schreck, A. Godil, and R. Veltkamp (eds.), SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2013 (3DOR 2013): 89-96, 2013. 

[2] B. Li, Y. Lu, A. Godil, T. Schreck, B. Bustos, A. Ferreira, T. Furuya, M.J. Fonseca, H. Johan, T. Matsuda, R. Ohbuchi, P.B. Pascoal, J.M. Saavedra, A comparison of methods for sketch-based 3D shape retrieval, Computer Vision and Image Understanding (2013), doi: http://dx.doi.org/10.1016/j.cviu.2013.11.008.

Tags: evaluation, large-scale-benchmark, shrec-contest, sketch-based-3d-model-retrieval
Organization: National Institute of Standards and Technology
SHREC'14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval

The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.

Sketch-based 3D model retrieval is to retrieve relevant 3D models using sketch(es) as input. This scheme is intuitive and convenient for users to learn and search for 3D models. It is also popular and important for related applications such as sketch-based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard. However, most existing 3D model retrieval algorithms target the Query-by-Model framework which uses existing 3D models as queries. 

Much less research work has been done regarding the Query-by-Sketch framework. Large scale sketch-based 3D shape retrieval has received more and more attentions in the community of content-based 3D object retrieval. The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a comprehensive 3D model dataset. The benchmark contains 12,680 sketches and 8,987 3D models, divided into 171 distinct classes. In this track, 12 runs were submitted by 4 groups and their retrieval performance was evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote the progress of this research direction for the 3D model retrieval community.

Evaluation Method:
The performance is evaluated by Precision-Recall (PR) graph, Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), E-Measures (E), Discounted Cumulated Gain (DCG) and Average Precision (AP)

Please cite the papers:

[1] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Hongbo Fu, Takahiko Furuya, Haisheng Li, Jianzhuang Liu, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma, Yajuan Wan, Chaoli Zhang, Changqing Zou. A Comparison of 3D Shape Retrieval Methods Based on a Large-scale Benchmark Supporting Multimodal Queries. Computer Vision and Image Understanding, November 4, 2014. 

[2] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Hongbo Fu, Takahiko Furuya, Henry Johan, Jianzhuang Liu, Ryutarou Ohbuchi, Atsushi Tatsuma, Changqing Zou. SHREC' 14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval. Eurographics Workshop on 3D Object Retrieval 2014 (3DOR 2014): 121-130, 2014.

Tags: 3d-models, 3d-shape-retrieval, evaluation, sketches
Organization: National Institute of Standards and Technology
SHREC'15: Range Scans based 3D Shape Retrieval

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

Tags: 3d-models, 3d-shape-retrieval, evaluation-and-measurement-science, range-scans
Organization: National Institute of Standards and Technology
pySCATMECH: A Python interface to the SCATMECH C++ library of polarized light scattering codes

SCATMECH is a library of object-oriented C++ computer codes originally developed for disseminating models for polarized light scattering from surfaces and aerosols and for diffraction from gratings. The pySCATMECH package has been developed as an interface to the SCATMECH library, simplifying use of the codes and allowing for more rapid development of software for these applications.

Tags: aerosol, bidirectional-reflectance, brdf, diffuse, gratings, mie-scattering, modeling, mueller-matrix, polarization, python, roughness, scatter, surface
Organization: National Institute of Standards and Technology
SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes

Non-rigid 3D objects are commonly seen in our surroundings. However, previous efforts have been mainly devoted to the retrieval of rigid 3D models, and thus comparing non-rigid 3D shapes is still a challenging problem in content-based 3D object retrieval. Therefore, we organize this track to promote the development of non-rigid 3D shape retrieval.
The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-scale database of non-rigid 3D watertight meshes generated by our group.

Task description: The task is to evaluate the dissimilarity between every two objects in the database and then output the dissimilarity matrix.

Data set: Our large-scale database consists of 600 non-rigid 3D objects (see the figure for some examples) that are created by our group using some modeling software and our own codes. We classified these models properly to make sure that every class contains equal number of models. The models are represented as watertight triangle meshes and the file format is selected as the ASCII Object File Format (*.off).
(Note that: Some of these models we recreated and modified with permission are originally from several publicly available databases: such as McGill database, TOSCA shapes, Princeton Shape Benchmark, etc.)

Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).

Please Cite the paper : SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes, Z. Lian, A. Godil, B. Bustos, M. Daoudi, J. Hermans, S. Kawamura, Y. Kurita, G. Lavouï¿½, H.V. Nguyen, R. Ohbuchi, Y. Ohkita, Y. Ohishi, F. Porikli, M. Reuter, I. Sipiran, D. Smeets, P. Suetens, H. Tabia, and D. Vandermeulen , In: H. Laga and T. Schreck, A. Ferreira, A. Godil, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2011. http://dx.doi.org/10.2312/3DOR/3DOR11/079-088


Tags: 3d-non-rigid-models, 3d-non-rigid-shape-retrieval, 3d-shape-analysis, evaluation
Organization: National Institute of Standards and Technology
SHREC'10 track: Range scan retrieval

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



Tags: 3d-models, 3d-range-scans, 3d-shape-retrieval, evaluation
Organization: National Institute of Standards and Technology
SHREC'10 Track: Generic 3D Warehouse

The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a Generic 3D shape benchmark based on the Google 3D Warehouse.

Introduction: With the increasing number of 3D models are created everyday and stored in databases. Effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of results of different 3D shape retrieval approaches. Here, we propose a new publicly available 3D shape benchmark based on the Google 3D Warehouse to advance the state of art in 3D shape retrieval

Task description: The task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix.

Data set: All the 3D models in the generic shape benchmark were acquired by a web crawler from the Google 3D Warehouse. To classify the 3D shape models into a ground truth database, one person based on the Google tags has classified objects into ground truth categories based mainly on visual similarity. In this benchmark, there will be over three thousand 3D models. The file format used to represent the 3D models will be 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: SHREC'10 Track: Generic 3D Warehouse., T.P. Vanamali, A. Godil, H. Dutagaci,T. Furuya, Z. Lian, R. Ohbuchi, In: M. Daoudi, T. Schreck, M. Spagnuolo, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2010.


Tags: 3d-models, 3d-shape-analysis, 3d-shape-retrieval, evaluation-and-measurement-science
Organization: National Institute of Standards and Technology
SHREC'10 Track: Non-rigid 3D Shape Retrieval

Non-rigid 3D objects are commonly seen in our surroundings. However, previous efforts have been mainly devoted to the retrieval of rigid 3D models, and thus comparing non-rigid 3D shapes is still a challenging problem in content-based 3D object retrieval. Therefore, we organize this track to promote the development of non-rigid 3D shape retrieval.

The objective of this track is to evaluate the performance of 3D shape retrieval approaches on the subset of a publicly available non-rigid 3D models database----McGill Articulated Shape Benchmark database.

Task description: The task is to evaluate the dissimilarity between every two objects in the database and then output the dissimilarity matrix.

Data set: The McGill Articulated Shape Benchmark database consists of 255 non-rigid 3D models which are classified into 10 categories. The maximum number of the objects in a class is 31, while the minimum number is 20.
200 models are selected (or modified) to generate our test database to ensure that every class contains equal number of models. The models are represented as watertight triangle meshes and the file format is selected as the ASCII Object File Format (*.off). The original database is publicly available on the website: http://www.cim.mcgill.ca/~shape/benchMark/

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: SHREC'10 Track: Non-rigid 3D Shape Retrieval., Z. Lian, A. Godil, T. Fabry, T. Furuya, J. Hermans, R. Ohbuchi, C. Shu, D. Smeets, P. Suetens, D. Vandermeulen, S. Wuhrer 
In: M. Daoudi, T. Schreck, M. Spagnuolo, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2010.


Tags: 3d-shape-analysis, nonrigid-3d-models, nonrigid-3d-shape-retrieval
Organization: National Institute of Standards and Technology
SRM 1450e Fibrous Glass Board

SRM 1450e Fibrous Glass Board - This Standard Reference Material (SRM) is intended primarily for use in the measurement of the thermal conductivity or thermal resistance of insulation materials. SRM 1450e is a high-density fibrous glass board certified for bulk density and thermal conductivity. The SRM can be used in conjunction with ASTM C177 or ASTM C518. A unit of SRM 1450e consists of a square panel of fibrous glass and phenolic binder molded into a semi-rigid board. The nominal dimensions of a unit are 610 mm × 610 mm × 25 mm, and the bulk density of the material lot ranges from 110 kg/m3 to 154 kg/m3. Certified values of thermal conductivity are valid for mean temperatures from 280 K to 360 K and barometric pressures from 60 kPa to sea-level pressure (101.325 kPa).

Tags: bulk-density, certified-reference-material, fit, guarded-hot-plate, high-density-molded-fibrous-glass-board, model, regression-analysis, standard-reference-material, thermal-conductivity, thermal-insulation
Organization: National Institute of Standards and Technology
SHREC'09 Track: Generic Shape Retrieval

The objectives of this shape retrieval contest are to evaluate the performance of 3D shape retrieval approaches on a new generic 3D shape benchmark.

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.

Data set: In this new generic benchmark there are 800 3D models. The target database contains 720 complete 3D models, which are categorized into 40 classes. In each class there are 18 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).

Paper: Godil, A., Dutagaci, H., Akgül, C.B., Axenopoulos, A., Bustos, B., Chaouch, M., Daras, P., Furuya, T., Kreft, S., Lian, Z. and Napoleon, T., 2009, March. SHREC'09 Track: Generic shape retrieval. In 3DOR (pp. 61-68). http://dx.doi.org/10.2312/3DOR/3DOR09/061-068

Tags: 3d-models, 3d-shape-retrieval, evaluation, generic-shape-retrieval
Organization: National Institute of Standards and Technology