We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry. Finally, we also have done some in a Structural Bioinformatics, Bio-Image analysis and retrieval.

NADA (Not-A-Database) is an easy-to-use geometric shape data generator that allows users to define non-uniform multivariate parameter distributions to test novel methodologies. The full open-source package is provided at GIT:NA_DAtabase. See Technical Report for details on how to use the provided package.

This database includes 3 repositories:

• NADA_Dis: Is the model able to correctly characterize/Disentangle a complex latent space?
  The repository contains 3x100,000 synthetic black and white images to test the ability of the models to correctly define a proper latent space (e.g., autoencoders) and disentangle it. The first 100,000 images contain 4 shapes and uniform parameter space distributions, while the other images have a more complex underlying distribution (truncated Gaussian and correlated marginal variables).
  
  
• NADA_OOD: Does the model identify Out-Of-Distribution images?
  The repository contains 100,000 training images (4 different shapes with 3 possible colors located in the upper left corner of the canvas) and 6x100,000 increasingly different sets of images (changing the color class balance, reducing the radius of the shape, moving the shape to the lower left corner) providing increasingly challenging out-of-distribution images.
  This can help to test not only the capability of a model, but also methods that produce reliability estimates and should correctly classify OOD elements as "unreliable" as they are far from the original distributions.
  
  
• NADA_AlEp: Does the model distinguish between different types (Aleatoric/Epistemic) of uncertainties?
  The repository contains 5x100,000 images with different type of noise/uncertainties:
  
  • NADA_AlEp_0_Clean: Dataset clean of noise to use as a possible training set.
  • NADA_AlEp_1_White_Noise: Epistemic white noise dataset. Each image is perturbed with an amount of white noise randomly sampled from 0% to 90%.
  • NADA_AlEp_2_Deformation: Dataset with Epistemic deformation noise. Each image is deformed by a randomly amount uniformly sampled between 0% and 90%. 0% corresponds to the original image, while 100% is a full deformation to the circumscribing circle.
  • NADA_AlEp_3_Label: Dataset with label noise. Formally, 20% of Triangles of a given color are missclassified as a Square with a random color (among Blue, Orange, and Brown) and viceversa (Squares to Triangles). Label noise introduces \textit{Aleatoric Uncertainty} because it is inherent in the data and cannot be reduced.
  • NADA_AlEp_4_Combined: Combined dataset with all previous sources of uncertainty.

Each image can be used for classification (shape/color) or regression (radius/area) tasks.

All datasets can be modified and adapted to the user's research question using the included open source data generator.

Boundary Shapes for the US Census 'Places' 2021

The dataset collection in question is a compilation of tables that are related to each other. These tables have been gathered from the website of Lantmäteriet (The Swedish Land Survey) in Sweden. The arrangement of the data within each table is systematic, with rows and columns aiding in the interpretation of the information. The dataset is robust and diverse, contributing to a comprehensive understanding of the subject matter it pertains to. It's worth noting that this dataset collection includes a specific table that is projected to offer valuable insights for the year 2024. Tables Historical Versioning of Orthophoto Outcomes 2024TSV The table in focus is a historical data table which is part of a larger dataset collection. It keeps track of the version history of base table rows with the help of specific columns that mark the start and end dates of each row. These dates indicate when the data row was extracted from the source and when a new version was subsequently extracted. The fact that a row is the latest version is indicated by a null value in the end date column. In addition to the version tracking, the table also contains geographical information which has been converted from the shapefile format, a common format for storing geographical features. This geographical data can represent various geographical features like points, lines, or polygons (areas). The data for this table is sourced from the website of...

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.

This data set contains Philip Stooke shape models for 243 Ida, 253 Mathilde, 951 Gaspra, comet Halley, J5 Amalthea, J14 Thebe, N7 Larissa, N8 Proteus, S10 Janus, S11 Epimetheus, S16 Prometheus, and S17 Pandora, based on optical data from the NEAR, Galileo, Giotto, Vega 1, Vega 2, and Voyager missions.

This data set contains the file for the models weights trained on the shape detector data set. This is a InceptionV3 model which achieved almost 100% accuracy onboard training and testing dataset..

The 2D shape structure dataset: A user annotated open access database

A Carlier, K Leonard, S Hahmann, G Morin, M Collins

This code was tested on Matlab R2015a, on Ubuntu 14.04 and on Mac OS 10.9.5.

Testdata for the eShape project

This data set contains a collection of shape models and their associated reference frame for the Rosetta target 67P/Churyumov-Gerasimenko 1 (1969 R1). These were produced by Rosetta mission teams, based on OSIRIS and NAVCAM image data obtained at the comet. This is version 2.0 of this data collection. Since the last version, the SPG_LAM_PSI SHAP5, SPG_DLR SHAP4S, and SPC_ESA MTP019 shape models have been added to this collection.

Dataset relative to the following publication:

Schmidt, F., Hegele, M., & Fleming, R. W. (2017). Perceiving animacy from shape. Journal of Vision, 17, 10. http://dx.doi.org/10.1167/17.11.10

Each folder contains the data relative to one experiment and a text file with comments.

Data for Stroessner, S.J., Benitez, J., Perez, M.A., Wyman, A.B., Carpinella, C.M., & Johnson, K.L. (Under review). What's In a shape? Evidence of gender category associations with basic forms.

This dataset is a subset of a collection of statistically derived body shapes developed by the Institute of Biomechanics (www.ibv .org) for the InKreate project (https://www.inkreate.eu/). The purpose of the collection is to offer a set of ready-to-use realistic body shapes that could be used for design development. Given the purpose of the collection, statistically available body shapes had to be grouped into a finite number of categories using parameters familiar to designers.

The original collection of body models consists of 32 female bodies and 24 male bodies. Combined with four poses, the final collection consists of 224 realistic avatars. The released subset includes 8 females and 6 males. This subset can serve to show design students how the shape of the body varies realistically according to its size and shape.

This data set contains the Peter Thomas shape models for small solar system bodies, as well as image mosaics constructed from these models. The current version of the data set contains the following: 243 Ida, 951 Gaspra, M1 Phobos, M2 Deimos, S7 Hyperion, S10 Janus, S11 Epimetheus.

This data file is used to define the geographical path or shape that each transit route follows. This file contains information that describes the exact path of a route, using a series of geospatial coordinates (latitude and longitude points) that form a line representing the route's path

The spatial layout of cities is an important feature of urban form, highlighted by urban planners but overlooked by economists. This paper investigates the causal economic implications of city shape in India. I measure cities’ geometric properties over time using satellite imagery and historical maps. I develop an instrument for urban shape based on geographic obstacles encountered by expanding cities. Compact city shape is associated with faster population growth and households display positive willingness to pay for more compact layouts. Transit accessibility is an important channel. Land use regulations can contribute to deteriorating city shape.

African states are both unusually large and well known for having artificial borders created during the colonial period. While African state size and shape have been previously shown to be correlated with negative development outcomes, no one has heretofore examined the origins of either phenomenon. Here, I show that African state size and shape are not arbitrary but are rather a consequence of Africa's low pre-colonial population density, whereby low-density areas were consolidated into unusually large colonial states with artificial borders. I also show that state size has a strong negative relationship with pre-colonial trade and that trade and population density alone explain the majority of the variation in African state size. Finally, I do not find a relationship between population density and state size or shape among non-African former colonies, thereby emphasizing the distinctiveness of modern African state formation.

Optical shape models of 10 planetary moons and asteroids, derived from spacecraft imaging by Philip Stooke.

PVDF based graft copolymer was synthesized. Shape memory behavior of the obtained polymers were investigated.

1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as: (i) ordinations of the units, where interpoint distances optimally display the measured differences; (ii) clustering the units into homogeneous clusters; or (iii) assessing differences between pre-specified groups of units (e.g., regions, periods, treatment-control groups). 2. These methods all conceal a fundamental question: To what extent are the differences between the sampling units, computed according to the chosen proximity function, capturing the "size" in the multivariate observations, or their "shape"? "Size" means the overall level of the measurements: for example, some samples contain higher total abundances or more biomass, others less. "Shape" mea...