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.

Objective:
The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database which contains different types of models, such as generic, articulated, CAD and architecture models.

Thermal conductivity data acquired previously for the establishment of Standard Reference Material (SRM) 1450, Fibrous Glass Board, as well as subsequent renewals 1450a, 1450b, 1450c, and 1450d, are re-analyzed collectively and as individual data sets. Additional data sets for proto-1450 material lots are also included in the analysis (eleven data sets in total).

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.

We present correlations found between the tensile strengths of yarns extracted from armor and the V50 ballistic limit when significant degradation of the mechanical properties of the extracted yarns was observed. These studies provided the basis for a validation dataset from which we compare the experimentally measured V50 ballistic limit results to the theoretically predicted V50 results. This approach is promising for the development of a tool for fielded-armor-performance surveillance relying upon mechanical testing of armor coupon samples.

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.

This benchmark aims to provide tools to evaluate 3D Interest Point Detection Algorithms with respect to human generated ground truth. Please refer to the paper for more information about this benchmark: "Helin Dutagaci, Chun Pan Cheung, Afzal Godil: Evaluation of 3D interest point detection techniques via human-generated ground truth", The Visual Computer, 2012.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.

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.

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.

Note that this SRD supersedes SRD 64 Version 3.2. The NIST Electron Elastic-Scattering Cross-Section Database provides values of differential elastic-scattering cross sections, total elastic-scattering cross sections, phase shifts, and transport cross sections in electron-atom scattering for elements with atomic numbers from 1 to 96 and for electron energies between 50 eV and 300 keV (in steps of 1 eV).