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.

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.

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.

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.

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.

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.

This tool calculates metrics for investment analysis documented in NIST Advanced manufacturing Series 200-5. It calculates net present value, internal rate of return, and payback period along with executing sensitivity analysis using Monte Carlo techniques. The tool helps to identify the most economical projects/investments. For instance, it could be used to identify the most economical heating and cooling system or it might be used to rank a set of potential investments.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of iodide. A unit of SRM 3180 consists of five 5 mL, sealed, amber, borosilicate glass ampoules of solution prepared gravimetrically to contain a known mass fraction of iodide. The matrix of the solution is water containing sodium sulfite at a concentration of approximately 0.01 mol/L and sodium hydroxide at a concentration of approximately 0.005 mol/L. This data is public in the Certificate of Analysis for this material.