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.

SRM 3235 Soy Milk - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining vitamins, elements, proximates, fatty acids, and amino acids in soy milk and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is a soy milk prepared by a commercial manufacturer. A unit of SRM 3235 consists of 10 ampoules, each containing approximately 10 mL of material. This data is public in the Certificate of Analysis for this material.

SRM 3252 Protein Drink Mix - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining proximates, fatty acids, cholesterol, vitamins, elements, and amino acids in protein drink mixes and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. This data is public in the Certificate of Analysis for this material.

SRM 3290 Dry Cat Food - This Standard Reference Material (SRM) is intended primarily for use in validating methods for determining proximates, fatty acids, vitamins, elements, and amino acids in dry cat food and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house control materials. This SRM is a blend of commercially available dry cat foods. A unit of SRM 3290 consists of five heat-sealed, aluminized pouches, each containing approximately 10 g of material. This data is public in the Certificate of Analysis for this material.

Standard Reference Material 3949 Folate Vitamers in Frozen Human Serum