The performance standard for ballistic-resistant body armor published by the National Institute of Justice (NIJ), NIJ Standard 0101.06, recommends estimating the perforation performance of body armor by performing a statistical analysis on V50 ballistic limit testing data. The first objective of this study is to evaluate and compare the estimations of the performance provided by different statistical methods applied to ballistic data generated in the laboratory.

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.

BEST VIEW SELECTION corresponds to the task of automatically selecting the most representative view of a 3D model.

This benchmark aims to provide tools to evaluate automatic best view selection algorithms. It consists of the preferred viewpoints of 68 models selected by 26 human subjects, and a code to calculate "View selection error". The subjective viewpoints were collected using a web-based subjective experiment where the users were asked to select the most informative view of a 3D model.

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.

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.

There are two objectives of this shape retrieval contest:

a) To evaluate partial similarity between query and target objects and retrieve complete 3D models that are relevant to a partial query object.

b) To retrieve 3D models those are relevant to a query depth map. 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

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.

This version of NIST Standard Reference Database 46 is a major enhancement to this widely used database which provides comprehensive coverage of interactions for aqueous systems of organic and inorganic ligands with protons and various metal ions and is based on the six-volume Critical Stability Constants by Martell and Smith. The new version contains 225 additional ligands, new data, data printing, rapid bibliography searching and more streamlined commands. New literature has resulted in revision and upgrading of 30% of previous data.

This dataset is called the "IIT CDIP collection". "CDIP" stands for "Complex Document Information Processing" and "IIT" stands for "Illinois Institute of Technology" who originally built the dataset. The dataset consists of documents from the states' lawsuit against the tobacco industry in the 1990s.