The aim of this track of SHREC'13 is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The final report of the track can be found here.
The dataset consists of ten classes. Each class is created as follows: First, two 3D meshes are considered and endowed with three different textures, for a total amount of six null models. Then, for each null shape 4 transformations are applied, including one non-rigid deformation, two non-metric-preserving deformations and one addititive noise deformation. What we get at the end is a database containing 24 models in each class, for a total amount of 240 textured shapes.
Ground thruth and evaluation
The collection to search in is made of hundreds of watertight mesh models. Meshes will be provided in one set of watertight meshes without self-intersections. Each model will be used in turn as a query against the remaining part of the database. For a given query, the goal of the track is to retrieve the most similar objects. The retrieval performance will be evaluated according to a ternary relevance scale: If a retrieved object shares both shape and texture with the query, then it is highly relevant; if it shares only shape, it is considered marginally relevant; otherwise, it is not relevant. The evaluation process will be thus based on a 2-level ground-thruth, by using the following evaluation measures: Precision-recall curves, nearest neighbor, first tier, second tier, normalized discounted cumulated gain and average dynamic recall.
Registration and other procedures
Each participant is requested to register to the track by sending an email to
Silvia Biasotti (email: email@example.com) and
Andrea Cerri (email:
with the subject "Registration for SHREC13: Retrieval on textured 3D models".
Then, you will receive an answer with the instructions to: