Shape Retrieval Contest (SHREC) Datasets
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Robustness
The common denominator of shape retrieval approaches is the creation of a shape descriptor or signature which captures the unique properties of the shape that distinguish it from shapes belonging to other classes on the one hand, and is invariant to a certain class of transformations a shape can undergo on the other. In rigid shape analysis, different types of invariance are rotation and translation. Dealing with non-rigid shapes requires compensating for the degrees of freedom resulting from deformations. In scanned shapes, connectivity and topological changes, noise, and holes are common plights. Other typical transformations encountered in shape retrieval problems, especially when dealing with Internet data include: scale, missing parts, different sampling and triangulation. The proposed benchmark evaluates the performance of shape retrieval on a large-scale dataset under a wide variety of different transformations.
SHREC 2010
SHREC 2011
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Correspondence finding
Correspondence and similarity are two intimately related problems in shape analysis. Defining optimal correspondence based on some structure preservation criterion, one can obtain a criterion of shape similarity as the amount of structure distortion. Finding correspondence between two shapes that would be invariant to a wide variety of transformations is thus a cornerstone problem in many approaches for shape similarity and retrieval. The proposed benchmark evaluates the performance of algorithms for establishing correspondence between shapes under a wide variety of different transformations.
SHREC 2010
SHREC 2011
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Feature detection and description
Feature-based approaches have recently become very popular in computer vision and image analysis application. Using these approaches, an image is described as a collection of local features ("visual words"), resulting in a representation referred to as a "bag of features". The bag of features paradigm relies heavily on the choice of the local feature descriptor. A common comparison of different image feature detection and description algorithms is the stability of the detected features and their invariance to different transformations applied to an image. In shape analysis, feature-based approaches have been introduced more recently and are becoming a promising direction in shape retrieval applications. The proposed benchmark evaluates the performance of shape feature detectors and descriptors under a wide variety of different transformations.
SHREC 2010
SHREC 2011
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