Feature-based similarity search in 3D object databases

By Benjamin Bustos, Daniel A. Keim, Dietmar Saupe, Tobias Schreck, Dejan V. Vranic. CSUR, 2005

433 citations recorded by the source corpus.

The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/31836ea66900873fb6b8105fe241414de80dbdbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Spatiotemporal representation of 3D skeleton joints-based action recognition using modified spherical harmonics (2016, neutral): “Such as, face recognition under unknown lighting challenge was adopted by (Zhang and Samaras, 2006), as well as 3D object retrieval (Bustos et al., 2005), and volumetric descriptors (Vranic, 2003).”
Shape google: Geometric words and expressions for invariant shape retrieval (2011, methodology): “The reader is referred to the review paper [Bustos et al. 2005] and the recent benchmarks [Bronstein et al.”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, methodology): “These methods have been widely used in the context of 3D shape retrieval and classification [16], [17].”
A survey of content based 3D shape retrieval methods (2004, neutral): “Bustos et al. [ 24 ] focus on feature based methods.”
Contextual Part Analogies in 3D Objects (2010, background): “Shape matching is also an active topic of research, and numerous shape signatures based on geometry and topology have been proposed (Bustos et al. 2005; Gal et al. 2007; Tangelder and Veltkamp 2008).”
Developing an engineering shape benchmark for CAD models (2006, methodology): “However, several other twodimensional view-based approaches have been proposed in the literature, including aspect graphs [45], which were used in computer vision research.”