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Robust volumetric shape descriptor
This paper introduces a volume-based shape descriptor that is robust with respect to changes in pose and topology. We use modified shape distributions of [OFCD02] in conjunction with the interior distances and barycentroid potential that are based on ...
A robust 3D interest points detector based on Harris operator
With the increasing amount of 3D data and the ability of capture devices to produce low-cost multimedia data, the capability to select relevant information has become an interesting research field. In 3D objects, the aim is to detect a few salient ...
Semantics-driven approach for automatic selection of best views of 3D shapes
We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes share the same salient features that discriminate them from the models of ...
The fast reject schema for part-in-whole 3D shape matching
This paper proposes a new framework for an efficient detection of template shapes within a target 3D model, or scene. The proposed approach distinguishes from the previous literature because the part-in-whole matching between the template and the target ...
Feature selection for enhanced spectral shape comparison
In the context of shape matching, this paper proposes a framework for selecting the Laplacian eigenvalues of 3D shapes that are more relevant for shape comparison and classification. Three approaches are compared to identify a specific set of ...
Learning the compositional structure of man-made objects for 3D shape retrieval
While approaches based on local features play a more and more important role for 3D shape retrieval, the problems of feature selection and similarity measurement between sets of local features still remain open tasks. Common algorithms usually measure ...
Person independent 3D facial expression recognition by a selected ensemble of SIFT descriptors
Facial expression recognition has been addressed mainly working on 2D images or videos. In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that relies ...
Fast human classification of 3D object benchmarks
Although a significant number of benchmark data sets for 3D object based retrieval systems have been proposed over the last decade their value is dependent on a robust classification of their content being available. Ideally researchers would want ...
SHREC'10 track: large scale retrieval
- Remco C. Veltkamp,
- Geert-Jan Giezeman,
- Hannah Bast,
- Thomas Baumbach,
- Takahiko Furuya,
- Joachim Giesen,
- Afzal Godil,
- Zhouhui Lian,
- Ryutarou Ohbuchi,
- Waqar Saleem
This paper is a report on the 3D Shape Retrieval Constest 2010 (SHREC'10) track on large scale retrieval. This benchmark allows evaluating how wel retrieval algorithms scale up to large collections of 3D models. The task was to perform 40 queries in a ...
SHREC'10 track: robust shape retrieval
- A. M. Bronstein,
- M. M. Bronstein,
- U. Castellani,
- B. Falcidieno,
- A. Fusiello,
- A. Godil,
- L. J. Guibas,
- I. Kokkinos,
- Z. Lian,
- M. Ovsjanikov,
- G. Patané,
- M. Spagnuolo,
- R. Toldo
The 3D Shape Retrieval Contest 2010 (SHREC'10) robust shape retrieval benchmark simulates a retrieval scenario, in which the queries include multiple modifications and transformations of the same shape. The benchmark allows evaluating how algorithms ...
SHREC'10 track: feature detection and description
- A. M. Bronstein,
- M. M. Bronstein,
- B. Bustos,
- U. Castellani,
- M. Crisani,
- B. Falcidieno,
- L. J. Guibas,
- I. Kokkinos,
- V. Murino,
- M. Ovsjanikov,
- G. Patané,
- I. Sipiran,
- M. Spagnuolo,
- J. Sun
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. The SHREC'10 feature detection and description benchmark simulates the feature ...
SHREC'10 track: correspondence finding
- A. M. Bronstein,
- M. M. Bronstein,
- U. Castellani,
- A. Dubrovina,
- L. J. Guibas,
- R. P. Horaud,
- R. Kimmel,
- D. Knossow,
- E. Von Lavante,
- D. Mateus,
- M. Ovsjanikov,
- A. Sharma
The SHREC'10 correspondence finding benchmark simulates a one-to-one shape matching scenario, in which one of the shapes undergoes multiple modifications and transformations. The benchmark allows evaluating how correspondence algorithms cope with ...
SHREC'10 track: generic 3D warehouse
In this paper we present the results of the 3D Shape Retrieval Contest 2010 (SHREC'10) track Generic 3D Warehouse. The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on a large Generic benchmark based on the ...
SHREC'10 track: non-rigid 3D shape retrieval
- Z. Lian,
- A. Godil,
- T. Fabry,
- T. Furuya,
- J. Hermans,
- R. Ohbuchi,
- C. Shu,
- D. Smeets,
- P. Suetens,
- D. Vandermeulen,
- S. Wuhrer
Non-rigid shape matching is one of the most challenging fields in content-based 3D object retrieval. The aim of the 3D Shape Retrieval Contest 2010 (SHREC'10) track on non-rigid 3D shape retrieval is to evaluate and compare the effectiveness of ...
SHREC'10 track: range scan retrieval
The 3D Shape Retrieval Contest 2010 (SHREC'10) on range scan retrieval aims at comparing algorithms that match a range scan to complete 3D models in a target database. The queries are range scans of real objects, and the objective is to retrieve ...
SHREC'10 track: protein model classification
- L. Mavridis,
- V. Venkatraman,
- D. W. Ritchie,
- N. Morikawa,
- R. Andonov,
- A. Cornu,
- N. Malod-Dognin,
- J. Nicolas,
- M. Temerinac-Ott,
- M. Reisert,
- H. Burkhardt,
- A. Axenopoulos,
- P. Daras
This paper presents the results of the 3D Shape Retrieval Contest 2010 (SHREC'10) track Protein Models Classification. The aim of this track is to evaluate how well 3D shape recognition algorithms can classify protein structures according to the CATH [...