A study on the robustness of shape descriptors to common scanning artifacts
| Autor: | Tomislav Pribanić, Joaquim Salvi, Ferran Roure, Xavier Lladó, Josep Forest, Yago Diz |
|---|---|
| Přispěvatelé: | Ukita, Norimichi. Segawa, Eigo. Yui, Norichika., Ministerio de Ciencia e Innovación (Espanya) |
| Rok vydání: | 2015 |
| Předmět: |
Computer science
business.industry Outlier removal Visió per ordinador 3D registration 3D reconstruction SHOT Spin Image ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Visualització tridimensional (Informàtica) Pattern recognition Ranging Synthetic data Data modeling Robustness (computer science) Histogram Outlier Preprocessor Computer vision Three-dimensional display systems Artificial intelligence business |
| Zdroj: | MVA © Machine Vision Applications (MVA), 2015 14th IAPR International Conference on, 2015, p.522-525 Articles publicats (D-ATC) DUGiDocs – Universitat de Girona instname |
| DOI: | 10.1109/mva.2015.7153245 |
| Popis: | Registration is a fundamental problem in a myriad of applications ranging from heritage reconstruction to industrial applications. Descriptors are an important part of the registration pipeline as well as a very active research field. However, the sets used to illustrate descriptor performance have often undergone several preprocessing steps such as noise filtering, hole filling or outlier removal. These steps simplify the problem but are not readily available in many applications. In this paper we compare the performances of 4 state of the art shape descriptors: SHOT [1], Spin Image [2], FPFH [3] and 3DSC [4]. Experiments were carried out with real as well as synthetic data paying special attention to issues commonly present in real data (noise, outliers and low overlap). The method obtaining a best result overall is SHOT, based mostly on the results with synthetic data. Experiments with real data showed how state of the art descriptors are not yet able to produce optimal results in the most challenging scenarios This work has been supported by the FP7-ICT-2011-7 project PANDORA-Persistent Autonomy through Learning, Adaptation, Observation and Replanning (Ref 288273) funded by the European Commission and the project RAIMON-Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref CTM2011-29691-C02-02) funded by the Ministry of Economy and Competitiveness of the Spanish Government |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|