Using GNG on 3D Object Recognition in Noisy RGB-D data
- Authors
- Rangel, José Carlos; Morell, Vicente; Cazorla, Miguel; Orts-Escolano, Sergio; García Rodríguez, José
- Format
- Article
- Status
- publishedVersion
- Description
The object recognition task on 3D scenes is a growing research field that faces some problems relative to the use of 3D point clouds. In this work, we focus on dealing with the noise in the clouds through the use of the Growing Neural Gas (GNG) network filtering algorithm. The GNG method is able to represent the input data with a desired amount of neurons while preserving the topology of the input space. The selected recognition pipeline works describing extracted keypoints of the clouds, grouping and comparing it to detect the presence of an object in the scene, through a hypothesis verification algorithm. Experiments show how the GNG method yields better recognitions results that others filtering algorithms when noise is present.
The object recognition task on 3D scenes is a growing research field that faces some problems relative to the use of 3D point clouds. In this work, we focus on dealing with the noise in the clouds through the use of the Growing Neural Gas (GNG) network filtering algorithm. The GNG method is able to represent the input data with a desired amount of neurons while preserving the topology of the input space. The selected recognition pipeline works describing extracted keypoints of the clouds, grouping and comparing it to detect the presence of an object in the scene, through a hypothesis verification algorithm. Experiments show how the GNG method yields better recognitions results that others filtering algorithms when noise is present.
- Publication Year
- 2019
- Language
- eng
- Topic
- Three-dimensional displays
Robustness
Three-dimensional displays
Robustness
- Repository
- RI de Documento Digitales de Acceso Abierto de la UTP
- Get full text
- https://ieeexplore.ieee.org/abstract/document/7280353/keywords#keywords
https://ridda2.utp.ac.pa/handle/123456789/9439
- Rights
- embargoedAccess
- License