Name
Abstract | ||
|---|---|---|
The semantic segmentation problem has been widely studied in the computer vision community. However, state-of-the-art solutions based on deep learning are only available for 2D images. The lack of large annotated datasets makes more difficult the training of models with 3D images. In this work we propose to use the already available 2D deep learning based solutions to semantically segment the 3D environment for robotic applications. Concretely, deep learning applications provide the semantic labeling, and the geometrical information from RGB-D cameras along with the robot pose provides the 3D position. |
Year | Venue | Field |
|---|---|---|
2017 | ROBOT | Computer vision,Scale-space segmentation,Segmentation,Computer science,Segmentation-based object categorization,RGB color model,Artificial intelligence,Deep learning,Robot,Robotics,Semantic map |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina Romero-González | 1 | 3 | 3.52 |
| Jesus Martinez-gomez | 2 | 24 | 5.76 |
| Ismael García-varea | 3 | 275 | 36.16 |