Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks
![Thumbnail](/dspace/bitstream/handle/2183/34119/XoveTIC_2023_proceedings_Parte47.pdf.jpg?sequence=5&isAllowed=y)
Use this link to cite
http://hdl.handle.net/2183/34119
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)
Metadata
Show full item recordTitle
Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation TasksDate
2023Abstract
[Abstract] Deep neural networks are well known for demanding large amounts of training data,
motivating the appearance of multiple synthetic datasets covering multiple domains. However,
synthetic datasets have not yet outperformed real data for autonomous driving applications, particularly
for semantic segmentation tasks. Thus, a deeper comprehension about how the parameters
involved in synthetic data generation could help in creating better synthetic datasets. This
work provides a summary review of prior research covering how image noise, camera noise and
rendering photorealism could affect learning tasks. Furthermore, we presents novel experiments
aimed at advancing our understanding around generating synthetic data for autonomous driving
neural networks aimed at semantic segmentation
Keywords
Redes neuronales (Informática)
Datos sintéticos
Segmentación semántica
Datos sintéticos
Segmentación semántica
Description
Cursos e Congresos , C-155
Editor version
Rights
Attribution 4.0 International (CC BY 4.0)