On depth prediction for autonomous driving using self-supervised learning
Fuente:
arXiv
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929270974054400 |
|---|---|
| author | Boulahbal, Houssem |
| author_facet | Boulahbal, Houssem |
| contents | Perception of the environment is a critical component for enabling autonomous driving. It provides the vehicle with the ability to comprehend its surroundings and make informed decisions. Depth prediction plays a pivotal role in this process, as it helps the understanding of the geometry and motion of the environment. This thesis focuses on the challenge of depth prediction using monocular self-supervised learning techniques. The problem is approached from a broader perspective first, exploring conditional generative adversarial networks (cGANs) as a potential technique to achieve better generalization was performed. In doing so, a fundamental contribution to the conditional GANs, the acontrario cGAN was proposed. The second contribution entails a single image-to-depth self-supervised method, proposing a solution for the rigid-scene assumption using a novel transformer-based method that outputs a pose for each dynamic object. The third significant aspect involves the introduction of a video-to-depth map forecasting approach. This method serves as an extension of self-supervised techniques to predict future depths. This involves the creation of a novel transformer model capable of predicting the future depth of a given scene. Moreover, the various limitations of the aforementioned methods were addressed and a video-to-video depth maps model was proposed. This model leverages the spatio-temporal consistency of the input and output sequence to predict a more accurate depth sequence output. These methods have significant applications in autonomous driving (AD) and advanced driver assistance systems (ADAS). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06194 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On depth prediction for autonomous driving using self-supervised learning Boulahbal, Houssem Computer Vision and Pattern Recognition Perception of the environment is a critical component for enabling autonomous driving. It provides the vehicle with the ability to comprehend its surroundings and make informed decisions. Depth prediction plays a pivotal role in this process, as it helps the understanding of the geometry and motion of the environment. This thesis focuses on the challenge of depth prediction using monocular self-supervised learning techniques. The problem is approached from a broader perspective first, exploring conditional generative adversarial networks (cGANs) as a potential technique to achieve better generalization was performed. In doing so, a fundamental contribution to the conditional GANs, the acontrario cGAN was proposed. The second contribution entails a single image-to-depth self-supervised method, proposing a solution for the rigid-scene assumption using a novel transformer-based method that outputs a pose for each dynamic object. The third significant aspect involves the introduction of a video-to-depth map forecasting approach. This method serves as an extension of self-supervised techniques to predict future depths. This involves the creation of a novel transformer model capable of predicting the future depth of a given scene. Moreover, the various limitations of the aforementioned methods were addressed and a video-to-video depth maps model was proposed. This model leverages the spatio-temporal consistency of the input and output sequence to predict a more accurate depth sequence output. These methods have significant applications in autonomous driving (AD) and advanced driver assistance systems (ADAS). |
| title | On depth prediction for autonomous driving using self-supervised learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.06194 |