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Camera and LiDAR Fusion For Point Cloud Semantic Segmentation
Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic...
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| Format: | Thesis |
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AUC Knowledge Fountain
2022
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| _version_ | 1867613421169541120 |
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| access_status_str | Open Access |
| author | Abdelkader, Ali |
| author_browse | Abdelkader, Ali |
| author_facet | Abdelkader, Ali |
| author_sort | Abdelkader, Ali |
| collection | Thesis |
| description | Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from LiDAR and camera sensors to improve the segmentation of LiDAR point clouds. It also addresses the question of which fusion level in this deep learning framework provides better performance. This was done following an empirical approach in which different fusion models were designed and evaluated against each other using SemanticKITTI dataset. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2890 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:51.500Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2890 Camera and LiDAR Fusion For Point Cloud Semantic Segmentation Abdelkader, Ali Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from LiDAR and camera sensors to improve the segmentation of LiDAR point clouds. It also addresses the question of which fusion level in this deep learning framework provides better performance. This was done following an empirical approach in which different fusion models were designed and evaluated against each other using SemanticKITTI dataset. 2022-01-31T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1871 https://fount.aucegypt.edu/context/etds/article/2890/viewcontent/ali_abdelkader_thesis.pdf Theses and Dissertations AUC Knowledge Fountain Point Semantic Segmentation Sensor Fusion Computer Vision Other Computer Engineering |
| spellingShingle | Point Semantic Segmentation Sensor Fusion Computer Vision Other Computer Engineering Abdelkader, Ali Camera and LiDAR Fusion For Point Cloud Semantic Segmentation |
| title | Camera and LiDAR Fusion For Point Cloud Semantic Segmentation |
| title_full | Camera and LiDAR Fusion For Point Cloud Semantic Segmentation |
| title_fullStr | Camera and LiDAR Fusion For Point Cloud Semantic Segmentation |
| title_full_unstemmed | Camera and LiDAR Fusion For Point Cloud Semantic Segmentation |
| title_short | Camera and LiDAR Fusion For Point Cloud Semantic Segmentation |
| title_sort | camera and lidar fusion for point cloud semantic segmentation |
| topic | Point Semantic Segmentation Sensor Fusion Computer Vision Other Computer Engineering |
| url | https://fount.aucegypt.edu/etds/1871 https://fount.aucegypt.edu/context/etds/article/2890/viewcontent/ali_abdelkader_thesis.pdf |
| work_keys_str_mv | AT abdelkaderali cameraandlidarfusionforpointcloudsemanticsegmentation |