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Hunan Province","award":["GZSYS-KY-2022-024"],"award-info":[{"award-number":["GZSYS-KY-2022-024"]}]},{"name":"National Natural Science Foundation of Hunan Province","award":["202208183000751"],"award-info":[{"award-number":["202208183000751"]}]},{"name":"National Natural Science Foundation of Hunan Province","award":["2023JJ30696"],"award-info":[{"award-number":["2023JJ30696"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurate 3D object detection is essential for autonomous driving, yet traditional LiDAR models often struggle with sparse point clouds. We propose perspective-aware hierarchical vision transformer-based LiDAR-camera fusion (PLC-Fusion) for 3D object detection to address this. This efficient, multi-modal 3D object detection framework integrates LiDAR and camera data for improved performance. First, our method enhances LiDAR data by projecting them onto a 2D plane, enabling the extraction of object perspective features from a probability map via the Object Perspective Sampling (OPS) module. It incorporates a lightweight perspective detector, consisting of interconnected 2D and monocular 3D sub-networks, to extract image features and generate object perspective proposals by predicting and refining top-scored 3D candidates. Second, it leverages two independent transformers\u2014CamViT for 2D image features and LidViT for 3D point cloud features. These ViT-based representations are fused via the Cross-Fusion module for hierarchical and deep representation learning, improving performance and computational efficiency. These mechanisms enhance the utilization of semantic features in a region of interest (ROI) to obtain more representative point features, leading to a more effective fusion of information from both LiDAR and camera sources. PLC-Fusion outperforms existing methods, achieving a mean average precision (mAP) of 83.52% and 90.37% for 3D and BEV detection, respectively. Moreover, PLC-Fusion maintains a competitive inference time of 0.18 s. Our model addresses computational bottlenecks by eliminating the need for dense BEV searches and global attention mechanisms while improving detection range and precision.<\/jats:p>","DOI":"10.3390\/info15110739","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T11:58:07Z","timestamp":1732017487000},"page":"739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["PLC-Fusion: Perspective-Based Hierarchical and Deep LiDAR Camera Fusion for 3D Object Detection in Autonomous Vehicles"],"prefix":"10.3390","volume":"15","author":[{"given":"Husnain","family":"Mushtaq","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2740-8025","authenticated-orcid":false,"given":"Xiaoheng","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}]},{"given":"Fizza","family":"Azhar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Chenab, Gujrat 50700, Pakistan"}]},{"given":"Mubashir","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK"}]},{"given":"Hafiz Husnain","family":"Raza Sherazi","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle Upon Tyne NE4 5TG, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1109\/TIV.2023.3331972","article-title":"Sparsefusion3d: Sparse sensor fusion for 3d object detection by radar and camera in environmental perception","volume":"9","author":"Yu","year":"2023","journal-title":"IEEE Trans. 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