Multi-View Fusion for 3D Object Detection in Autonomous Vehicles
| Autor: | Chung-Yen Tsai, 蔡忠諺 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 In this thesis, we propose a novel architecture, which amasses RGB-D images, and the front view (FV) with fast depth completion and the bird's eye view (BEV) both from the light detection and ranging (LIDAR) point cloud for 3D object detection in autonomous vehicles. In contrast to previous works, a fast depth completion algorithm is invoked to infer a dense depth map from the sparse depth input. A position-sensitive multi-model (PSM) fusion scheme is also addressed to learn to retain the translation-variant information in the fusion process. Moreover, a 3D intersection of union (IoU) loss function is employed for joint optimization of box parameters to enhance the detection accuracy of hard samples and decrease the false positive. Simulation results show that the new approach can provide superior performance over the state-of-the-art works on the widespread KITTI dataset. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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