Object Detection Framework Based on Sensor Fusion Using Unsupervised Depth Completion Network
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- LUO Minjie
- 東京大学大学院 工学系研究科
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- YANG Bo
- 東京大学生産技術研究所
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- NAKANO Kimihiko
- 東京大学生産技術研究所
Bibliographic Information
- Other Title
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- 教師なし深度補完ネットワークを使用したセンサーフュージョンに基づく物体検出フレームワーク
Abstract
<p>In this paper, a novel perception framework is presented for 2D and 3D object detection, based on sensor fusion of cameras and Li-DAR. While camera images provide abundant environmental features, they lack depth information. Conversely, Li-DAR point clouds offer accurate depth information, which however, are sparse in nature. Recognizing the complementary nature of each sensor’s strengths and weaknesses, an unsupervised depth completion network to enrich information from both sensors is used. This enhanced data is then utilized for performing 2D and 3D object detection tasks using a state-of-the-art detection network. The proposed framework is validated on KITTI data set, and experimental results demonstrate notable improvements in both 2D and 3D tasks when compared to baseline results.</p>
Journal
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- SEISAN KENKYU
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SEISAN KENKYU 76 (1), 75-80, 2024-02-01
Institute of Industrial Science The University of Tokyo
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Details 詳細情報について
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- CRID
- 1390580704332256768
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- ISSN
- 18812058
- 0037105X
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed