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|a 10.1109/TVCG.2023.3331779
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|a eng
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|a Gu, Lipeng
|e verfasserin
|4 aut
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|a PointSee
|b Image Enhances Point Cloud
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|a Date Revised 01.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a There is a prevailing trend towards fusing multi-modal information for 3D object detection (3OD). However, challenges related to computational efficiency, plug-and-play capabilities, and accurate feature alignment have not been adequately addressed in the design of multi-modal fusion networks. In this paper, we present PointSee, a lightweight, flexible, and effective multi-modal fusion solution to facilitate various 3OD networks by semantic feature enhancement of point clouds (e.g., LiDAR or RGB-D data) assembled with scene images. Beyond the existing wisdom of 3OD, PointSee consists of a hidden module (HM) and a seen module (SM): HM decorates point clouds using 2D image information in an offline fusion manner, leading to minimal or even no adaptations of existing 3OD networks; SM further enriches the point clouds by acquiring point-wise representative semantic features, leading to enhanced performance of existing 3OD networks. Besides the new architecture of PointSee, we propose a simple yet efficient training strategy, to ease the potential inaccurate regressions of 2D object detection networks. Extensive experiments on the popular outdoor/indoor benchmarks show quantitative and qualitative improvements of our PointSee over thirty-five state-of-the-art methods
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|a Yan, Xuefeng
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| 700 |
1 |
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|a Cui, Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gong, Lina
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xie, Haoran
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Fu Lee
|e verfasserin
|4 aut
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| 700 |
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|a Qin, Jing
|e verfasserin
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| 700 |
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|a Wei, Mingqiang
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|t IEEE transactions on visualization and computer graphics
|d 1996
|g 30(2024), 9 vom: 07. Aug., Seite 6291-6308
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|g volume:30
|g year:2024
|g number:9
|g day:07
|g month:08
|g pages:6291-6308
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|u http://dx.doi.org/10.1109/TVCG.2023.3331779
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