LVP: Leverage Virtual Points in Multimodal Early Fusion for 3-D Object Detection

Due to the sparsity and occlusion of point clouds, pure point cloud detection has limited effectiveness in detecting such samples. Researchers have been actively exploring the fusion of multimodal data, attempting to address the bottleneck issue based on LiDAR. In particular, virtual points, generat...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 15
Hlavní autori:	Chen, Yidong, Cai, Guorong, Song, Ziying, Liu, Zhaoliang, Zeng, Binghui, Li, Jonathan, Wang, Zongyue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	3-D object detection Accuracy Algorithms autonomous driving Cloud computing early fusion Effectiveness Feature extraction Geoscience and remote sensing Laser radar Lidar Modules multimodal Object detection Object recognition Occlusion Point cloud compression Proposals Sensory integration Shape Three dimensional models Three-dimensional displays Valproic acid virtual points
ISSN:	0196-2892, 1558-0644
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