3-D HANet: A Flexible 3-D Heatmap Auxiliary Network for Object Detection

3-D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3-D object detection. We propose a novel flexible 3-D heatmap auxiliary network (3-D HANet) for object det...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023, Vol.61, p.1-13
Main Authors: Xia, Qiming, Chen, Yidong, Cai, Guorong, Chen, Guikun, Xie, Daoshun, Su, Jinhe, Wang, Zongyue
Format: Article
Language:English
Subjects:
Boxes
Cloud computing
Computation
Computer networks
Detection
Feature extraction
Meteorological satellites
Object recognition
Three dimensional models
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Summary:3-D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3-D object detection. We propose a novel flexible 3-D heatmap auxiliary network (3-D HANet) for object detection. To obtain complete structure and location information from an incomplete point cloud structure, we propose a 3-D heatmap to reflect object information. Also, we design a plug-and-play auxiliary network based on 3-D heatmap, which improves the accuracy of the entire detection network without extra computation in the inference stage. We validate the 3-D HANet on the basis of three classic 3-D object detection networks: PointPillars, sparsely embedded convolutional detection (SECOND), and structure aware single-stage 3-D object detection from point cloud (SASSD). Experimental results show that our auxiliary network augments the feature extraction ability of the backbone network, which is manifested in that the predicted boxes and the ground-truth boxes are more suitable in size and more aligned in direction. Furthermore, we conducted verification experiments on the state-of-the-art (SOTA) detector, CasA, and made a further improvement on the official ranking of the KITTI dataset.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3250229