Paper Info
Title
Label-Guided Auxiliary Training Improves 3D Object Detector.
Abstract
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently. In this paper, we propose a Label-Guided auxiliary training method for 3D object detection (LG3D), which serves as an auxiliary network to enhance the feature learning of existing 3D object detectors. Specifically, we propose two novel modules: a Label-Annotation-Inducer that maps annotations and point clouds in bounding boxes to task-specific representations and a Label-Knowledge-Mapper that assists the original features to obtain detection-critical representations. The proposed auxiliary network is discarded in inference and thus has no extra computational cost at test time. We conduct extensive experiments on both indoor and outdoor datasets to verify the effectiveness of our approach. For example, our proposed LG3D improves VoteNet by 2.5% and 3.1% mAP on the SUN RGB-D and ScanNetV2 datasets, respectively. The code is available at https://github.com/FabienCode/LG3D.
Year
DOI
Venue
2022
10.1007/978-3-031-20077-9_40
European Conference on Computer Vision
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Yaomin Huang100.34
Xinmei Liu200.34
Yichen Zhu300.34
Zhiyuan Xu400.68
Chaomin Shen516112.57
Zhengping Che600.34
Guixu Zhang712825.80
Yaxin Peng87316.82
Feifei Feng900.68
Jian Tang10109574.34