Label-Guided Auxiliary Training Improves 3D Object Detector
- URL: http://arxiv.org/abs/2207.11753v1
- Date: Sun, 24 Jul 2022 14:22:21 GMT
- Title: Label-Guided Auxiliary Training Improves 3D Object Detector
- Authors: Yaomin Huang, Xinmei Liu, Yichen Zhu, Zhiyuan Xu, Chaomin Shen,
Zhengping Che, Guixu Zhang, Yaxin Peng, Feifei Feng, Jian Tang
- Abstract summary: We propose a Label-Guided auxiliary training method for 3D object detection (LG3D)
Our proposed LG3D improves VoteNet by 2.5% and 3.1% mAP on the SUN RGB-D and ScanNetV2 datasets.
- Score: 32.96310946612949
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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