Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
By Xiuwei Xu, Yifan Wang, Yu Zheng, Yongming Rao, Jiwen Lu, Jie Zhou. 2022
34 citations recorded by the source corpus.
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers). In order to remedy the information loss from box annotations to centers, our method, namely Back to Reality (BR), makes use of synthetic 3D shapes to convert the weak labels into fully-annotated virtual scenes as stronger supervision, and in turn utilizes the perfect virtual labels to complement and refine the real labels. Specifically, we first assemble 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we go back to reality by applying a virtual-to-real domain adaptation method, which refine the weak labels and additionally supervise the training of detector with the virtual scenes. Furthermore, we propose a more challenging benckmark for indoor 3D object detection with more diversity in object sizes for better evaluation. With less than 5% of the labeling labor, we achieve comparable detection performance with some popular fully-supervised approaches on the widely used ScanNet dataset. Code is available at: https://github.com/wyf-ACCEPT/BackToReality.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Continual Sequence Generation with Adaptive Compositional Modules (2022, background): “…follows a line of work that doesn’t store real examples for experience replay, such as generating examples by GAN (Atkinson et al., 2018), synthesizing examples (Xu et al., 2022) by model-inversion (Smith et al., 2021b), and using unlabeled data in the learning environment (Smith et al., 2021a).”