ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection

Author:

Yin Junbo,Zhou Dingfu,Zhang Liangjun,Fang Jin,Xu Cheng-Zhong,Shen Jianbing,Wang Wenguan

Publisher

Springer Nature Switzerland

Reference49 articles.

1. Bogoslavskyi, I., Stachniss, C.: Efficient online segmentation for sparse 3D laser scans. J. Photogrammetry Remote Sens. Geoinf. Sci. 85(1), 41–52 (2017)

2. Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: NeurIPS (2020)

3. Chang, A.X., et al.: ShapeNet: an information-rich 3d model repository. arXiv preprint (2015)

4. Chen, K., Hong, L., Xu, H., Li, Z., Yeung, D.Y.: MultiSiam: self-supervised multi-instance Siamese representation learning for autonomous driving. In: ICCV (2021)

5. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)

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