SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation-Reference-Cited by-同舟云学术

SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Xu Qiangeng¹,Zhou Yin²,Wang Weiyue²,Qi Charles R.²,Anguelov Dragomir²

Affiliation:

1. University of Southern California

2. Waymo, LLC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711355.pdf?arnumber=9711355

Reference70 articles.

1. Unpaired image-to-image translation using cycle-consistent adversarial networks;zhu;Proceedings of the IEEE International Conference on Computer Vision,2017

2. Pointnet: Deep learning on point sets for 3d classification and segmentation;qi;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017

3. Frustum PointNets for 3D Object Detection from RGB-D Data

4. LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving

5. Unsupervised scene adaptation for semantic segmentation of urban mobile laser scanning point clouds

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