Semi-Supervised Domain Adaptation on LiDAR 3D Object Detection with Self-Training and Knowledge Distillation-Reference-Cited by-同舟云学术

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Semi-Supervised Domain Adaptation on LiDAR 3D Object Detection with Self-Training and Knowledge Distillation

Published:2023-09-01 Issue:3 Volume:18 Page:346-351
ISSN:1975-6291
Container-title:Journal of Korea Robotics Society
language:en
Short-container-title:J. Korea Robot. Soc.

Author:

Woo Jungwan,Kim Jaeyeul,Im Sunghoon

Funder

Ministry of Science and ICT

National Research Foundation of Korea

Publisher

The Korea Robotics Society

Subject

Process Chemistry and Technology,Economic Geology,Fuel Technology

Reference15 articles.

1. Scalability in Perception for Autonomous Driving: Waymo Open Dataset

2. Vision meets robotics: The KITTI dataset

3. nuScenes: A Multimodal Dataset for Autonomous Driving

4. J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, L. Chen, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska, “One Thousand and One Hours: Self-driving Motion Prediction Dataset,” Conference on Robot Learning (CoRL), Cambridge, USA, pp. 409-418, 2020, [Online], https://proceedings.mlr.press/v155/houston21a.html

5. Argoverse: 3D Tracking and Forecasting With Rich Maps

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