Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation-Reference-Cited by-同舟云学术

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Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Cao Haozhi¹,Xu Yuecong²,Yang Jianfei¹,Yin Pengyu¹,Yuan Shenghai¹,Xie Lihua¹

Affiliation:

1. Nanyang Technological University

2. A*STAR,Institute for Infocomm Research,Singapore

Funder

National Research Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377199.pdf?arnumber=10377199

Reference52 articles.

1. A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

2. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

3. Parameter-free Online Test-time Adaptation

4. Real-time multi-modal semantic fusion on unmanned aerial vehicles with label propagation for cross-domain adaptation

5. nuScenes: A Multimodal Dataset for Autonomous Driving

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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