Boosting Multi-Modal Unsupervised Domain Adaptation for LiDAR Semantic Segmentation by Self-Supervised Depth Completion-Reference-Cited by-同舟云学术

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Boosting Multi-Modal Unsupervised Domain Adaptation for LiDAR Semantic Segmentation by Self-Supervised Depth Completion

Published:2023 Issue: Volume:11 Page:85155-85164
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Cardace Adriano¹^ORCID,Conti Andrea¹^ORCID,Ramirez Pierluigi Zama¹^ORCID,Spezialetti Riccardo¹^ORCID,Salti Samuele¹^ORCID,Stefano Luigi Di¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10214589.pdf?arnumber=10214589

Reference46 articles.

1. EfficientLPS: Efficient LiDAR Panoptic Segmentation

2. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

3. nuScenes: A Multimodal Dataset for Autonomous Driving

4. Unsupervised Depth Completion From Visual Inertial Odometry

5. RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

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