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

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

LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which however suffer from the domain shift problem as other de...

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Bibliographic Details
Main Authors: Adriano Cardace, Andrea Conti, Pierluigi Zama Ramirez, Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Depth completion
lidar segmentation
multi-modal
unsupervised domain adaptation
Online Access:https://ieeexplore.ieee.org/document/10214589/

Internet

https://ieeexplore.ieee.org/document/10214589/

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