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Hwijeong Lee

2 accepted papers

2025

No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather

CVPR 2025poster

Existing domain generalization methods for LiDAR semantic segmentation under adverse weather struggle to accurately predict "things" categories compared to "stuff" categories. In typical driving scenes, "things" categories can be dynamic and associated with higher collision risks, making them crucia…

Cited by 0SourcePDFScholar
2024

Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation

IROS 2024

We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops due to the significant imbalance between ground-truth and

Cited by 1SourcecodeScholar