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Ryusuke Miyamoto

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Conference Jul 2026

Detecting Hard Examples for Semantic Segmentation with Auxiliary 3D LiDAR

In this study, we propose a method for automatically detecting hard examples from unlabeled real-world data that lead to performance degradation in semantic segmentation models. The proposed method estimates obstacle regions using geometric information from a 3D LiDAR and generates obstacle masks by projecting them ont...

Yuriko Ueda, Miho Adachi, Ryusuke Miyamoto · 0 citations

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