September 25, 2025Open Access

Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads

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Proposed training approach increased mean intersection-over-union from 33.5% to 51.8%, enhancing accuracy.
Utilizing only 50 labeled point clouds from the target domain, the method outperformed naive training significantly.
Implementation of manifold mixup as a regularizer and point prompt training shows promising results.
The framework emphasizes the necessity of pre-training across multiple datasets for better performance.

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Case study reveals improved semantic segmentation in unimproved roads, suggesting robust methods with minimal data.

Yarovoi et al. (2025) studied this question.

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