{"ID":2878276,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20135","arxiv_id":"2508.20135","title":"Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads","abstract":"In this case study, we present a data-efficient point cloud segmentation pipeline and training framework for robust segmentation of unimproved roads and seven other classes. Our method employs a two-stage training framework: first, a projection-based convolutional neural network is pre-trained on a mixture of public urban datasets and a small, curated in-domain dataset; then, a lightweight prediction head is fine-tuned exclusively on in-domain data. Along the way, we explore the application of Point Prompt Training to batch normalization layers and the effects of Manifold Mixup as a regularizer within our pipeline. We also explore the effects of incorporating histogram-normalized ambients to further boost performance. Using only 50 labeled point clouds from our target domain, we show that our proposed training approach improves mean Intersection-over-Union from 33.5% to 51.8% and the overall accuracy from 85.5% to 90.8%, when compared to naive training on the in-domain data. Crucially, our results demonstrate that pre-training across multiple datasets is key to improving generalization and enabling robust segmentation under limited in-domain supervision. Overall, this study demonstrates a practical framework for robust 3D semantic segmentation in challenging, low-data scenarios. Our code is available at: https://github.com/andrewyarovoi/MD-FRNet.","short_abstract":"In this case study, we present a data-efficient point cloud segmentation pipeline and training framework for robust segmentation of unimproved roads and seven other classes. Our method employs a two-stage training framework: first, a projection-based convolutional neural network is pre-trained on a mixture of public ur...","url_abs":"https://arxiv.org/abs/2508.20135","url_pdf":"https://arxiv.org/pdf/2508.20135v1","authors":"[\"Andrew Yarovoi\",\"Christopher R. Valenta\"]","published":"2025-08-26T20:00:36Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":610470,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2878276,"paper_url":"https://arxiv.org/abs/2508.20135","paper_title":"Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads","repo_url":"https://github.com/andrewyarovoi/MD-FRNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}