标题： 面向未改善道路的数据高效点云语义分割流程 显示英文标题

标题： Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads

摘要： 在本案例研究中，我们提出了一种数据高效的点云分割流程和训练框架，用于对未改善的道路和其他七个类别进行鲁棒分割。 我们的方法采用了一个两阶段的训练框架：首先，在公共城市数据集的混合数据和一个小的、精心挑选的领域内数据集上对基于投影的卷积神经网络进行预训练；然后，在领域内数据上仅对轻量级预测头进行微调。 在此过程中，我们探索了点提示训练在批量归一化层中的应用，以及流形混叠作为我们流程中的正则化效果。 我们还探讨了引入直方图归一化的环境以进一步提升性能的效果。 仅使用来自目标领域的50个标记点云，我们表明，与在领域内数据上进行简单训练相比，我们提出的训练方法将平均交并比从33.5%提高到51.8%，整体准确率从85.5%提高到90.8%。 至关重要的是，我们的结果表明，在多个数据集上进行预训练是提高泛化能力的关键，并且在有限的领域内监督下实现了鲁棒的分割。 总体而言，本研究展示了一个在具有挑战性、低数据场景下的鲁棒三维语义分割的实用框架。 我们的代码可在以下位置获得：https://github.com/andrewyarovoi/MD-FRNet. 显示英文摘要

摘要： 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.