Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads

• URL: http://arxiv.org/abs/2508.20135v1
• Date: Tue, 26 Aug 2025 20:00:36 GMT
• Title: Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
• Authors: Andrew Yarovoi, Christopher R. Valenta,
• Abstract summary: We present a data-efficient point cloud segmentation pipeline and training framework for robust segmentation of unimproved roads.<n>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.<n>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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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