LMSeg: An end-to-end geometric message-passing network on barycentric dual graphs for large-scale landscape mesh segmentation

• URL: http://arxiv.org/abs/2407.04326v3
• Date: Sat, 08 Nov 2025 19:42:06 GMT
• Title: LMSeg: An end-to-end geometric message-passing network on barycentric dual graphs for large-scale landscape mesh segmentation
• Authors: Zexian Huang, Kourosh Khoshelham, Martin Tomko,
• Abstract summary: We introduce the BudjBim Wall dataset, a large-scale annotated mesh dataset from the UNESCO World Heritage-listed Budj Bim cultural landscape in Victoria, Australia.<n>We propose LMSeg, a deep graph message-passing network for semantic segmentation of large-scale meshes.<n>Experiments on three large-scale benchmarks (SUM, H3D, and BBW) show that LMSeg achieves 75.1% mIoU on SUM, 78.4% O.A. on H3D, and 62.4% mIoU on BBW, using only 2.4M lightweight parameters.
• Score: 5.698076346359676
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic segmentation of large-scale 3D landscape meshes is critical for geospatial analysis in complex environments, yet existing approaches face persistent challenges of scalability, end-to-end trainability, and accurate segmentation of small and irregular objects. To address these issues, we introduce the BudjBim Wall (BBW) dataset, a large-scale annotated mesh dataset derived from high-resolution LiDAR scans of the UNESCO World Heritage-listed Budj Bim cultural landscape in Victoria, Australia. The BBW dataset captures historic dry-stone wall structures that are difficult to detect under vegetation occlusion, supporting research in underrepresented cultural heritage contexts. Building on this dataset, we propose LMSeg, a deep graph message-passing network for semantic segmentation of large-scale meshes. LMSeg employs a barycentric dual graph representation of mesh faces and introduces the Geometry Aggregation+ (GA+) module, a learnable softmax-based operator that adaptively combines neighborhood features and captures high-frequency geometric variations. A hierarchical-local dual pooling integrates hierarchical and local geometric aggregation to balance global context with fine-detail preservation. Experiments on three large-scale benchmarks (SUM, H3D, and BBW) show that LMSeg achieves 75.1% mIoU on SUM, 78.4% O.A. on H3D, and 62.4% mIoU on BBW, using only 2.4M lightweight parameters. In particular, LMSeg demonstrates accurate segmentation across both urban and natural scenes-capturing small-object classes such as vehicles and high vegetation in complex city environments, while also reliably detecting dry-stone walls in dense, occluded rural landscapes. Together, the BBW dataset and LMSeg provide a practical and extensible method for advancing 3D mesh segmentation in cultural heritage, environmental monitoring, and urban applications.

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