  Semantic segmentation of large-scale 3D landscape meshes is pivotal for
various geospatial applications, including spatial analysis, automatic mapping
and localization of target objects, and urban planning and development. This
requires an efficient and accurate 3D perception system to understand and
analyze real-world environments. However, traditional mesh segmentation methods
face challenges in accurately segmenting small objects and maintaining
computational efficiency due to the complexity and large size of 3D landscape
mesh datasets. This paper presents an end-to-end deep graph message-passing
network, LMSeg, designed to efficiently and accurately perform semantic
segmentation on large-scale 3D landscape meshes. The proposed approach takes
the barycentric dual graph of meshes as inputs and applies deep message-passing
neural networks to hierarchically capture the geometric and spatial features
from the barycentric graph structures and learn intricate semantic information
from textured meshes. The hierarchical and local pooling of the barycentric
graph, along with the effective geometry aggregation modules of LMSeg, enable
fast inference and accurate segmentation of small-sized and irregular mesh
objects in various complex landscapes. Extensive experiments on two benchmark
datasets (natural and urban landscapes) demonstrate that LMSeg significantly
outperforms existing learning-based segmentation methods in terms of object
segmentation accuracy and computational efficiency. Furthermore, our method
exhibits strong generalization capabilities across diverse landscapes and
demonstrates robust resilience against varying mesh densities and landscape
topologies.
