  Thanks to the application of deep learning technology in point cloud
processing of the remote sensing field, point cloud segmentation has become a
research hotspot in recent years, which can be applied to real-world 3D, smart
cities, and other fields. Although existing solutions have made unprecedented
progress, they ignore the inherent characteristics of point clouds in remote
sensing fields that are strictly arranged according to latitude, longitude, and
altitude, which brings great convenience to the segmentation of point clouds in
remote sensing fields. To consider this property cleverly, we propose novel
convolution operators, termed Twin Deformable point Convolutions (TDConvs),
which aim to achieve adaptive feature learning by learning deformable sampling
points in the latitude-longitude plane and altitude direction, respectively.
First, to model the characteristics of the latitude-longitude plane, we propose
a Cylinder-wise Deformable point Convolution (CyDConv) operator, which
generates a two-dimensional cylinder map by constructing a cylinder-like grid
in the latitude-longitude direction. Furthermore, to better integrate the
features of the latitude-longitude plane and the spatial geometric features, we
perform a multi-scale fusion of the extracted latitude-longitude features and
spatial geometric features, and realize it through the aggregation of adjacent
point features of different scales. In addition, a Sphere-wise Deformable point
Convolution (SpDConv) operator is introduced to adaptively offset the sampling
points in three-dimensional space by constructing a sphere grid structure,
aiming at modeling the characteristics in the altitude direction. Experiments
on existing popular benchmarks conclude that our TDConvs achieve the best
segmentation performance, surpassing the existing state-of-the-art methods.
