  The benefits of having digital twins of urban buildings are numerous.
However, a major difficulty encountered in their creation from airborne LiDAR
point clouds is the effective means of accurately reconstructing significant
occlusions amidst point density variations and noise. To bridge the
noise/sparsity/occlusion gap and generate high fidelity 3D building models, we
propose APC2Mesh which integrates point completion into a 3D reconstruction
pipeline, enabling the learning of dense geometrically accurate representation
of buildings. Specifically, we leveraged complete points generated from
occluded ones as input to a linearized skip attention-based deformation network
for 3D mesh reconstruction. In our experiments, conducted on 3 different
scenes, we demonstrate that: (1) APC2Mesh delivers comparatively superior
results, indicating its efficacy in handling the challenges of occluded
airborne building points of diverse styles and complexities. (2) The
combination of point completion with typical deep learning-based 3D point cloud
reconstruction methods offers a direct and effective solution for
reconstructing significantly occluded airborne building points. As such, this
neural integration holds promise for advancing the creation of digital twins
for urban buildings with greater accuracy and fidelity.
