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.

通过点云完整性补充方法整合点云数据和 3D 重建技术，提出 APC2Mesh 算法，用于建筑物的精确重构，实验证明 APC2Mesh 能够有效应对多种风格和复杂性的被遮挡建筑物点云数据的难题，为提高城市建筑数字模型的准确度和真实性提供了新途径。

APC2Mesh: 从遮挡的建筑外立面到完整的 3D 模型的连接

APC2Mesh: Bridging the gap from occluded building façades to full 3D  models

Point completion refers to completing the missing geometries of an object
from incomplete observations. Main-stream methods predict the missing shapes by
decoding a global feature learned from the input point cloud, which often leads
to deficient results in preserving topology consistency and surface details. In
this work, we present ME-PCN, a point completion network that leverages
`emptiness' in 3D shape space. Given a single depth scan, previous methods
often encode the occupied partial shapes while ignoring the empty regions (e.g.
holes) in depth maps. In contrast, we argue that these `emptiness' clues
indicate shape boundaries that can be used to improve topology representation
and detail granularity on surfaces. Specifically, our ME-PCN encodes both the
occupied point cloud and the neighboring `empty points'. It estimates
coarse-grained but complete and reasonable surface points in the first stage,
followed by a refinement stage to produce fine-grained surface details.
Comprehensive experiments verify that our ME-PCN presents better qualitative
and quantitative performance against the state-of-the-art. Besides, we further
prove that our `emptiness' design is lightweight and easy to embed in existing
methods, which shows consistent effectiveness in improving the CD and EMD
scores.

本文介绍了一种基于三维形状空间 “空洞” 特征的点完整性网络 ME-PCN，该网络能够在保留拓扑一致性和表面细节的前提下预测出完整的表面点云，实验表明 ME-PCN 优于现有技术并且采用了一种轻量级的 “空洞” 设计。

ME-PCN：基于掩码空洞的点云补全

ME-PCN: Point Completion Conditioned on Mask Emptiness

Point completion refers to complete the missing geometries of objects from
partial point clouds. Existing works usually estimate the missing shape by
decoding a latent feature encoded from the input points. However, real-world
objects are usually with diverse topologies and surface details, which a latent
feature may fail to represent to recover a clean and complete surface. To this
end, we propose a skeleton-bridged point completion network (SK-PCN) for shape
completion. Given a partial scan, our method first predicts its 3D skeleton to
obtain the global structure, and completes the surface by learning
displacements from skeletal points. We decouple the shape completion into
structure estimation and surface reconstruction, which eases the learning
difficulty and benefits our method to obtain on-surface details. Besides,
considering the missing features during encoding input points, SK-PCN adopts a
local adjustment strategy that merges the input point cloud to our predictions
for surface refinement. Comparing with previous methods, our skeleton-bridged
manner better supports point normal estimation to obtain the full surface mesh
beyond point clouds. The qualitative and quantitative experiments on both point
cloud and mesh completion show that our approach outperforms the existing
methods on various object categories.

通过预测三维骨架以及从骨架点学习偏移量，提出了一种骨架桥接点完形网络（SK-PCN），将形状完形分解为结构估计和表面重建，并采用本地调整策略合并输入点云以进行表面细化。实验表明，相比于现有方法，该方法在点云和网格完成的各个对象类别上表现更好。