With the rapid advancement of 3D sensing technologies, obtaining 3D shape
information of objects has become increasingly convenient. Lidar technology,
with its capability to accurately capture the 3D information of objects at long
distances, has been widely applied in the collection of 3D data in urban
scenes. However, the collected point cloud data often exhibit incompleteness
due to factors such as occlusion, signal absorption, and specular reflection.
This paper explores the application of point cloud completion technologies in
processing these incomplete data and establishes a new real-world benchmark
Building-PCC dataset, to evaluate the performance of existing deep learning
methods in the task of urban building point cloud completion. Through a
comprehensive evaluation of different methods, we analyze the key challenges
faced in building point cloud completion, aiming to promote innovation in the
field of 3D geoinformation applications. Our source code is available at
this https URL

通过点云补全技术完成对不完整数据的处理，建立新的真实世界基准 Building-PCC 数据集来评估现有深度学习方法在城市建筑点云补全任务中的性能，并通过对不同方法的综合评估，分析在建筑点云补全中面临的关键挑战，旨在促进 3D 地理信息应用领域的创新。