Abstract
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.
Abstract (translated)
随着3D传感技术的快速发展，获取物体三维形状信息变得越来越方便。激光雷达技术通过其准确捕捉远处物体三维信息的能力，在城市场景中广泛应用于3D数据的收集。然而，收集的点云数据经常由于遮挡、信号吸收和镜面反射等因素而表现不完整。本文探讨了点云完成技术在处理这些不完整数据中的应用，并建立了一个新的真实世界基准建筑-PCC数据集，以评估现有深度学习方法在城市建筑点云完成任务中的性能。通过全面评估不同方法，我们分析了几种方法在构建点云完成中所面临的关键挑战，旨在推动3D地理信息应用领域创新。我们的源代码可在此处下载：https://www.example.com/。
URL
https://arxiv.org/abs/2404.15644
