Urb3DCD : Urban Point Clouds Simulated Dataset for 3D Change Detection

[NEW] Urb3DCD V2 is now avalaible!In a context of rapid urban evolution, there is a need of surveying cities. Nowadays predictive models based on machine learning require large amount of data to be trained, hence the necessity of providing some public dataset allowing to follow up urban evolution. While most of changes occurs onto the vertical axis, there is no public change detection dataset composed of 3D point clouds and directly annotated according to the change at point level yet. With the proposed dataset, we aim to fill this gap since we believe that 3D point clouds bring some supplementary information on height that seems useful in the context of building change extraction, given that main modifications occur onto the vertical axis. Furthermore, spectral variability of a same object over time, difference of viewing angles between acquisition of 2D images, perspective and distortion effects could complicate change retrieval based on 2D data. Thus, this dataset is composed of bi-temporal pairs of point clouds annotated according to the change. Point clouds are acquired via a simulator of aerial LiDAR Survey over dense urban areas. Changes are also introduced by the simulator.Different sub-datasets are made available in order to provide 3D change data in different conditions of acquisition from low resolution noisy data to higher resolution and more precise aerial LiDAR alike data. For each sub-dataset, training, validation and testing sets are furnished.

【新发布】Urb3DCD V2版本现已推出！在快速城市演化的背景下，对城市进行调研的需求日益迫切。目前，基于机器学习的预测模型需要大量数据进行训练，因此提供允许追踪城市演化的公共数据集显得尤为必要。尽管大多数变化发生在垂直轴上，但尚无公开的基于3D点云和直接按点级变化进行标注的变化检测数据集。本数据集旨在填补这一空白，因为我们相信3D点云在提供补充高度信息方面具有潜在价值，尤其是在建筑变化提取的情境下，鉴于主要修改往往发生在垂直轴上。此外，同一物体随时间变化的谱变异性、2D图像采集时的视角差异以及透视和畸变效应可能会使得基于2D数据的变化检索变得复杂。因此，本数据集由根据变化进行标注的双时相点云对组成。点云是通过针对密集城市区域的航空激光雷达测量模拟器获取的。模拟器还引入了变化。为了提供在不同采集条件下（从低分辨率噪声数据到更高分辨率且更精确的航空激光雷达类似数据）的3D变化数据，我们提供了不同的子数据集。对于每个子数据集，都提供了训练集、验证集和测试集。

搜集汇总

背景与挑战

背景概述

Urb3DCD是一个用于3D变化检测的模拟城市点云数据集，包含多个子数据集，涵盖不同分辨率和噪声水平的点云数据，并提供了详细的变化标注和单时相语义标签。该数据集旨在填补公开3D点云变化检测数据集的空白，支持机器学习模型的训练和评估。