Detecting changes that occurred in a pair of 3D airborne LiDAR point clouds, acquired at two different times over the same geographical area, is a challenging task because of unmatching spatial supports and acquisition system noise. Most recent attempts to detect changes on point clouds are based on supervised methods, which require large labelled data unavailable in real-world applications. To address these issues, we propose an unsupervised approach that comprises two components: Neural Field (NF) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes. NF offer a grid-agnostic representation to encode bi-temporal point clouds with unmatched spatial support that can be regularised to increase high-frequency details and reduce noise. The reconstructions at each timestamp are compared at arbitrary spatial scales, leading to a significant increase in detection capabilities. We apply our method to a benchmark dataset of simulated LiDAR point clouds for urban sprawling. The dataset offers different challenging scenarios with different resolutions, input modalities and noise levels, allowing a multi-scenario comparison of our method with the current state-of-the-art. We boast the previous methods on this dataset by a 10% margin in intersection over union metric. In addition, we apply our methods to a real-world scenario to identify illegal excavation (looting) of archaeological sites and confirm that they match findings from field experts.

翻译：同一地理区域在两个不同时间获取的机载LiDAR点云对中的变化检测是一项具有挑战性的任务，原因是空间支撑不匹配与采集系统噪声。最近基于点云的变化检测尝试大多采用监督方法，但这些方法需要大规模标注数据，而实际应用中此类数据往往难以获取。为解决这些问题，我们提出一种无监督方法，包含两个组件：用于连续形状重建的神经场(Neural Field, NF)和用于变化分类的高斯混合模型。NF提供了一种与网格无关的表示方法，可编码空间支撑不匹配的双时点云，并通过正则化增强高频细节并降低噪声。每个时间戳的重建结果可在任意空间尺度下进行比较，显著提升检测能力。我们将该方法应用于城市扩张领域的模拟LiDAR点云基准数据集。该数据集包含不同分辨率、输入模态和噪声水平的多种挑战场景，可实现多场景下我们的方法与现有最先进技术的比较。在该数据集上，我们的方法以10%的交并比优势超越了此前方法。此外，我们将方法应用于识别考古遗址非法挖掘（盗掘）的真实场景，并证实其结果与实地专家发现高度吻合。