Building 3D geometric maps of man-made spaces is a well-established and active field that is fundamental to computer vision and robotics. However, considering the evolving nature of built environments, it is essential to question the capabilities of current mapping efforts in handling temporal changes. In addition, spatiotemporal mapping holds significant potential for achieving sustainability and circularity goals. Existing mapping approaches focus on small changes, such as object relocation or self-driving car operation; in all cases where the main structure of the scene remains fixed. Consequently, these approaches fail to address more radical changes in the structure of the built environment, such as geometry and topology. To this end, we introduce the Nothing Stands Still (NSS) benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map. Specifically, the benchmark involves registering two or more partial 3D point clouds (fragments) from the same scene but captured from different spatiotemporal views. In addition to the standard pairwise registration, we assess the multi-way registration of multiple fragments that belong to any temporal stage. As part of NSS, we introduce a dataset of 3D point clouds recurrently captured in large-scale building indoor environments that are under construction or renovation. The NSS benchmark presents three scenarios of increasing difficulty, to quantify the generalization ability of point cloud registration methods over space (within one building and across buildings) and time. We conduct extensive evaluations of state-of-the-art methods on NSS. The results demonstrate the necessity for novel methods specifically designed to handle large spatiotemporal changes. The homepage of our benchmark is at http://nothing-stands-still.com.

翻译：构建人造空间的3D几何地图是计算机视觉与机器人领域中一项成熟且活跃的基础性工作。然而，考虑到建筑环境的动态演变特性，质疑当前建图方法处理时间变化的能力至关重要。此外，时空建图在实现可持续性和循环经济目标方面具有巨大潜力。现有建图方法聚焦于小幅变化，如物体重新摆放或自动驾驶汽车运行，这些场景均假设场景主体结构保持不变。因此，这些方法无法应对建筑环境中更为剧烈的结构变化（如几何与拓扑改变）。为此，我们提出"万物非静止"（NSS）基准测试，专注于经历大尺度时空变化的3D场景配准，最终生成连贯的时空地图。具体而言，该基准测试涉及对同一场景在不同时空视角下采集的两个或多个局部3D点云（碎片）进行配准。除标准两两配准外，我们还评估属于任意时间阶段的多个碎片的联合配准。作为NSS基准的一部分，我们提出一个在大型建筑室内环境中重复采集的3D点云数据集，这些建筑正处于施工或翻新阶段。NSS基准测试包含三种难度递增的评估场景，用于量化点云配准方法在空间维度（同一建筑内部及跨建筑）与时间维度上的泛化能力。我们对NSS上的现有最优方法进行了广泛评估。结果表明，亟需设计专门处理大时空变化的新方法。本基准测试主页：http://nothing-stands-still.com。