# Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change

arXiv: https://arxiv.org/abs/2311.09346 · HTML: https://mlsanity.com/paper/2311.09346/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2023.

## Built on by (most cited first, 16 of 16)

- [The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods](https://mlsanity.com/paper/2411.10546/) (2024)
- [Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments](https://mlsanity.com/paper/2312.09138/) (2023)
- [Multiway Point Cloud Mosaicking with Diffusion and Global Optimization](https://mlsanity.com/paper/2404.00429/) (2024)
- [CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization](https://mlsanity.com/paper/2506.21117/) (2025)
- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos](https://mlsanity.com/paper/2604.05621/) (2026)
- [Has Anything Changed? 3D Change Detection by 2D Segmentation Masks](https://mlsanity.com/paper/2312.01148/) (2023)
- [Agent-to-Sim: Learning Interactive Behavior Models from Casual Longitudinal Videos](https://mlsanity.com/paper/2410.16259/) (2024)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [ReScene4D: Temporally Consistent Semantic Instance Segmentation of Evolving Indoor 3D Scenes](https://mlsanity.com/paper/2601.11508/) (2026)
- [CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering](https://mlsanity.com/paper/2501.06927/) (2025)
- [Dynamic Objects Relocalization in Changing Environments with Flow Matching](https://mlsanity.com/paper/2509.16398/) (2025)
- [BIM-Constrained Optimization for Accurate Localization and Deviation Correction in Construction Monitoring](https://mlsanity.com/paper/2504.17693/) (2025)
- [The City that Never Settles: Simulation-based LiDAR Dataset for Long-Term Place Recognition Under Extreme Structural Changes](https://mlsanity.com/paper/2505.05076/) (2025)
- [Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark](https://mlsanity.com/paper/2608.07757/) (2026)
- [DeltaSeek: Toward Active Perception in Evolving Construction Environments](https://mlsanity.com/paper/2609.33836/) (2026)

## Abstract (arXiv)

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.