# City-scale Scene Change Detection using Point Clouds

arXiv: https://arxiv.org/abs/2103.14314 · HTML: https://mlsanity.com/paper/2103.14314/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [RPM-Net: Robust Point Matching using Learned Features](https://mlsanity.com/paper/2003.13479/) (2020)
- [PPFNet: Global Context Aware Local Features for Robust 3D Point Matching](https://mlsanity.com/paper/1802.02669/) (2018)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration](https://mlsanity.com/paper/1807.09413/) (2018)
- [Learning Compact Geometric Features](https://mlsanity.com/paper/1709.05056/) (2017)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System](https://mlsanity.com/paper/1809.05477/) (2018)
- [Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection](https://mlsanity.com/paper/1810.09111/) (2018)
- [DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds](https://mlsanity.com/paper/1811.11397/) (2018)
- [Visual Localization Using Sparse Semantic 3D Map](https://mlsanity.com/paper/1904.03803/) (2019)

## Built on by (most cited first, 7 of 7)

- [Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms](https://mlsanity.com/paper/2409.16850/) (2024)
- [A Guide to Structureless Visual Localization](https://mlsanity.com/paper/2504.17636/) (2025)
- [From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.07203/) (2026)
- [Automatic Map Update Using Dashcam Videos](https://mlsanity.com/paper/2109.12131/) (2021)
- [SceneEdited: A City-Scale Benchmark for 3D HD Map Updating via Image-Guided Change Detection](https://mlsanity.com/paper/2511.15153/) (2025)
- [Crowd Source Scene Change Detection and Local Map Update](https://mlsanity.com/paper/2203.05205/) (2022)
- [TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection](https://mlsanity.com/paper/2605.20822/) (2026)

## Abstract (arXiv)

We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times. We first generate 3D point clouds for each traversal from the images and approximate GNSS/INS readings using Structure-from-Motion (SfM). A direct comparison of the two point clouds for change detection is not ideal due to inaccurate geo-location information and possible drifts in the SfM. To circumvent this problem, we propose a deep learning-based non-rigid registration on the point clouds which allows us to compare the point clouds for structural change detection in the scene. Furthermore, we introduce a dual thresholding check and post-processing step to enhance the robustness of our method. We collect two datasets for the evaluation of our approach. Experiments show that our method is able to detect scene changes effectively, even in the presence of viewpoint and illumination differences.