# Multi-View Pose-Agnostic Change Localization with Zero Labels

arXiv: https://arxiv.org/abs/2412.03911 · HTML: https://mlsanity.com/paper/2412.03911/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes](https://mlsanity.com/paper/2101.10994/) (2021)
- [INeRF: Inverting Neural Radiance Fields for Pose Estimation](https://mlsanity.com/paper/2012.05877/) (2020)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Learning to Describe Differences Between Pairs of Similar Images](https://mlsanity.com/paper/1808.10584/) (2018)
- [DSR -- A dual subspace re-projection network for surface anomaly detection](https://mlsanity.com/paper/2208.01521/) (2022)
- [Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2203.00259/) (2022)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection](https://mlsanity.com/paper/2310.07716/) (2023)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [Hierarchical Paired Channel Fusion Network for Street Scene Change Detection](https://mlsanity.com/paper/2010.09925/) (2020)
- [ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments](https://mlsanity.com/paper/2103.05368/) (2021)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection](https://mlsanity.com/paper/2404.06832/) (2024)
- [The Change You Want to See (Now in 3D)](https://mlsanity.com/paper/2308.10417/) (2023)
- [Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints](https://mlsanity.com/paper/1712.02941/) (2017)
- [Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms](https://mlsanity.com/paper/2409.16850/) (2024)

## Built on by (most cited first, 12 of 12)

- [Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis](https://mlsanity.com/paper/2603.22786/) (2026)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [3D Scene Change Modeling With Consistent Multi-View Aggregation](https://mlsanity.com/paper/2512.22830/) (2025)
- [From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.07203/) (2026)
- [Situat3DChange: Situated 3D Change Understanding Dataset for Multimodal Large Language Model](https://mlsanity.com/paper/2510.11509/) (2025)
- [Leveraging Geometric Priors for Unaligned Scene Change Detection](https://mlsanity.com/paper/2509.11292/) (2025)
- [LTGS: Long-Term Gaussian Scene Chronology From Sparse View Updates](https://mlsanity.com/paper/2510.09881/) (2025)
- [PlenoCI: Plenoptic CharacterIstics for View Dependence Aware Change Classification](https://mlsanity.com/paper/2609.28930/) (2026)
- [MATCH: Flow Matching for Multi-View Anomaly Detection](https://mlsanity.com/paper/2606.24375/) (2026)
- [C3-Bench: A Context-Aware Change Captioning Benchmark](https://mlsanity.com/paper/2606.25445/) (2026)
- [ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization](https://mlsanity.com/paper/2609.31339/) (2026)

## Abstract (arXiv)

Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussian Splatting (3DGS) representation of the scene. With as few as 5 images of the post-change scene, our approach can learn an additional change channel in a 3DGS and produce change masks that outperform single-view techniques. Our change-aware 3D scene representation additionally enables the generation of accurate change masks for unseen viewpoints. Experimental results demonstrate state-of-the-art performance in complex multi-object scenes, achieving a 1.7x and 1.5x improvement in Mean Intersection Over Union and F1 score respectively over other baselines. We also contribute a new real-world dataset to benchmark change detection in diverse challenging scenes in the presence of lighting variations.