Long-term autonomy in mobile robotics requires maps that remain accurate as environments change over time. Visual Place Recognition (VPR), a core localization capability, degrades sharply as the temporal gap between query and database images grows, particularly across seasonal transitions. Scene Change Detection (SCD) offers a principled mechanism for database maintenance, but existing methods rely on binary, uni-modal visual features that cannot distinguish structural changes from viewpoint-induced differences - a distinction essential for correct update decisions. We propose SCD4VPR, a scene change detection that jointly reasons about what has changed and distinguishes genuine change from viewpoint-induced difference in a unified vision-language framework. SCD4VPR fuses VLM-generated semantic descriptions with visual features via cross-modal attention and refines predictions with geometric-semantic matching, producing multi-class change masks that separately identify object changes, appearance changes, and viewpoint-induced changes. We introduce NYC-CD, the first real-world street-view SCD benchmark with pixel-level multi-class annotations across 8,122 image pairs. Experiments across four SCD benchmarks show that SCD4VPR consistently improves three architecturally distinct backbones. In a controlled VPR database maintenance experiment on NYU-VPR spanning summer through late winter, we confirm that retrieval performance deteriorates substantially when the database is left unchanged, and show that SCD4VPR-guided updates recover most of this loss (+30.1 R@1 at the largest time gap) while keeping the database far more compact than naive append.
Figures & tables
Dataset
Real/Sim
Pairs
Obj.
App.
VP.
CARLA-OBJCD [ 28 ]
Sim
15000
✓
✗
✓
Changesim [ 26 ]
Sim
∼ 130k
✓
✓
✗
Standardsim [ 27 ]
Sim
12718
✓
✗
✗
CD2014 [ 29 ]
Real
7000
✓
✗
✗
PCD [ 23 ]
Real
200
✓
✗
✗
VL-CMU-CD [ 8 ]
Real
1362
✓
✗
✗
TABLE I : Comparison of major SCD datasets with our NYC-CD dataset. Obj. : object change, App. : appearance change, VP. : viewpoint difference.
Fig. 2 : Four image pairs annotated by our pipeline. Each pair shows T0 (top) and T1 (bottom) with multi-class change masks: new objects , appearance changes , and viewpoint-induced changes .
Fig. 3 : Overview of the proposed SCD4VPR. Two images ( T0 , T1 ) are processed by a shared-weight image encoder. The Cross-Modal Feature Enhancer injects language-derived priors about new objects in T1 into visual features, producing an initial change mask. The Geometric–Semantic Matching Module refines this mask by enforcing spatial completeness and semantic consistency.
Model
ChangeVPR
VL-CMU-CD
PSCD
NYC-CD (ours)
F1
IoU
F1
IoU
F1
IoU
F1
IoU
GeSCF
0.47
0.36
0.77
0.65
0.40
0.28
0.16
0.10
GeSCF+SCD4VPR
0.64 (+17%)
0.55 (+19%)
0.82 (+5%)
0.71 (+6%)
0.49 (+9%)
0.37 (+9%)
0.57 (+41%)
0.49 (+38%)
RSCD
0.24
0.16
0.83
0.73
0.54
0.40
0.13
0.17
RSCD+SCD4VPR
0.63 (+39%)
0.54 (+38%)
0.85 (+2%)
0.75 (+2%)
0.59 (+5%)
0.46 (+6%)
0.70 (+57%)
0.58 (+51%)
C-3PO
0.12
0.08
0.50
0.39
0.66
0.54
0.09
0.10
TABLE II : F1 and IoU comparison of GeSCF, RSCD, and C-3PO with and without SCD4VPR across four datasets. Bold: highest per dataset. Green : improvement over baseline.
Class
F1
IoU
RSCD
+SCD4VPR ( Δ )
RSCD
+SCD4VPR ( Δ )
Non-change
0.899
0.945 (+4.6%)
0.816
0.895 (+7.9%)
New/Missing Obj.
0.438
0.472 (+3.4%)
0.281
0.309 (+2.8%)
Appearance
0.655
0.703 (+4.8%)
0.487
0.542 (+5.5%)
Viewpoint
0.459
0.563 (+10.4%)
0.298
0.391 (+9.3%)
Mean
0.613
0.671 (+5.8%)
0.471
0.534 (+6.3%)
TABLE III : Multi-class change detection on NYC-CD (RSCD backbone).
Method
F1
IoU
RSCD
0.127
0.168
RSCD + Text (InternVL)
0.513
0.345
RSCD + Text (QwenVL)
0.537
0.401
RSCD + Text (GPT-4o)
0.563
0.424
RSCD + Text + SM
0.584
0.487
RSCD + Text + SM + GM
0.696
0.582
TABLE IV : Ablation on NYC-CD using RSCD as the base architecture.
Fig. 4 : Qualitative comparison on NYC-CD. SCD4VPR produces semantically accurate multi-class masks distinguishing object changes , appearance changes , and viewpoint-induced changes , while RSCD (SOTA baseline) produces fragmented binary detections that miss or misclassify meaningful scene changes.
Strategy
Obj.
App.
VP.
No-chg.
DB size
NU (naïve update)
replace
replace
replace
replace
constant
R (replace)
replace
replace
skip
skip
constant
R+V (replace+viewpoint-add)
replace
replace
add
skip
grows
A (add-all)
add
add
add
skip
grows
NA (naïve append)
add
add
add
add
grows
TABLE V : Database update strategies. Each column shows the action taken for a given SCD4VPR per-pair output category—object change ( Obj. ), appearance change ( App. ), viewpoint-induced change ( VP. ), or no change ( No-chg. ).
Fig. 5 : ( Top ) Recall@1 over four temporal windows; all strategies evaluate against the current database before updating. ( Bottom ) Database size over time. R+V achieves the highest recall at W3 and W4 while maintaining a 27% smaller database than NA. NU, R, and D0 maintain constant.
Fig. 6 : Recall@1 vs. database size across four update windows. Marker fill shows progression from Window 1 (hollow) to Window 4 (solid, outlined); Windows 2–3 are faded. Static (D 0 ), R, and NU maintain a constant database size of 20,687 images across all windows; their markers are offset horizontally by ± 500 images purely for visual separation and do not reflect actual database size differences.
Window
Strategy
R@1
R@5
R@10
R@20
W2
Static ( D0 )
79.7
86.6
88.0
89.5
NU
84.1
90.9
92.0
93.0
R
82.5
89.5
90.9
92.4
A
86.0
92.0
93.2
94.1
NA
86.7
92.4
93.6
94.4
R+V
85.5
91.8
93.0
94.1
TABLE VI : Extended recall metrics (R@1/5/10/20) by strategy and window.
Detecting what has changed in an environment is essential for long-term autonomy, yet most change detection settings assume fixed viewpoints, mild misalignment, or only a few changed objects. We introduce Video-based Scene Change Detection (VSCD), which predicts a pixel-wise change mask for each query frame, given a reference and a query RGB video of the same indoor space recorded at different times under unconstrained camera motion. The two videos are not temporally synchronized, and many object instances may appear or disappear. To study this setting, we build a large-scale benchmark with over 1.1 million frames annotated with pixel-accurate change masks, together with a real-world test set for evaluating transfer beyond simulation. We propose a query-centric multi-reference model that learns temporal matching implicitly from change-mask supervision, aligns candidate reference features to the query via local patch correspondence, and fuses per-candidate change features using frame-level and patch-level confidence before decoding a high-resolution mask once per frame. Our approach achieves state-of-the-art performance against strong image- and video-based baselines, and we validate its real-world impact by deploying it on a mobile robot for two downstream applications -- visual surveillance and object incremental learning.
Jiae Yoon, Ue-Hwan Kim
Department of AI Convergence, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea · GIST InnoCORE AI-Nano Convergence Institute for Early Detection of Neurodegenerative Diseases, Gwangju Institute of Science and Technology, 61005 Gwangju, Republic of Korea.
Earth-surface monitoring requires change detection models capable of recognizing arbitrary semantic categories. Open-Vocabulary Change Detection (OVCD) addresses this need. However, existing methods often entangle temporal perception, semantic discrimination, and region verification, causing unstable results and redundant computation. Inspired by human visual change perception, we propose CogVis, a cognitive memory-guided framework that reformulates OVCD as a perception-memory-verification paradigm. CogVis first employs a Scene Change Perceptron (SCP) to extract a reusable, category-agnostic change prior from frozen bi-temporal features, thereby decoupling temporal evidence from semantic category decisions. A Semantic Memory Calibrator (SMC) then compensates for category-dependent score shifts by dynamically estimating an image-query-specific decision threshold. Finally, an Adaptive Region Filter (ARF) filters connected candidates using learned semantic, temporal, and structural reliability. Experiments on seven benchmarks spanning semantic change detection, binary change localization, and building-damage assessment show that CogVis achieves state-of-the-art performance across all evaluated datasets. By sharing scene-level change perception, CogVis further avoids repeating category-agnostic temporal perception across queries and improves inference throughput by 28.50%.
Zijie Wang, Chen Zhong, Wei He
Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University Wuhan, China
Scene Change Detection (SCD) evaluates changes using predefined query-reference (i.e., present-past) image pairs. However, this formulation overlooks a critical dependency: the corresponding query-reference pair is assumed to be prepared in advance. In real-world applications, such as mobile robots, future query views cannot be known in advance, and thus their corresponding reference images cannot be predefined. To remove this dependency and push change detection toward more practical applications, we introduce Environmental Change Detection (ECD). A key aspect of ECD is to avoid unrealistically predefined and aligned query-reference pairs and instead retrieve environmental cues from an uncurated image database of reference scenes. To tackle this new challenging task, we additionally introduce an initial solution that enables change detection under unknown and imperfect query-reference conditions. The main idea of our solution is to retrieve multiple reference candidates and aggregate semantically rich representations for change detection. We further construct ECD benchmark sets by reformulating three standard change detection datasets. Extensive experimental results demonstrate the efficacy of our solution in both ECD and SCD.