GOLDILOCS: GENERAL OBJECT-LEVEL DETECTION AND LABELING OF CHANGES IN SCENES
Authors: Almog Friedlander
Ariel Shamir
Ohad Fried
Venue: ICLR 2026 Poster (2026)
Keywords: Change detection
scene change detection
semantic change detection
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: We propose GOLDILOCS: a novel zero-shot, pose-agnostic method for object-level semantic change detection in the wild. While supervised Scene Change Detection (SCD) methods achieve impressive results on curated datasets, these models do not generalize and performance drops on out-of-domain data. Recent Zero-Shot SCD methods introduced a more robust approach with foundational models as backbone, yet they neglect the 3D aspect of the task and remain constrained to the image-pair setting. Conversely, 3D-centric SCD methods based on 3D Gaussian Splatting (3DGS) or NeRFs require multi-view inputs, but cannot operate on an image pair. Our key insight is that SCD can be reformulated as a 3D reconstruction problem over time, where geometric inconsistencies naturally indicate change. Although previous work considered viewpoint difference a challenge, we recognize the additional geometric information as an advantage. GOLDILOCS uses dense stereo reconstruction to estimate camera parameters and generate a pointmap of the commonalities between input images by filtering geometric inconsistencies. Rendering the canonical scene representation from multiple viewpoints yields reference images that exclude changed or occluded content. Rigid object changes are then detected through mask tracking, while nonrigid transformations are identified using SSIM heatmaps. We evaluate our method on a variety of datasets, covering both pairwise and multi-view cases in binary and multi-class settings, and demonstrate superior performance over prior work, including supervised methods.

Published as a conference paper at ICLR 2026
GOLDILOCS: GENERAL OBJECT-LEVEL DETECTION AND LABELING OF CHANGES IN SCENES
Almog Friedlander Reichman University
Ariel Shamir Reichman University
Ohad Fried
Reichman University
ABSTRACT
We propose GOLDILOCS: novel zero-shot, pose-agnostic method for object-level semantic change detection in the wild. While supervised Scene Change Detection (SCD) methods achieve impressive results on curated datasets, these models do not generalize and performance drops on out-of-domain data. Recent Zero-Sho SCD methods introduced more robust approach with foundational models backbone, yet they neglect the 3D aspect of the task and remain constrained to the image-pane pair setting. Conversely, 3D-centric SCD methods based 3D Gaussian Splatting (3DGS) or NeRFs require multi-view inputs, but cannot operate on an image pair. Our key insight is that SCD can be reformulated as 3D reconstruction problem over time, where geometric inconsistencies naturally indicate change. Although previous work considered viewpoint difference challenge, we recognize the additional geometric information as an advantage. GOLDILOCS uses dense stereo reconstruction estimate camera parameters and generate pointmap of the commonalities between input images by filtering geometric inconsistencies. Ren- dering the canonical scene representation from multiple viewpoints yields reference images that exclude changed or occluded content. Rigid object changes are then detected through mask tracking, while nonrigid transformations are identified using SSIM heatmaps. We evaluate pairwise and multi-view cases in binary and multi-class settings, and demonstrate superior performance over prior work, including supervised methods.
method on variety of datasets, covering both
INTRODUCTION
Scene Change Detection (SCD) is fundamental task in computer vision with broad applications in anomaly detection (Kruse et al. 2024), environmental monitoring (Khan et al. 2017). infrastructure management (Han et al.) 2021 and autonomous navigation (Krawciw et al.) 2024 Takeda et al. 2023). Given images of the same scene captured at different times, i. To and Ti. SCD aims to detect meaningful changes in the scene while remaining robust to illumination, viewpoint, and occlusion. Accurate SCD enables powerful downstream capabilities, such as updating geospatial
databases (Taneja et al. 201 and lawful surveillance (Huwer & Niemann, 2000 Zhang al. 2020).
Despite its utility, SCD remains challenging. Early deep learning methods cast as supervised segmentation, predicting binary change masks from image pairs (Alcantarilla et al. 2018 Varghese
al. 2018 Sakurada al. 2020 Chen et al. 202T), but required large, domain- -spccicc annotated
datasets. To mitigate this, semi- (Park et al. (022)) self-supervised (Ramkumar et al. 2022 Furukawal
al., 2020b), and synthetic-der approaches (Park et al. 202T Sachdeva & Zisserman (023a) were
proposed, though generalization of these methods remains limited. More recently, foundation models
have been introduced: SAMCD (Ding et al. 2024) integrates SAM (Kirillov al.] (023). while ZSSCD (Cho et al., 2025) leverages SAM2 (Raii al. (024) for segmentation and DEVA (Cheng cct al.) 2023) for mask tracking. While promising, these methods are confined to 2D and struggle to disentangle true changes from viewpoint differences.
In this work, we reformulate SCD as a 3D reasoning task. Our key insight is that meaningful changes appear geometric inconsistencies in reconstructions from time-separated views. If an object is added, removed, or moved between Tn and T,, then 3D reconstruction algorithms, which assume scenes statie, will fail to reconcile the region, yielding inconsistent geometry.

---

Published as a conference paper at ICLR 2026
We introduce GOLDILOCS (General Object-Level Detection and Labeling Of Changes in Scenes), zero-shot framework for object-centrie SCD. Our proposed framework identifies changed objects and labels the semantie type of change, inspired by experience of the three bears of the titular fairytale. Moreover, GOLDIIOCCS can detect changes even with a single image at To and a single image at Ti, given sufficient 3D overlap. Our method leverages a dense stereo 3D reconstruction model (Leroy itt al., 2024) to estimate camera intrinsics, extrinsics, and per-pixel 3D structure. It then performs depth filtering to remove temporally inconsistent geometry and yields a canonical static reconstruction of the scene. By comparing each input image to the clean rendering and propagating masks with SAM2 (Ravi et al. 2024), GOLDILOCS can identify and categorize scene changes as object-level additions, removals, movements, non-rigid transformations. The latter are detected via SSIM maps (Wang et al. 2004) highlighting local structural distortions across time.
Unlike prior work, GOLDILOCS is training-free, calibration-fres and generalizes to unconstrained, real-world imagery. We demonstrate state-of-the binary and multi-class change detection under zero-shot conditions on both image pairs and sets.
2 RELATED WORK
This section reviews prior work, from early image differencing approaches to emerging zero-shot and 3D representation-baae based methods. The discussion highlights how existing approaches balance label dependence, generalization, and geometric reasoning, motivating our proposed solution.
Pre-Neural Network Methods. Earlier approaches to SCD included simple image differencing, likelihood ratio tests, probabilistic mixture models, and extended up shading models and back- ground modeling; see Radke et al. (2005) for a survey. While these early methods established the systematic frameworks for change detection, their performance was often undermined by the reliance on pre-processing steps that addressed illumination variations and geometric misalignment.
Supervised Methods Supervised SCD methods rely on labeled image pairs and change maps.
Early work (e.g. (Sakurada & Okatani (2015) combined CNN features with superpixels. More advanced designs include ChangeNet (Varghese et al. 20118) Siamese-inspired CNN (Mueller & Thyagarajan 2016 Rao et al., 2017), , and transformer-based models (Wang al. 202 Chen et al. 2021) exploiting attention for feature differencing. SimSaC (Park et al. 2022) adds an optical-flow warping module, while C-3PO (Wang et al. (203) utilize backbone network and trains separate branches for change types. CYWS-3D (Sachdeva & Zisserman) (023b) extends supervision into 3D with frozen transformer backbone. These methods perform well in-domain but remain tailored to dataset-specific styles and image pair relationships, limiting generalization.
Semi-, Weakly- and Self-Supervised Methods To mitigate reliance on labeled data, semi- supervised (Lee & Kim 2024), wreakly-supervisee (Sakurada et al., (020) have been proposed. Additionally, SACD-Net (Furukawa et al., (020a) learns in a self-supervised manner from unlabeled data by jointly optimizing viewpoint alignment and change detection. While these approaches reduce label cost, they often overlook the difficulty of collecting image pairs. Data augmentation (Sachdeval & Zisserman 2202bb Lee & Kim| 2024) and synthetic datasets (Park et al., 2021) help increase training volume, but supervised pipelines still struggle with -ft-oi-domain images, robustness to style and domain variations is rarely addressed.
Zero-Shot Methods. Zero-shot approaches aim to detect changes without relying on task-specific training data, but such methods remain scarce. ZSSCD (Cho et al. (025) combines SAM2 (Ravi ct al. 2024) for segmentation with DEVA (Cheng et al. 2023) for temporal tracking across image and video pairs. Nevertheless, these methods lack 3D component to address viewpoint differences between Ty and Ti.
3D Representation-
SCD Recent works approach SCD by explicitly modeling the 3D structure of a scene, enabling view-independent reasoning rather than direct image differencing. C- NeRF (Huang et al. 2023) detects changes by generating two NeRFs for Ty and Tı, then comparing rendered views from aligned camera poses. Direction-consistent radiance differences highlight changes, but the method requires many images at both To and T,, controlled camera trajectories, and time-intensive optimization. 3DGS-CD (Lu al. 2025) offers a more efficient altemative by
2

---

Published as conference paper at ICLR 2026
A Removed
B Added
C Warped
U1 - 11-ccclueed
1b
I
To
I
D Moved (Full)
Moved (Partial)
U2 Out of frame
Static object
Target object
Object's previous location
L
I
Figure 1: Change types. Scenes A and B illustrate object removal and addition, respectively, Scene C depicts a warped object that remains static in space but undergoes internal structural change. Scenes D and E illustrate full and partial object movements, distinguished by whether the object's volumes intersect between To and Ti (the light blue ghost ball the old position at To and is placed in the I image for clarity). Scene U1 shows and scene U2 depicts an occluded object-both are thus unclassifiable.
where the object's location is not visible in both views,
reconstructing 3D Gaussian Splatting model from pre-change images and comparing novel-view renders against post-change observations with EfficientSA.M (Xiong et 2023). This approach improves scalability and is robust to sparse post-change inputs, though it still requires a large image set taken at To, auxiliary images from Ti, and explicit reconstruction. Gaussian Difference (Jiang et al. 2025) extends this idea by encoding spatial and temporal information within unified 4D Gaussian model. Instance IDs assigned via SAM and DEVA are used to track objects and identify temporal inconsistencies, enabling gaussians to be classified changed or unchanged. This produces accurate change maps from arbitrary viewpoints while improving robustness to lighting variations and reducing computational cost, though multi-view image sets both time points still required.
Several related methods, such as Taneja et al. (2011a) and Palazzolo & Stachniss (2018), rely on 3D model the base and detect changes images by reprojecting views using the provided 3D geometry. Adam et al. (202) extends this by requiring 3D models at both times, employing a graph optimization approach for object-level change detection. While these methods achieve strong performance, they depend on access to 3D model at one or both times and cannot operate on RGB image inputs solely.
Our Approach. Building upon these advancements. approach integrates the strengths of foundational models, 3D reasoning, and flexibility to varying inputs to achieve robust and efficient scene change detection. Using both 3D reconstruction and label-agnostic segmentation and tracking backbones, our method addresses the challenges of viewpoint variations and in-the-wild images, paving the way for more generalized and scalable SCD solutions.
PROBLEM DEFINITON
Given a pair RGB images 3D scene, lo and 1 captured at times To and Ti respectively, our goal is to detect and classify object-level changes between the two observations. Specifically, we aim to identify which objects have been moved, added, removed, or warped (i.e., undergone non-rigid transformations). For simplicity demonstrate the case of pair of images, but these definitions naturally extend to a pair or sets of images, To and I₁.
Object-centric Change Detection Taxonomy We propose a standardized taxonomy for object- level change labeling in 3D scenes, grounded in geometric and visibility- -ased reasoning. We define an object as any visually discernible entity present in an image. This includes volumetric elements such as boxes furniture, well planar or texture-bound features like a blot of ink on sheet of paper. The essential criterion visibility and perceptual distinguishability in the image domain, irrespective of semantic identity material composition. Some caveats this definition include phenomena such as shadows, reflections, and camera-induces artifacts like lens flare, which, while not being objects in the conventional sense, treated as such under this visibility-centric interpretation.

---

Published as a conference paper at ICLR 2026
Rigid change detection classification
Segmentation
Propagoted Masks
Mask Tracking Model
Added
Removed
Segment. Model
Warped
Moved
M
M
Jo
Pointmaps Camera Proj. Metrices
Clean Pointclouds
Renders
SSIM Heatmap
Co
o
C
O
Raa
Ry.o
Po
P1
R
R..
3D Stereo
Non-rigid change detaction
Recon.
pointcloud cleaning
Novel view synthesis
SSIM
Model
Input
ModelProcesss Output
Output
Pipeline overview. Given pair of images, GOLDILOCS first reconstructs the 3D geometry of the scene, then uses rendering of novel views, segmentation, and tracking of object masks across time to identify object-level changes including non-rigid transformations.
Figure 2:
Based on this definition, we categorize object-level changes into the following mutually exclusive types, and visualize them in Figure
Removed. An object that is visible in to but no longer visible in 41, and its spatial location at time To is visible Jn (Figure[Ty If the region occluded (Figure IJ), or out of the frame (Figure (2)) in I1, conservatively refrain from classifying the object removed due the absence of confirmatory evidence.
Added. Symmetric to the removed case. An object labeled as removed in 4o + 11 labeled as added in I - Io-
Moved. An object that appears in both In and I, but its location in 3D space has changed If the spatial volumes occupies at To and Ti are disjoint, we term the move "full" (Figure ID), and they overlap, the move is "partial" (Figure TE).
Warped. An object is warped if it is visible in both Iy and I, occupies approximately the same location in 3D space, but exhibits non-rigid deformation, e.g., bending. creasing, or squashing (Figure IC).
METHOD
A key insight motivating approach is that change detection between images be naturally interpreted as form of 3D reconstruction over time. However, while traditional multi-view stereo assumes that images captured within short time frame from different viewpoints, ensuring that the underlying scene remains static, we consider the case where the input images 4y and I₃ are taken different times, To and Ti, such that the scene is allowed undergo structural changes. To simplify the explanation, we first describe our framework assuming an image pair input; later, we will extend it a pair of multi-view image sets, detailed in Section 4.6
Under this formulation, objects or regions that have changed between To and Ti will violate the static scene assumption. As result, these regions are likely to produce inconsistent correspondences, contradicting depth estimates, or geometrically incompatible projections. By explicitly analyzing the
We acknowledge potential ambiguity in cases involving visually identical instances (e.g., two identical pens, removed, and another added to the scene), and assign movement classification regardless.
4

---

Published as a conference paper at ICLR 2026
3D reconstruction under temporal variation, gain a principled and geometry-awart mechanism for localizing and identifying scene changes. An overview of our method can be found Figure
4.1 STEREO 3D RECONSTRUCTION AND CAMERA ESTIMATION
We employ MAS13R (Leroy al. 2024), a foundational stereo 3D reconstruction model, as the core module to establish dense, geometrically grounded correspondences between pair of images To and I, to estimate the intrinsic and extrinsic parameters the cameras, and to model the 3D structure of the scene. Specifically, given a pair of RGB images lo, / E R x3 the stereo 3D reconstruction model produces dense pointmaps Po: P1 E R R"XW*x3 x representing the 3D coordinates of each pixel, reconstructed in shared coordinate system. The reconstruction also produces camera projection matrices Co. C1 E 3x3x4 with C; = [ [r]. where K; and RR, L:] are, respectively, the intrinsic and extrinsic camera parameters.
The choice of MASt3R for stereo reconstruction is motivated by its state-of-the-art performance on uncalibrated images and its ability to produce dense geometry and camera poses, key 3D reasoning. However, this component can be replaced by any other method with similar outputs.
4.2 CONFLICT RESOLUTION VIA DEPTH FILTERING
To eliminate geometric inconsistencies and occluders, we clean the pointmaps using a reverse depth test. Let P; denote the pointmap reconstructed from image 4, where each entry is 3D point in world coordinates. For every target view 7j. we additionally have a depth map Dj that records the camera-space value at each pixel, derived directly from the reconstruction process.
Given a world point p E P., we reproject it into the image plane of image I, to obtain the pixel coordinates (u, v) and its camera-space depth Zi-zj:
(u, U, وج=ز]) C₁P
where Cj the projection operator (intrinsies and extrinsics) mapping world-space points image coordinates in view j.
Depth Conffict Resolution. We define the cleaned pointmap PClenn C P, the subset of points from P; that do not conflict with the geometry observed in the opposing view I5. Specifically, point p E P, is considered conflicting with view if:
ži < Dj(u, v), where (w, U, -)-5)) = CjP
This condition indicates that p would project in front of the surface observed by Ij. and therefore represent an occluder or a changed object.
By removing geometrically conflicting points through reverse depth filtering, we obtain P PCleen and PClean that exclude points that are likely to correspond to objects that were added, removed, or moved between To and Ti. The union of these cleaned pointmaps, denoted P., is composed of the farthest visible surfaces of the scene and provides a consistent representation of the static scene geometry. A detailed example of this process illustrated in Figure 8 in the appendix.
Rendering Pointmaps as Images. We denote by Ra,j the rendered image of pointmap p: from the viewpoint of I,. where each point p E P, is projected using Cj and colored using its appearance in image 7, For example, Ro,0 denotes rendering with the geometry and viewpoint of To. an approximate reconstruction of the original image To. while Ro,1 synthesizes how the geometry captured in fo would appear from the viewpoint of Z,, allowing cross-time and cross-view comparisons.
The joint cleaned pointmap P, is utilized as canonical reference geometry for downstream compar- isons. For example, to identify objects only visible Io, we render P. using Co, producing R2,0- Discrepancies between this rendering and the reference image Io indicate the presence of dynamic or changed elements. By analyzing such discrepancies in the image space, we can reliably localize and classify the types of changes the objects underwent.
5

---

Published as a conference paper at ICLR 2026
4.3 SEMANTIC SEGMENTATION
We apply semantic segmentation through a foundational model for class-agnostic dense segmen- tation (Ravi et al. 2024), which offers state-of-the-art instance segmentation and propagation. As with the 3D reconstruction model, this component is modular and any mask-generating and tracking system can be used instead.
For an image 4; the segmentation model outputs a set of binary masks:
Mr {mask], mask............................ masky), mask E {0. 1) #x
(I)
To produce object-level predictions that align with our change taxonomy, we operate on rendered views of the reconstructed scene. Specifically, to detect objects that were removed moved between times To and Tú we first segment the rendering Ro,1. obtaining masks MR.. Note that we segment 1 and not To most often ground truth annotated from the point of view of Jı, thus it is required to generate masks for objects present only in To. e.g removed objects, using C1 to achieve the desired predictions. Masks are then propagated the clean rendering Rs,1 using the object tracking functionality of the segmentation model. Masks that fail to track are considered changed:
M Changed
=MR..
Track[MRR.. Ro.1 + R
Ro.1
We then pass the masks M Changed to the tracking model and propagate from R I1 to differentiate Ro., between moved and removed objects:
M Moved
Track M Channgd
Ro,1 Ii)
Ro.1
Ro.1
M Remooedd
Changed Movedd
Ro.1
Ro,
To identify added objects and validate moved objects detected in the previous process we perform complementary procedure:
Mf Changed
Mr Track(M 11 - R,,1)
M MMoved Track( (M,
Changed 11 + Ra,1)
l
M
Added M Changed \ MMooed
Filtering. To prevent false positives in occluded or out-of-view regions, we apply a visibility-aware filtering step to all tracked mask sets. For each predicted mask, we compute the proportion of its area that overlaps with gaps in Ro.1, i.e., regions that are either occluded or lie outside the mutually visible field of view between the two input images. We discard masks with an overlap proportion larger than threshold E [0, 1]. This filtering step ensures that only masks grounded in reliable geometric evidence are retained. The value of a is set once for each dataset, and correlates to the amount of overlap between image pairs, see appendix A.6 for detailed explanation.
4.4 DETECTING NON-RIGID CHANGES VIA VIEWPOINT- ALIGNED COMPARISON
Beyond rigid changes; additions, removals, and movement, we identify static objects that experienced non-rigid transformations that alter their structure. Towards this end compute dense Structural Similarity Index Measure (SSIM) map between the rendering Ro,1 and the target image 71- SSIM is well suited for this task captures perceptual differences in structure, luminance, and contrast, making it particularly effective in detecting subtle appearance changes in deformable objects that remain spatially static, such as a tablecloth that appears smooth Tú but wrinkled in Ti. We calculate the mean dissimilarity score of each static segmentation mask in M,, and classify those farther than a standard deviation from the mean across all masks as warped.
4.5
PER PIXEL PREDICTION
The per-pixel prediction for the target view I is obtained by stacking the filtered labeled masks according to the priority order W arped Moved Removed> Added. This ordering ensures that removed-object masks take precedence may appear "added" simply because it was previously occluded by a removed object rather than newly introduced into the scene.
added-object masks, resolving cases where an object

---

Published as a conference paper at ICLR 2026
4.6 MULTI-VIEW GENERALIZATION
Although introduced for pair of images, our method naturally extends to pair of image sets consisting of multiple views either both times: To (1))-= from To and I { /m from TT This capability expands GOLDILOCS's scope from image pairs to multi-view scenarios, including cases with single image from Ti, where [
Top-1 Selection via Geometric Matching, We define the matching score scalar value of two images as the number of corresponding points that are matched between the images by the 3D stereo reconstruction model. We compute pairwise matching scores between every target view Iī I1 and each candidate view IJ E Io- Then, each 7j is paired with its top ranked match from To as input for the pairwise pipeline as before.
Viess-ViewWaa Voting via Pixel Correspondence. Let be the per-pixel predicted labels for 7i: where each pixel is labeled from the set L {Added, Removed, Moved, W Warped, Unchanged] Given a segment s in f whose label is E C, let (s((s E Pi be the set of 3D points that projects to s. We map the coordinates of the pixels of s into _ by projecting P:(s) to the set of visible pixels in Cj. denoting the labels of this projected region by i' (8:-a)) Each different view I3 I then votes on the label of according to the per-pixel majority vote within (s;=s)):
label:(s) =
label = (ooee(((ss))))
If [Z]] m, then the final label of segment is given by cross-view majority:
(abel(s)) () = mode (flabel, (s) 1i= m)).
Ties are resolved by fixed priority over L. This ensures that each segment casts exactly one vote per view towards the final prediction, leading to consistent labeling across all target images. More details can be found in Appendix [A.3] and a visualization of the additional components relating to the multi-view view extension and it's interface with the pairwise pipeline in Figure
RESULTS
We evaluate our method across a diverse set of datasets including both real and simulated envi- ronments, indoor and outdoor scenes, and various types of change. These datasets differ in image alignment, annotation granularity, and in the types of changes they contain: target changes, which correspond to the object-level modifications from imaging conditions such as illumination, weather, or seasonal variation. While GOLDILOCS relies segmentation model that requires parameters, most settings shared across datasets; did not conduct a brute-force parameter search for the optimal settings for each dataset, and instead recommend values derived from dataset characteristics. See Appendix A.6 for further details.
aim to detect, and non-target changes, which arise
A summary of these key characteristics is provided in Table and an in depth explanation is provided in Appendix A.I Following the standard evaluation protocols commonly used for each dataset, we report Precision, Recall, F1 Fi-score, and Intersection over Union (IoU) for the NeRFCD and 3DGS-CD datasets. For ChangeSim and VL-CMU-CD, we additionally compute per-class IoU and F1, along with mean loU and macro-averaged F1.
We evaluate our framework against broad set of baseline methods, reporting performance using each method's 's original evaluation protocol. On datasets that include both binary and multi-class labels, we assess our approach under both settings to provide comprehensive evaluation. A method is considered in-domain if it was trained on To-Ti image pairs from the dataset prior inference on the test set. Table [ summarizes the capabilities of all compared methods, including required training, support for image pair and Qualitative results of our method are presented in Figure 3 and examples of the warped change type appear in Figure in the appendix.
inputs, semantic change labeling, and object-level prediction.
7

---

Published as a conference paper at ICLR 2026
Table 1: Overview of the evaluation datasets.
Dannett
Setting
Real/ / Sim
Indeur/ Quidoer
Image Aligsment
Target Change
Non tarpet Change
VL CML Alcomoolaa et 2013
Pairwise
Real
Oundooe
Counee
Missing objects
Weather, illumination
RC33D Sach & Zistnn 2023
Painwise
Real
Indoor
Nene
Missing ohjects
Ilumination
ChangeSm maan FII KM4
Pairwise
Samulated
Indoer
None
New, missing, noved. replaced
Illuiination Illuiiationn
NeRFCD (1ogns e 2014
Mutu view
Real
Indoor
None
New objects
3DXIS-C1
Mullissiww
Real
Indoor & Outdoer
Neoc
New, moved objects
Illmiiation
Table 2: Comparison of baseline SCD methods.
Mathod
Zemowhoe
Painsdee
Multi-imaae
Min. Views raquine nulli-inage inference
Change labeling
Worp Dotection
Object-lenel Prodiction
CSUDNer
CYWS 30
DR-IANa Eirrar
C.3PO (Whire pur4
ZSSCD Lossspil Aipe
CARRE Commereeppa
20-
3DGS-CIr (errapee slin3
Cousson Uni prinecio 2025
20-
GOLDiLOCS (nun)
ChangeSim Table presents quantitative results on the ChangeSim dataset, evaluating performance in both binary and multi-class settings. GOLDILOCS outperforms all baselines in terms of IoU and mloU for binary change detection, and achieves superior results across all multi-class categories and mean loU-except for the rotated class. Although we map ChangeSim's rotated category to our moved change type, in practice, many objects labeled as rotated are only slightly reoriented or toppled without significant spatial displacement. Given the dataset's 's resolution, scene depth, and the small pixel footprint of individual objects, this class remains particularly challenging for our framework. Table further reports scores on the binary changed class alone (a less commonly used protocol) where GOLDILOCS still outperforms several supervised, in-domain baselines.
VL-CMU-CD While GOLDILOCS does not match the performance of supervised, in-domain baselines, attribute this two key factors: (1) our framework produces precise object-level masks, whereas the dataset's s ground truth consists of coarse polygonal annotations, and (2) the dataset contains noisy labels with some genuine changes left unannotated, further penalizing methods that make fine- the-grained predictions, e.g., Figure 3 row on the right. Despite these challenges, GOLDILOCS achieves Fl score of 61.8%, the highest score among zero-shot methods, outperforming ZSSCD by a significant margin. Detailed results can be found in Table in the appendix.
RC-3D GOLDILOCS outperforms all baselines on this dataset while relying solely on RGB input and requiring no training, achieving an mAP of 0.531. In comparison, CYWS using only RGB input reaches mAP of 0.14, while CYWS-3D significantly boosts performance to 0.41, and further improves to 0.50 with RGB-D input. We attribute method's performance to the strong geometric priors encoded by the 3D reconstruction model, which enables our method to localize object-level changes downstream with high precision, even in the absence of depth input or dataset-specific training. Detailed results are provided in Table 9 in the appendix.
3DGS-CD Table 4 reports performance on the 3DGS-CD dataset across five diverse scenes. Note that GOLDILOCS performs inference on the test set without access to additional Ti views, while 3DGS-CD baseline relies on separate subset of four Tj images for reconstruction and inference. Moreover, unlike 3DGS-CD, our method avoids time-intensive explicit 3D reconstruction, achieving test-time inference within minutes per image compared to the hours required by the full 3DGS-CI CD pipeline. Even without auxiliary Ti frames and despite its significantly faster runtime, GOLDILOCS outperforms in both IoU and Fl metrics.
NeRFCD Table presents quantitative results scenes from the NeRFCD dataset. The compared methods achieve strong performance, but rely on dozens to hundreds of images per time point, captured from overlapping camera trajectories, and require significant computational effort. In contrast, GOLDILOCS outperforms other methods in the Fl and IoU metrics while operating in a zero-shot setting, without auxiliary J, images, in substantially shorter runtimes as shown in Table 5
8

---

Published as a conference paper at ICLR 2026
1o 1
GT
Ours 1p
Ours
---
Figure 3: Examples of different change types (Added, Removed, Moved, Replaced) detected by method compared to ground truth (GT) indoor and outdoor scenes. Our results provide color-coded labels with binary GT and exceed its accuracy, annotations often coarse miss small changes. This, however, can lower our reported metrics as we compare to such imperfect GT. Figure provides more qualitative results.
Table 3: Results (loU) the ChangeSim dataset comparing binary and multi-class performance. mloU provides the holistic metric, averaging over all multi-class labels.
Binary
Multi-class
Method
Changed
Unchanged
mloU
Added
Removed
Moved
Replaced
Unchanged
mloU
ChangeNet
17.6
73.3
45.4
6.9
11.6
6.6
80.6
23.0
CSCDNet
22.9
87.3
55.1
12.4
6.0
17.5
7.9
90.2
26.8
C-3PO
28.8
90.4
59.6
13.3
8.0
16.9
8.0
92.6
27.8
ZSSCD!
25.2
89.3
57.2
21.9
0.0
12.6
89.3
26.7
Ours
37,3
92.5
64.9
25.4
20.9
7.7
21.4
92.5
33.6
In onder perform accunite comparisoe with 2SSCD which reports metrics on video sequence inputs, we evaluated their method on the dalaset pair-w lie setting utilizing their published code.
Table 4: Results on the NeRFCD and 3DGS-CD datasets. Fl combines precision and recall, providing a measure overall performance, and can regarded as the primary metric along with IoU.
Ti Images
Dataset
Method
Evaluation
Auxiliary.
Precision
Recall
F17
loUf
3DGS-CD
97.84
97.60
97.51
95.16
3DGS-CD
Ours
4
0
97.09
98.17
97.72
95.30
Gaussian Difference'
10
10+
95.91
88.83
91. 11.90
85.74
D-NeRF
10
10+
47.22
66.92
52.92
37.73
NeRFCD
C-NERF
10+
84.93
93.39
88.89
80.83
Ours
95.14
89.65
91.97
87.62
Note that Gaussian Difference averaged over only of the scenes (Cats, Go pieces, Desk, Potting), omittinng the Block scene.
Table 5: Runtime comparison (in minutes) across pipeline stages on the multi-image Potting scene from the NeRFCD dataset. Our method is over 10x faster than Gaussian Difference and nearly 100x faster than C-NeRF.
Method
3D Reconstruction
Change Detection
Rendering/Gaussian Partition
Total
C-NeRF
964.2 min
45.0 min
46.8 min
1056.0 min
Gaussian Difference
70.2 min
6.0 min
72.0 min
148.2 min
Ours
4.7 min
5.2 min
0.6 min
10.6 min
ABLATIONS
We perform ablation experiments to highlight the importance of our 3D reasoning, segmentation and cross-view voting components. In ChangeSim (Table 10), removing stereo reconstruction and novel view synthesis significantly reduces IoU, particularly in the removed category, confirming that accurate 3D geometry contributes to reliable change localization. An additional analysis on EDGS-CD (Figure shows how performance degrades as images with fewer geometric correspondences

---

Published as a conference paper at ICLR 2026
used matches. Both Fl and IoU trend downwards with image rank, and reconstruction eventually fails below the 20th percentile. Moreover, ablation of the cross-view voting component (Table [T]) shows that it yields large gains in precision and IoU by filtering noisy detections and enforcing consistency across views. We conducted an experiment to examine the relationship between 31) reconstruction quality and detection performance. As shown in Figure [0] even with only 5% of the full reconstruction steps, nearly 90% of the final mloU is retained, demonstrating robustness to varying reconstruction quality. Finally, see Table [3 for an analysis of how different detection thresholds of the segmentation component affect performance on the VL-CMU-CD dataset.
LIMITATIONS
GOLDILOCS relies on SAM2 (Ravi et al. 2024) as foundational model, which can misinterpret shadows, reflections, texture patterns, or camera artifacts (e.g.. lens flares, specular highlights and motion blur of fast moving elements) as objects due to strong local contrast or 3D projection. Segmentation models are also parametrie, requiring different settings for optimal performance across scenarios. Object scale, for example, may vary drastically-a tennis ball might fill half the image or occupy only a few pixels depending on scene necesssicciiitiinng different sensitivity values, Lighting variations also affect other components, drastic illumination shifts can introduce SSIM errors or hinder 3D reconstruction. Additionally, the reliance on statistical SSIM thresholds might lead to misclassifications under certain conditions and more sophisticated techniques may further improve detection on edge cases.
Finally, the prediction pipeline employs label priority (Section 4.5) to resolve overlapping categories. As a result, nested changes within larger objects are not independently labeled: for instance, if a keyboard is displaced between To and Zi but key is also removed, the mask relating to the keyboard is labeled as moved, while the removed key is suppressed.
CONCLUSION
To solve the problem of object-level scene change detection, we introduced GOLDILOCS - a zero- shot, pose-agnostic framework for both pairwise and multi-view inputs that reformulates SCD as a 3D reconstruction problem over time. Our method decouples the challenges of 3D reconstruction from the semantic interpretation of change, allowing each component to be addressed using foundational models without further training. We fuse geometric and semantic cues in a modular inference pipeline, identify rigid object changes through mask propagation, and detect non-rigid transformations via SSIM-based comparison. This approach bypasses the limitations of 2D-only methods and generalizes beyond curated datasets and training regimes.
GOLDILOCS achieves strong zero-shot performance in one-to-one, multi-to-one and multi-to-multi settings across various real-world and synthetic datasets, while its generality and training- free approach allows change detection under minimal assumptions. Looking forward, integrating more expressive segmentation models or specialized 3D reconstruction techniques may further enhance performance. Future avenues of research include support for multi-label classes, nested objects and better distinction of non-target changes, enabling even more powerful general SCD.
REPRODUCIBILITY STATEMENT
The core implementation details, including the foundational models serving as the backbone for GOLDILOCS, are presented in Section 4 which also covers depth-aware point cloud cleaning, novel view synthesis and rendering, SSIM heatmap generation, and both rigid and non-rigid change
detection. Prediction pipelines are described separately: the pairwise case in Section 4.5 the multi-
view case in Section [4.6] and an extended version of the latter in Appendix A.5 Dataset usage appears in Section 5 with an extended version including pre-processing and related details provided
in Appendix 1 Finally, parameter settings and the rationale behind their selection are discussed in
Appendix A.6 Additionally, we will release our code to support reproducibility and further research.
10
ACKNOWLEDGMENTS
This work was partially supported by the Israel Science Foundation Grant no. 1427/25, 1574/21 and Joint NSFC-ISE Research Grant no. 3077/23.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Aikaterini Adam, Torsten Sattler, Konstantinos Karantzalos, and Tomas Pajdla. Objects can move: 3d change detection by geometric transformation constistency, 2022. URL wttpthttss//aiiii
org (atg/ab///0 2208. 09870
Pablo F Alcantarilla, Simon Stent, German Ros, Roberto Arroyo, and Riccardo Gherardi. Street-view change detection with deconvolutional networks. Autonomous Robots, 42: 42:1301-1322, 2018.
Shuo Chen, Kailun Yang, and Rainer Stiefelhagen. Dr-tanct: Dynamic receptive temporal attention network for street scene change detection. In 2021 IEEE Intelligent Vehicles Symposium (IV), pp. 502-509. IEEE, 2021.
Ho Kei Cheng, Scoung Wug Oh, Brian Price, Alexander Schwing, and Joon- Young Lee. Tracking anything with decoupled video segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1316-1326, 2023.
Kyusik Cho, Dong Yeop Kim, and Euntai Kim. Zero-shot scene change detection. In Proceedings of the AAAI Conference Artificial Intelligence, AAAT2///AAIIS/EAAAA22 AAAI Press, 2025. ISBN 978-1-57735-899 -7787775--9788. doi: 10.
Lei Ding, Kun Zhu, Daifeng Peng, Hao Tang, Kuiwu Yang, and Lorenzo Bruzzone. Adapting segment anything model for change detection in vhr remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2024.
Mark Everingham, Lue Van Gool, Christopher Williams. John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88:303-338, 06 2010. doi: 1007/s1 263-000-0025--4
Yukuko Furukawa, Kumiko Suzuki, Ryuhei Hamaguchi, Masaki Onishi, and Ken Sakurada. Self-supervised simultaneous alignment and change detection. In 2020 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), pp. 6025-6031, 2020a. doi:
10.1 N0.10099ROS547443200099999999 90..444000....
Yukuko Furukawa, Kumiko Suzuki, Ryuhei Hamaguchi, Masaki Onishi, and Ken Sakurada. Self- supervised simultaneous alignment and change detection. In 2020 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS), pp. 6025-6031. IEEE, 2020b.
Dongyeob Han, Suk Bae Lee, Mihwa Song, and Jun Sang Cho. Change detection in unmanned aerial vehicle images for progress monitoring of road construction. Buildings, 1(4):150, 2021.
Rui Huang, Binbin Jiang, Qingyi Zhao, William Wang, Yuxiang Zhang, and Qing Guo. C-nerf: Representing scene changes as directional consistency difference-based nerf, 2023. URL https
//arxiv. org/ abs 2322. 02751
S. Huwer and H. Niemann. Adaptive change detection for real-time surveillance applications. In Proceedings Third IEEE International Workshop on Visual Surveillance, pp. 37-46, 2000. doi: 10.1 000.///S0000.568555
Binbin Jiang, Rui Huang, Qingyi Zhao, and Yuxiang Zhang. Gaussian difference; Find any change instance in 3d scenes, 2025. URL
prggabs/2502 16941
Salman H. Khan, Xuming He, Fatih Porikli, and Mohammed Bennamoun. Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9):5407-5423, 2017. doi: 10.1109/TGRS.20112777773
Alexander Kirillov. Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan- Yen Lo, et al. Segment anything. In Proceedings of the IEEF/CVF international conference computer vision, pp. 4015-4026, 2023.
Alexander Krawciw, Jordy Schn, and Timothy D. Barfoot. Change of scenery: Unsupervised lidar change detection for mobile robots, 2024. URL htt.:/tttss//arrc..oor/////////0

---

Published as a conference paper at ICLR 2026
Mathis Kruse, Marco Rudolph, Dominik Woiwode, and Bodo Rosenhahn. Splatpose & detect: Pose-agnostic 3d anomaly detection, 2024. URL htttttp:://axx...or/////////////
06832
Seonhoon Lee and Jong-Hwan Kim. Semi-super scene change detection by distillation from feature-metric alignment. In Proceedings of the IEEE/CVF Winter Conference Applications of Computer Vision, pp. 1226-1235, 2024.
Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, pp. 71-91. Springer, 2024.
Ziqi Lu, Jianbo Ye. and John Leonard. 3dgs-cd: 3d gaussian splatting based change detection for physical object rearrangement, 2025, URL https:ttttssstttt org/abs/2411 03706
Jonas W. Mueller and Aditya Thyagarajan, Siamese recurrent architectures for learning sen-
tence similarity. In AAAI Conference on Artificial Intelligence, 2016. URL https://ap3 semant i cscho 1 ar. org/ Corpus TD: 6657628
Emanuele Palazzolo and Cyrill Stachniss. Fast image-based geometric change detection given 3d model. 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6308 6315, 2018.
doi: 10.1 10.1109/CRA.22188.846600
Jin-Man Park, Jae-Hyuk Jang. Sahng-Min Yoo, Sun-Kyung Lee, Ue-Hwan Kim, and long-Hwan Kim. Changesim: Towards end-to-end online scene change detection in industrial indoor environments. In 2021 IEEE/RSJ International Conference Intelligent Robots and Systems (IROS), pp. 8578-
8585. IEEE, 2021.
Jin-Man Park, Ue-Hwan Kim, Seon-Hoon Lee, and Jong-Hwan Kim. Dual task learning by leveraging both dense correspondence and mis-correspondence for robust change detection with imperfect matches. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13749-13759 2022.
R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam. Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing, 14(3):294 307, 2005. doi: 10.1 109/TIP.2004. 838698.
Vijaya Raghavan Ramkumar, Flahe Arani, and Bahram Zonooz. Differencing based seff-supeeiied pretraining for scene change detection. In Conference Lifelong Learning Agents, pp. 952-965. PMLR, 2022.
Dattaraj J Rao, Shruti Mittal, and S. Ritika, Siamese neural networks for one-shot detection of railway track switches, 2017. URL http::///aaav.eeeee org 11t./1111...0 08036
Nikhila Ravi, Valentin Gabeur, Yuan-Time Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.0001 14, 2024.
Ragav Sachdeva and Andrew Zisserman. The change you want see. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3993-4002, January 2023a.
Ragav Sachdeva and Andrew Zisserman. The change you want to see (now in 3d), 2023b. URL https://arxiv org. wttpa////..........
Ken Sakurada and Takayuki Okatani. Change detection from a street image pair using cnn features and superpixel segmentation. In British Machine Vision Conference, 2015. wtt.tttsss://///
semanticschen
org Dotpaarroo//coooosDD::5330
Ken Sakurada, Mikiya Shibuya, and Weimin Wang. Weakly supervised silhouette-based semantic scene change detection. 2020 IEEE International conference on robotics and automation (ICRA), pp. 6861-68677 IEEE, 2020.
Koji Takeda, Kanji Tanaka, and Yoshimasa Nakamura. Lifelong change detection: Continuous domain adaptation for small object change detection in every robot navigation, 2023, URL
https://arxx org. abs/ 2306. 16086
12

---

Published as a conference paper at ICLR 2026
Apara Taneja, Luca Ballan, and Mare Pollefeys. Image based detection of geometric changes in urban environments. In 2011 International Conference on Computer Vision, pp. 2336-23443 201 la. doi: 10.1 10.1109/CCCV2221...11260
Aparna Taneja, Luca Ballan, and Mare Pollefeys. Image based detection of geometric changes in urban environments. In 201 international conference on computer vision, pp. 2336-2343. IEEE, 2011b.
Ashley Varghese, Jayavardhana Gubbi, Akshaya Ramaswamy, and Balamuralidhar. Changenet: deep learning architecture for visual change detection. In Proceedings of the European conference on computer vision (ECCV) workshops, pp. 0-0, 2018.
Johanna Wald, Armen Avetisyan, Nassir Navab, Federico Tombari, and Matthias Nießner. Rio: 3d object instance re-localization in changing indoor environments, 2019. https://:
org/abs/1908 06109
Guo-Hua Wang, Bin-Bin Gao, and Chengjie Wang. How to reduce change detection to semantic segmentation. Pattern Recognition, 138:109384, 2023.
Zhixue Wang, Yu Zhang, Lin Luo, and Nan Wang. Transed: scene change detection via transformer- based architecture. Opt. Express, 29(25)41140 41427, Dec 2021. doi: 10. 10.16/00E.4407200 URL
29 25 41409
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions Image Processing, 13(4):600-612, 2004. 2004, doi: 10.1109910.222038889986.
Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest landola, Raghuraman Krishnamoorthi, and Vikas Chandra. Efficientsam: Leveraged masked image pretraining for efficient segment anything. 2023. URL
https::/arxiv.com org/ www.abgggss...ooooo..eee..eaeaee abs /tt//aas///2/////////
2312 00863
Xiang Zhang. Honggang Wu, Min Wu, and Celimuge Wu. Extended motion diffusion-based change detection for airport ground surveillance. IEEE Transactions Image Processing, 29:5677-5686. 2020. doi: 10. 109/T11.2000..29999 29948544.
13

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 DATASET OVERVIEW
ChangeSim (Park et al., 2021) is a benchmark dataset for scene change detection, comprising unaligned image pairs from simulated industrial indoor environment. It includes annotations of changes at the object level classified into four types: new, missing, rotated, and replaced. Although prior work has focused mainly binary change detection, we use fine-grained labels in the data set to perform multiclass prediction. To align with our change taxonomy, which does not explicitly define a 'replaced' class, we interpret predicted object additions that spatially overlap with predicted object removals as instances of replacement. Similarly, the 'rotated' category is treated as equivalent to our definition of partially-move objects. All experiments were carried out on the full set of 8,212 pairs of images at a native resolution of 640x480.
VL-CMU-CD (Alcantarilla et al., 2018) a dataset for the detection of scene changes in the real world consisting of aligned and time-separated images. The dataset contains significant visual variations due to seasonal and illumination differences. Following prior work, we evaluate on the test set comprising of 429 image pairs, resized to a resolution of 512x512, and treat the task as binary change detection. Given that the dataset's ' ground truth contains annotations of objects that were present in To and are missing in 7i, only the removed class in our change taxonomy is applicable, and we restrict evaluation to this category.
RC-3D (Sachdeva & Zisserman 2023b) is recently introduced dataset for change detection in real world indoor scenes consisting of unaligned image pairs. includes bounding box annotations of changes the object level. Following prior work, evaluate the available 100 image pairs provided at a resolution of 1920x1440) using the mean average precision metric defined by Everingham et al. (2010). Given that the dataset's ground truth contains annotations of objects that were present in To and are missing in Ti, only the removed class in our change taxonomy is applicable, and we restrict evaluation to this category.
3DGS-CD (Lu et al. 2025) is real- scene change detection dataset captured in cluttered indoor environments. It consists of five scenes Mustard, Desk, Swap, Bench, and Sill each exhibiting object-level changes limited to additions and movements, with binary ground truth. For each scene, approximately 150-200 images are captured at time To and 8 unaligned images are taken at time Ti, of which 4 are used for testing, provided at resolution of 1008x756. An exception is Sill, which includes 60 Ti unaligned images, with 30 reserved for testing.
NeRFCD (Huang et al. 2023) is scene change detection dataset designed to evaluate object-level changes, including forward-facing and 360-degree surrounding setups. It contains ten scenes in total: two surrounding scenes (Cats and Block) and eight forwardr forward-facing scenes (Go Pieces, Mural, Card, Text, Potting, Desk, Pottery and Sculpture). For each scene, approximately 20-200 unaligned images captured times To and Ti, of which 10 reserved for testing. Although the images are unaligned, the cameras at both times follow similar motion trajectories to reduce viewpoint discrepancies. We conduct our experiments at resolution of 1008x756. Due the nature of the scenes and the subtlety of the changes involved, we excluded Mural, Card, Text, Sculpture, and Pottery from our experiments. As presented in Figure these scenes focus on close-up views of individual objects, where changes are limited to fine surface-level details such as abrasions or ink specks, which are oft-of-scope of our object-centric detection framework, and also do not fall under the warped change type from our taxonomy.
3RScan (Wald et al. 2019 a 3D-centric real-world dataset of indoor environments designed to evaluate 3D object instance re-localization. It consists of 1482 RGB-D scans across 478 scenes. For each scan, the dataset provides calibrated RGB-D image sequences with corresponding camera intrinsics and extrinsics, globally aligned textured meshes, dense instance-level semantic annotations, as well as ground truth object alignments deseribing rigid transformations and symmetries between matched instances. This evaluation of geometric and semantic changes in 3D such as object additions, removals, and movements. We conduct our experiments at a resolution of 960x540 and present qualitative results in Figure
14

---

Published as a conference paper at ICLR 2026
Card
Mural
Potting
Text
Sculpture
lo
Mi CYPR
Mith CYPR
Figure 4: Scenes excluded from the NeRFCD dataset.
Table 6: Detailed results on selected scenes from the NeRFCI dataset.
# T2 Images
Dataset
Method
Evaluation
Auxiliary.
Precision
Recallf
FIf
IoUt
Gaussian Difference
10
115
97.47
93.21
92.41
91.03
D-NeRF
10
115
59.24
60.57
59.68
42.71
Cats
C-NERF
10
115
86.70
90.46
88.54
79.47
Ours
95.79
97.91
96.76
93.83
D-NeRF
25.83
79.48
38.06
23.88
Block
C-NERF
10
177
77.70
93.62
84.92
74.04
Ours
10
89.11
82.38
85.42
81.59
Gaussian Difference
10
16
98.64
93.70
96.10
92.51
D-NeRF
10
16
46.34
82.07
58.41
41.42
Go picces
C-NERF
10
16
92.66
99.71
96.05
92.41
Ours
98.67
93.44
95.67
92.26
Gaussian Difference
10
93.99
91.88
92.78
86.75
D-NeRF
10
10
20.81
41.72
27.74
16.42
Desk
C-NERF
10
10
96.02
90.48
93.17
87.24
Ours
10
96.33
98.48
97.30
94.90
Gaussian Difference
10
11
93.54
76.51
84.31
72.67
D-NeRF
10
11
83.87
70.77
76.71
62.23
Potting
C-NERF
71.59
94.66
81.75
69.01
Ours
10
95.78
76.02
84.68
73.54
A.2 DETAILED RESULTS
Table presentssssents per-scene comparisons for the NeRFCD dataset against in-domain baselines. Our method, despite being evaluated zero-shot setting, consistently matches surpasses supervised approaches. On the scenes Cats, Desk, Blocks and Potting we achieve state-of-the-an performance, surpassing all other methods, In the remaining Go pieces scene we achieve comparable results the best performing baseline. These results highlight that our zero-shot framework generalizes effectively across scene types without dataset-specific training or auxiliary images.
Table 7 reports per-scene performance on 3DGS-CD using F1 and mloU the primary metrics of comparison. Relative to the 3DGS-CD method, GOLDILOCS attains higher mean and mean mloU across the five scenes and achieves the top F1/mloU on the majority of individual scenes. Where the bascline is slightly ahead on a particular scene or single metric, the margin is small.
Table reports results on the ChangeSim and VL-CMU-CD datasets using the Fl metric. On ChangeSim, our method achieves 52.4%, outperforming some of the existing in-domain methods. On VL-CMU-CHI which is annotated only for removals, approach again surpasses ZSSCD by over 10% 1..8% vs. 51.6%), although supervised baselines such as C-3PO and SimSaC remain stronger (80.0% and 79.7% respectively). To some extent attribute this higher precision compared to
15

---

Published as a conference paper at ICLR 2026
Table Detailed results per scene the 3DGS-CD dataset.
# Ti Images
Dataset
Method
Evaluation
Auxiliary
Precision
Recall
F11
loU1
3DGS-CD
4
97.68
98.75
98.21
96.48
Mustard
Ours
4
96.24
99.80
97.93
96.06
3DGS-CD
4
4
99.17
95.82
97.47
95.06
Desk
Ours
4
96.79
99.88
98.30
96.67
3DGS-CD
4
98.84
98.82
98.83
97.69
Swap
Ours
98.72
94.35
96.20
93.12
3DGS-CD
4
96.51
96.76
96.64
93.49
Bench
Ours
4
95.60
99.82
97.65
95.44
3DGS-CD
4
95.00
97.86
96.41
93.07
Sill
Ours
4
98.10
96.99
96.51
95.23
the ground truth, which may use crude annotation or miss some changes. These results underscore that while in-domain training still provides edge specific datasets and annotation protocols, shot method generalizes better than existing zero-shot approaches without requiring dataset-specific supervision.
Table reports results on the RC-3D dataset using the mean average precision (mAP) metric. GOLDILOCS achieves mAP of 0.53 using only RGB input and without any training, outperforming even the RGB-D variant of CYWS-3D. CYWS using only RGB input achieves a mAP of 0.14, while CYWS-3D significantly boosts performance 0.41, and further improves 0.50 with RGB-D input. We attribute this performance to the strong geometric priors encoded by the 3D reconstruction model, which enables our method localize object-level changes with high precision, even the absence of depth input or dataset-specet specific training.
Given the unique ground truth format of this dataset, bounding box annotations available on both To and /, we applied the mask propagation procedure in twice, for both viewpoints. Specifically, objects detected in lo were propagated to Ro.1. and conversely, objects detected Ro,1 were propagated back to Io- This bidirectional strategy enhanced robustness to viewpoint changes and ensured consistent object correspondence across views. The downstream detection and classification steps remained unchanged.
Figure Iillustrates qualitative results on a scene from the 3RScan dataset, highlighting moved objects. While GOLDILOCS produces 2D change maps, demonstrate that these outputs can be lifted into 3D space by projecting them onto reconstructed meshes or point clouds. This enables both evaluation datasets with geometric ground truth and visualization in 3D context. However, the nature of the dataset introduces several challenges: the scan sequences often include motion blur, out-of-focuu frames, occlusions from the person performing the scan, and extremely short camera-to scene distances. As a result, many frames capture only fragments of objects rather than complete or semantically meaningful views, posing significant difficulties for our method, which relies on least one image per time point capturing objects in their entirety. Nevertheless, GOLDILOCS is able to handle these out-of-distribution inputs and still produce meaningful predictions.
Taken together, these evaluations show that our method is competitive with superior to strong in-domain baselines many settings, while surpassing existing zero-shot methods. The framework adapts across synthetic and real domains, binary and multi-class tasks, and both pairwise and multi- view scenarios.
Figure 6 highhlgghts several representative cases where objects have remained in place but undergone non non-rigid transformations, such as creasing, denting, squashing and bending. These results demon- strate GOLDILOCS's ability capture subtle shape deformations that do not involve displacement of the object itself. Note that highly textured surfaces, such as the terrazzo tiles visible in the rug example, can give rise false positive masks, limitation of our approach. Such cases underscore
16

---

Published as a conference paper at ICLR 2026
Table 8: Results the ChangeSim and VL-CMMUCD datasets using the F1 metric.
Method
Zero-shot / In-domain
ChangeSim
VL-CMU-CD
CSCDNet
In-domain
32.6
71.0
DR-TANet
In-domain
40.2
75.5
SimSaC
In-domain
69.1
79.7
ChangeNet
In-domain
79.0
C-3PO
In-domain
80.0
ZSSCD
zero-shot
51.6
Ours
Zero-shot
52.4
61.8
Table 9: Results the RC-3D dataset using the mean average precision (mAP) metric.
Method
Training data
Input type
mAP
CYWS
coco-inpainted + KC-3D
RGB
0.14
CYWS-3D
coco-inpainted + KC-3D
RGB
0.41
CYWS-3D
coco-inpainted + KC-3D
RGB-D
0.50
Ours
Zero-shot
RGB
0.53
the challenge detecting genuine non-rigid deformations from artifacts and noise introduced by the 3D reconstruction model, well as illumination phenomena present in the input images.
A.3 ABLATIONS
To understand the contribution of individual components in our method, we performed ablation studies on the 3D reconstruction components relating to the novel-view synthesis and on the geometric matching module used for multi-view scenarios.
Table 10 reports binary and multi-class IoU metrics with and without 3D stereo reconstruction.
Concretely, removing the 3D reconstruction component degrades mean IoU by 9.2 (27.5%). Per- class IoU drops are: Added 4.3 (17.2%), Removed 49.9 (46.9%), Moved 44.9 (64.5%), Replaced 1 6.1 (28.6%); Static also falls by 20.6 (22.3%). The largest absolute loss is in Removed, while the largest relative loss is in Moved, indicating that the 3D reconstruction and novel-view synthesis are especially critical for removals and partial relocations.
Figure analyzes robustness to I I, selection base the geometric matching module in the 3DGS CD dataset. Using lower-ranked reference images leads to a steady decline in Fl and loU, with reconstruction failure below the 20th percentile due insufficient correspondences.
Table demonstrates the impact of cross-view voting on multi-image change detection performance across five scenes in the 3DGS-CD dataset. Comparing the results, we observe substantial improve- ments in all Prriiiss-rrriiion... F1, and IoU-when cross-view voting is enabled. Notably, while recall remains near-perfect without voting due the method's high sensitivity (often reaching 100%), the lack of cross-view voting leads to significant drops in precision (e.g., 59.57% vs. 95.60% on Bench, 64.42% vs. 96. 66.79% on Desk), indicating many false positives. Incorporating cross-view voting dramatically reduces these errors, yielding Fl scores exceeding 96% and loU gains of over 30 points some cases. This demonstrates that our voting mechanism critical for filtering noisy or spurious detections and achieving high-precist precisso,,,,on, object-consistent predictions across views. See Appendix A.5 for detailed overview of the cross-view voting component.
Figure 10 illustrates the correlation between 3D reconstruction performance and multiclass detection metrics, evaluated on 1 1 of the ChangeSim dataset. As expected, fewer optimization steps result in higher mean loss and lower mloU. Notably, with just 10 optimization steps, only 5% of the full optimization process, nearly 90% of the final mloU performance retained. This highlights the robustness of our method to varying levels of 3D reconstruction quality.
17

---

Published as a conference paper at ICLR 2026
10
GT
Ours
Ours
Figure 5: Extended qualitative results of different change types (Added, Removed, Moved, Re- placed) detected by our method compared to ground truth (GT) in indoor and outdoor scenes. Our results provide color-coded labels even with binary GT and can exceed its accuracy, as annotations are often coarse or miss small changes. This, however, can lower our reported metrics as we compare to imperfect GT.
A.4 CONFLICT RESOLUTION VIA DEPTHI FILTERING
To eliminate geometric inconsistencies and occluders, we clean the pointmaps using a reverse depth test. Let P; denote the pointmap reconstructed from image I,, where each entry is 3D point in world coordinates. For every target view I,. we additionally have a depth map D, that records the camera-space value at each pixel, derived directly from the reconstruction process.
Given a world point p E Pi, we reproject it into the image plane of image L, to obtain the pixel coordinates (u, v) and its camera-space depth Zi=zzi
(u, U, 21+5) C5P
where Cj is the projection operator (intrinsics and extrinsics) mapping world-space points to image coordinates in view j.
Depth Conflict Resolution. We define the cleaned pointmap PClean C P; the subset of points from P, that not conflict with the geometry observed in the opposing view I,. Specifically, point p E P; is considered confticting with view if:
244j (u, v). where (w, v, 2)-5) Cjp
This condition indicates that would project in front of the surface observed by I,. and therefore represent occluder changed object.
18

---

Published as a conference paper at ICLR 2026
Io
Ours
Ours
Figure 6: Examples of the Warped change type detection by our method. We present results for changed objects such creased bedlinen, dented can, rug with a foot imprint, and book with turned page. Note that highly textured surfaces like the terrazzo tiles in the rug scene pose a challenge for our method, as seen the false positive masks detected.
To
Tj
Ours
Figure 7: Qualitative results on a scene from the 3RScan dataset. Although GOLDILOCS generates 2D change maps, we show that its output can be transformed into a 3D format, enabling broader applicability and evaluation additional datasets.
By removing geometrically conflicting points through reverse depth filtering, we obtain PClean and PClean that exclude points that are likely to correspond to objects that were added, removed, or moved between To and Ti. The union of these cleaned pointmaps, denoted P., is composed of the farthest visible surfaces of the scene and provides a consistent representation of the static scene geometry. A detailed example of this process is illustrated in Figure g
Rendering Pointmaps as Images. We denote by Ra,j the rendered image of pointmap P: from the viewpoint of I,. where each point p E P, is projected using Cj and colored using its appearance in image 7, For example, Ro,o denotes rendering with the geometry and viewpoint of To. an approximate reconstruction of the original image To. while Ro,1 synthesizes how the geometry captured in Lo would appear from the viewpoint of Z,, allowing cross-time and cross-view comparisons.
The joint cleaned pointmap P, is utilized as canonical reference geometry for downstream compar- isons. For example, to identify objects only visible Io, we render P. using Co, producing R2,0- Discrepancies between this rendering and the reference image Io indicate the presence of dynamic or changed elements. By analyzing such discrepancies in the image space, we can reliably localize and classify the types of changes the objects underwent.
19

---

Published as a conference paper at ICLR 2026
lo
11
1
Polenn via Cj polean via Cj
R.11
Figure 8: Depth filtering. Estimated depth maps are cleaned by reprojecting 3D points generated from each input image into the viewpoint the other camera (e.g., Rg.1, 1. the rendering of Po via C1) and identifying geometric conflicts. Points that project in front of corresponding surfaces in the opposite view (e.g., comparing I and Ro,1) are classified as conflicting and removed, yielding cleaned pointmaps, PClean and PC The joint cleaned pointmap P, retains only the farthest visible surfaces, corresponding to the persistent, static geometry of the scene. R,,1 the rendering of P. from C1 demonstrates that objects that experienced rigid changes are filtered out, while static yet non-rigidly deformed objects (e.g., the warped tablecloth) remain in the reconstruction.
A.5 MULTI-VIEW PIPELINE DETAILS
Pairwise Correspondence Maps. For each ordered pair (7i, Pf) with i j. we compute a per- pixel correspondence map that links each pixel in I to the pixel in I3 observing the same mutually visible 3D point. Let p E P: be the point that projects to (u, v) in C, and to (u', )') in Cj. Collecting all mappings yields a tensor Corr E zmxmx xx defined as:
(,,,,, = (w',v')
if the 3D point at pixel (u, v) E Ii projects inside 1ú (-1,-1) otherwise.
Intuitively, Corr is per-pixel cross-view map: enabling us to address the relevant pixel i.e. the same scene point-consistently across all target images.
Cross- View Voting. Let I be the per-pixel predicted labels for 1i. where each pixel labeled from the set L (Added, Removed, M oved, W araedd, Unchanged). Given a segment 8 in i whose label is / L. let P((s) E Pe be the of 3D points that projects of the pixels of mapped into i from Corrli, :. denoting the labels of this projected region by (s(--)). Each different view P E In then votes on the label of according to the per-pixel majority vote within (s(--;)):
We retrieve the coordinates
label,(s) 1,
labeljuy1)(s) = mf
If [ m, then the final label of the segment is given by cross-view majority:
label(s) mode (!labelz(s) 1,.., m)).
Ties are resolved by a fixed priority over L. This ensures that each segment casts exactly one vote per view towards the final prediction, leading to consistent labeling across all target images.
Finally, for completeness we include an overview of the multi-view pipeline used in our method (Figure 11). This schematic illustrates the data flow from unaligned multi-view inputs through 3D
20

---

Published as a conference paper at ICLR 2026
Table 10: Ablation results (IoU) on 10% of the ChangeSim dataset.
Binary
Multi-class
Method
Change
Static
mloU
Added
Removed
Moved
Replaced
Static
mloU
Ours w/o 3D reconstruction
20.7
71.9
46.3
20.7
11.2
2.7
15.2
71.9
24.3
Ours
37.5
92.5
65.0
25.0
21.1
7.6
21.3
92.5
33.5
Performance Change vs, Baseline (top-1) on 'Desk' scene from 3DGS-CD
% Change from baseline
20
-80
change loU change
Top-1
90%
60%
10%
60%
50%
40%
30%
20%
Image
Figure 9: Performance change in Fl and loU compared to the top-1 baseline on the Desk scene from the 3DGS-CD dataset. Below the 20th percentile, reconstruction fails due to insufficient matches.
reconstruction, correspondence filtering, and mask propagation, providing context for the datasets and results reported above.
A.6 PARAMETERS
Table 12 presents the values of the parameters used for SAM2 in the segmentation step, as well as the parameter utilized in the filtering step. Although the method exposes several tunable parameters, most settings are shared across all datasets and experiments; we chose values derived from our insights regarding dataset characteristics (image resolution, scene depth, typical pixel size of targets, and appearance ambiguity). Although we believe a thorough parameter search can further enhance results on specific data (or even specific examples), we refrained from doing this.
SAM2. The parameters control how the automatic mask generator samples prompts and filters candidate masks. Specifically, they determine (i) the spatial and multi-scale sampling used to propose masks, (ii) the quality thresholds that accept or reject proposals, and (iii) options that broaden or refine the set of candidates. points pointssppr___s per_ _er__ideeeeeeeeesspp_ppeppppeeee side sets the density of regular grid of point prompts over the image; larger values probe the scene more densely and typically improve recall at higher compute cost. If crop_n_layers 0, SAM2 also objects; crop_r_points_ hop__r_poits_ddowocaaleee runtime manageable. After proposing masks, two quality gates prune weak results: pred iou thresh keeps only masks whose predicted loU (the model's 's internal confidence in mask quality) exceeds the threshold, and stability suaility_score___ _score _thresh keeps masks that remain stable under small binarization perturbations. multi mask lockkouuuuttaaa returns multiple plausible masks per prompt, useful for instances where boundaries are ambiguous. use_ use_mmm is a SAM2 flag that enables mask-to-mast refinement, where previously predicted masks are used as prompts to refine final mask prediction.
amage-pyramid of crops better capture small reduces the grid density on deeper crop levels to keep
Filtering. To prevent false positives in occluded out-of-view regions, we apply visibility-aware filtering step to all tracked masks. For each predicted mask, we compute the fraction its area that lies inside Ro,1 and keep the mask only if this fraction is at least This suppresses detections that are supported mainly by pixels that are occluded or outside the shared field of view. Datasets with strong inter-view overlap and larger scene depth (e.g., ChangeSim) can higher a, since
21

---

Published as a conference paper at ICLR 2026
Performance And 3D Reconstruction Loss Optimization Steps On ChangeSim
0.40
33
0.35
32
Mean Loss
31
0.30
mio
0.25
0.20
-26
Optimization Steps
Figure 10: Relationship between 3D reconstruction loss and multiclass mloU function of optimization steps. A clear correlation observed: as mean loss decreases, performance (mloU) improves. Notably, nearly 90% of the final performance is achieved with just 5% (10 steps) of the total optimization process.
Table 11: Ablation results of the cross-view voting component on the 3DGS-CD dataset.
Dataset
Method
Precision
Recall
F11
IoUt
Ours w/o cross-view voting Ours
59.57 95.60
100.00
73.48
59.57
Bench
99.82
97.65
95.44
Ours w/o cross-view voting Ours
64.42
100.00
76.98
64.42
Desk
96.79
99.88
98.30
96.67
Ours w/o cross-view voting Ours
72.80
99.99
83.38
72.79
Mustard
96.24
99.80
97.93
96.06
Ours w/o cross-view voting Ours
66.97
97.19
77.90
66.42
Sill
98.10
96.99
96.51
95.23
Ours w/o cross-view voting Ours
79.93
95.13
86.56
78.06
Swap
98.72
94.35
96.20
93.12
most objects are mutually visible and less affected by occlusion or parallax. Datasets with sparser overlap and smaller scene depth (e.g., 3DGS-CD benefit from lower a, because objects may be only partially observed in each image and are more susceptible to occlusions and parallax; relaxing the filter avoids discarding such cases.
Parameter selection. We selected the SAM2 parameters per dataset based on four practical factors: the input image resolution, the expected scene depth (and resulting parallax/ocelusions)) the typical pixel scale of target objects, and their appearance ambiguity (e.g.. objects sharing color/texture with nearby surfaces). These considerations drove the balance between coverage and precision (grid/crops vs. thresholds) and when to allow multiple candidates per prompt.
For ChangeSim, we enable multi_ paa_mmaaskkuuuut use a dense grid (64) and one crop layer, and set moderate quality gates (pred_ion Le___oo__tie thesh denser sampling supports smaller targets at the given resolution, while the relaxed thresholds tolerate appearance ambiguity (e.g., box with nearly the same color its shelf), which would otherwise be over-pruned. 3DGS-CD and NeRFCD (indoor multi-view datasets) retain the same dense grid and single crop preserve recall their object scales, but raise both gates to 0.9/0.9 0.9 improve precision
itability waabiityyssooeeee.. sccorescre__eee www.settrrrehhh_te__te
0.8). The
22

---

Published as a conference paper at ICLR 2026
Top-1 selection
Top la per
Pairwise GOLDILOCS
Independent pred.
Mutt-view consistent predictions
IA
P
Corr tensor
Target views pixel
Cross-view voting
correspondence mapping
Figure 11: Overview of the multi-view pipeline.
Table 12: Parameters used for SAM2 and values used in GOLDILOCS's filtering step.
Fitering
SAME
Dasaset
prot_ Jou thrssh
subiiity scoce_ Bursh
milti musk cutpur
usc_m12
poins per Akd
Jayees
crop, poins poop.o_pins_dawezccleee Jowascale. fecoor
ChangeSim
0.5
0.8
0.3
True
Fales
64
3DGS CD
0.25
0.9
Tue
Falic
64
NeRFCD
0.25
0.9
Tiue
Falie
VL CMU CD
0,8
Filse
False
32
under clutter and depth-induced inconsistencies. Finally, VL-CMU-CD disables multi-mask output, uses lighter grid (32) and no crops, with mid-strict gates (0.8) (0.8)) in order to avoid detection of smaller objects not annotated by the creators of the dataset. summary, higher thresholds address precision when depth/clutter increase false positives; denser grids and crop layers recover small objects in higher-resolution settings; and enabling multi-mask helps when class/texture similarity makes boundaries ambiguous. See Table 13 for analysis of how different detection thresholds affect performance on the VL-CMU-CD dataset.
3D reconstruction. We selected MASt3R parameters that faciliate accurate reconstruction with an emphasis on rendering accurately aligned 2D images. In order to be able to infer on each pixel of the input data it was important avoid pointmap cleaning at the reconstruction phase as we aimed to retain all of the scene geometry in both time points, such that we can clean the pointmaps with our depth filtering logic. Below are parameters used for reconstruction:
1r1 0.07
niterl 500
1r2 0.014
niter2 200
optim_level= "coarse'
matching__cof__tir-0
shared_intrinssics False
desc_conf='3d'
scenegraph_type 'complete'
winsize=1
23

---

Published as a conference paper at ICLR 2026
Table 13: Analysis of SAM2 parameter settings VL-CCMU-ID and their impact binary change detection performance. Following prior work, the primary metric for the VL-CMU-CD dataset is Fl All other SAM2 and filtering parameters remain presented in Table 12
SAM2
Binary Motrics (96)
Dataset
pred_ jou, thresh
stability_scnme secrriyy__see__eeeeeeeeeeeeeeeee thresh
Flag
Flg
Flyg
Precig
Recally
Precig
Rocalby
VL-CMU-CD
0.7
78.3
59.5
96.9
53.6
79.4
98.2
96.1
V1-CMMU-CD
0.8
79.6
61.8
97.4
60.5
72.7
97.7
97.4
VL-CMU-CD
72.4
47.1
97.7
62.8
43.7
96.2
99.4
win_cyolic = False
refid=0
min min_conf_th:=aee 0.0
tol = 0.0
cam cam_size = 0.2
TSDF_thresh= = 0.0
as_pointcloudd:Tnee True
mask_sky=Fall False
=a False
clean_with_cooff False
transparent_cams False
A.7 LARGE LANGUAGE MODELS (LLMS) USAGE
In this paper we utilized LLMs as writing support tools, limited to rephrasing sentences, shortening paragraphs and refining wording in select instances. Note that LLMs were not used for ideation, literature discovery, paper summarization, nor the methodology development.
24


Reviews:
[{"review_id": "TodFqFwmxt", "reviewer": "Reviewer_KMk1", "summary": "The paper presents a new method for scene change detection. The new method, entitled GODILOCS, offers an alternative for zero-shot, class-agnostic object-level change detection, leveraging recent advances in foundation models. The method is 3D geometry aware, firstly by calculating the 3D geometry of the scene using Mast3R. The created point-maps are filtered for inconsistent regions (likely corresponding to changes in the scene) and then images are rendered using the same viewpoints such that the images can be compared. Discrepancies between the rendered and initial images help identify object-level changes, with the help of object-level masks created by SAM2. A foundation model for tracking is the afterwards deployed to differentiate between rigid and non-rigid changes. The presented method achieves state-of-the-art results compared with a set of relevant baselines both on synthetic and real-world dataset.", "questions": "Is the integration of Mast3R really necessary? Given that posed, calibrated images were used, using a simpler and more lightweight SfM method, wouldn’t the traditional render and compare lead to similar results? That would be an interesting experiment that would explore the trade-off between computational resource needed for the method and the success of the method.\n\nSince the proposed method reasons in 3D, why are its results not also evaluated in 3D? Using an appropriate dataset, e.g., 3RScan [1] and extending the method by back-projecting the highlighted changes into 3D would also give interesting insights on the success of the method in the 3D environment.\n\n[1] Wald, Johanna, et al. \"Rio: 3d object instance re-localization in changing indoor environments.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper addresses an interesting topic: scene change detection. Especially, it also helps sub-categorize the change into added, removed, or moved objects (rigid and non-rigid changes). Scene change detection is an understudied and interesting topic. \n\nThe paper leverages the latest trends in foundation models, offering a class-agnostic, zero-shot method for scene change detection. More specifically, using Mast3R, the proposed method creates a 3D reconstruction of the scene to have a 3D geometry-aware 3D change detection, and then uses a class-agnostic instance segmentation model (SAM2) to identify objects in the scene and track them using DEVA].\n\nThe paper achieves SoTA results, compared to other baselines, validating the motivation of having a 3D geometry aware change detection methods when just images of a scene are given.\n\nThe paper is well-motivated, and well-written. The methodology is thoroughly explained.", "weaknesses": "The novelty of the paper is limited. The paper is mainly extending the work of [1] that uses Mast3R towards creating a geometry aware-scene change detection method. Mast3r offers the 3D reconstruction of the scene, along with the poses and the calibration matrices of the views. Through that, the proposed method can then compare depth maps and render novel views, using the same viewpoints. On the rendered views, the methodologies applied in [1] are then deployed.\n\nMore baselines should be included, including CYWS-3D (Sachdeva & Zisserman, 2023b). Even though the paper offers bounding boxes as changing regions, SAM could be deployed to get the mask for the bounding boxes. Moreover, since the method does not categorize into the different types of changes, the authors could present comparative metrics on all changes and not specific categories.\n\nMost importantly, methods that reason in 3D such as (Taneja et al., 2011), [Adam et al., Objects can move: 3d change detection by geometric transformation consistency, ECCV 2022], [Palazzolo and Stachniss, Fast image-based geometric change detection given a\n3d model, ICRA 2018] are not included in the experimental evaluation and are mostly not discussed in the related work. Such methods could be easily adopted to the given use case by using Mast3r to obtain the 3D models they reason on, which could result in strong baselines. Right now, the presented baselines do not integrate any kind of knowledge about the 3D scene.\n\n[1] Kannan, Shyam Sundar, and Byung-Cheol Min. \"Zeroscd: Zero-shot street scene change detection.\" 2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pHt5o4j8EB", "reviewer": "Reviewer_rCUL", "summary": "The authors introduce GOLDILOCS, a framework that establishes a standardized taxonomy for object-level change labeling in 3D scenes. The approach is grounded in geometric reconstruction and visibility reasoning, where an object is defined as any visually identifiable entity ranging from volumetric elements like boxes or furniture to planar textures such as ink stains on paper.\n\nGOLDILOCS reconstructs the 3D geometry of a scene and employs novel-view rendering, segmentation, and temporal tracking of object masks to detect both rigid and  non-rigid transformations over time. The framework is evaluated across multiple datasets, demonstrating consistent and satisfactory improvements over state-of-the-art methods in object-level change detection and labeling.", "questions": "The proposed framework is computationally heavy, as it integrates conventional segmentation and 3D stereo reconstruction modules, making it less efficient for real-time or large-scale applications.\nThe 3D stereo reconstruction component appears to rely on existing methods with minimal innovation, reducing the novelty of that part of the pipeline.\nThe paper lacks clear technical details about the underlying 3D reconstruction model its architecture, parameters, and optimization strategy are not adequately discussed.\nThe motivation for selecting specific existing models for segmentation and reconstruction is not well justified. The rationale behind these design choices should have been elaborated to strengthen the methodological clarity.\nThe computational complexity, including FLOPs, memory requirements, and inference time, is not reported. Such analysis would be valuable for early reference and comparative evaluation.\nGiven its reliance on multiple integrated modules, the pipeline may face scalability and deployment challenges in dynamic or resource-limited environments.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper introduces a well-defined and systematic taxonomy for object-level change labeling in 3D scenes, addressing the lack of standardization in existing approaches.\n\nThe authors adopt an inclusive definition of object, covering both volumetric entities (e.g., furniture) and planar or texture-based elements (e.g., ink blotches), which enhances the framework’s generality and applicability.\n\nThe integration of 3D geometric reconstruction and visibility-based reasoning allows for more precise understanding of scene changes compared to purely 2D or image-based methods.\n\nThe use of segmentation and mask tracking across time ensures consistent identification of objects and their transformations, including non-rigid changes.\n\nThe pipeline’s inclusion of novel-view rendering improves the robustness of change detection under different viewpoints and occlusions.\n\nThe model has been tested on multiple datasets, demonstrating strong generalization and satisfactory improvements over state-of-the-art methods.\n\nBy combining geometry, visibility, and temporal reasoning, GOLDILOCS can be extended to practical domains like robotics, AR/VR scene updates, and autonomous navigation.", "weaknesses": "The proposed framework is computationally heavy, as it integrates conventional segmentation and 3D stereo reconstruction modules, making it less efficient for real-time or large-scale applications.\nThe 3D stereo reconstruction component appears to rely on existing methods with minimal innovation, reducing the novelty of that part of the pipeline.\nThe paper lacks clear technical details about the underlying 3D reconstruction model its architecture, parameters, and optimization strategy are not adequately discussed.\nThe motivation for selecting specific existing models for segmentation and reconstruction is not well justified. The rationale behind these design choices should have been elaborated to strengthen the methodological clarity.\nThe computational complexity, including FLOPs, memory requirements, and inference time, is not reported. Such analysis would be valuable for early reference and comparative evaluation.\nGiven its reliance on multiple integrated modules, the pipeline may face scalability and deployment challenges in dynamic or resource-limited environments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "098Jj66CH8", "reviewer": "Reviewer_x35Y", "summary": "The paper introduces GOLDILOCS, a zero-shot and pose-agnostic approach for object-level semantic change detection (SCD). The key idea is utilizing  3D geometric inconsistencies to detect changes. With the estimated pointmaps by dense stereo reconstruction models , the method generates reference images of clean point clouds. Changes are then identified via mask tracking for rigid objects and SSIM heatmaps for non-rigid ones. Evaluations on various datasets show better performance over baseline methods.", "questions": "- Is there analysis of how the performance/generality of 3D reconstruction model affect the result of SCD results? Are there any visualization of reconstructed results?\n- The “conflict resolution” in Sec. 4.2 is confusing, what does it mean?\n- Is the method end-to-end? Or they are rule-based after getting the segmentation/reconstruction by existing models.\n- Citation of 3DGS-CD of Tab.1 differs from that of Tab. 2.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- With the help of SAM2, the proposed method is zero-shot.\n- The idea to utilize SAM and MASt3R to SCD problem is practical.\n- The proposed method achieved best overall performance in most settings.", "weaknesses": "- The proposed method depends on two large foundation models, which demands large computations and may limit its applications.\n- Lack of implementation details/runtime comparisons to show the extra cost of the proposed pipeline.\n- The peromance improvement is marginal (e.g, resutls in Tab.4.)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dnjltU8wW0", "reviewer": "Reviewer_14W9", "summary": "This paper proposes GOLDILOCS, a zero-shot, 3D-aware framework for object-level scene change detection (SCD) from image pairs or multi-view image sets. The method reformulates SCD as a combination of stereo 3D reconstruction, semantic segmentation, mask tracking, and non-rigid change detection via SSIM-based comparison. It leverages foundational models (MASt3R for 3D reconstruction and SAM2 for segmentation) without additional training and introduces a standardized object-level change taxonomy including Removed, Added, Moved, and Warped. Experiments on both synthetic and real datasets (ChangeSim, VL-CMU-CD, 3DGS-CD, NeRFCD) demonstrate strong zero-shot performance, especially in IoU and F1 metrics, while avoiding expensive multi-view reconstruction at test time.", "questions": "1. How sensitive is GOLDILOCS to SSIM thresholds and mask tracking parameters across datasets?\n\n2. How does it handle partially occluded or highly dynamic objects?\n\n3. Could hierarchical segmentation improve detection for nested objects?\n\n4. Would longer temporal sequences improve detection robustness?\n\n5. Any plans for evaluation in real-world dynamic scenes?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Zero-shot object-level change detection with 3D geometric reasoning.\n\n2. Modular pipeline leveraging stereo reconstruction and segmentation models.\n\n3. Introduces a clear object-level change taxonomy (Removed, Added, Moved, Warped).\n\n4. Faster inference compared to traditional 3D reconstruction pipelines.\n\n5. Comprehensive experiments across multiple datasets.", "weaknesses": "Ⅰ. Parameter Sensitivity:\n\nSSIM Thresholds: The thresholds used for non-rigid change detection significantly affect results. A threshold set too high may miss subtle deformations, while a threshold too low may falsely classify static objects as changed.\n\nLabel Priority Ordering: The per-pixel prediction relies on the priority order (Warped > Moved > Removed > Added). This can affect nested or overlapping object changes; for example, small objects inside a larger moved object may be suppressed and misclassified.\n\nⅡ. Limited Ablation:\n\nAblation experiments mainly focus on stereo reconstruction and novel view synthesis, but the contribution of non-rigid change detection and mask propagation is not thoroughly analyzed.\n\nThe effects of individual components such as depth filtering, cross-view voting, and mask propagation on each change type (added, removed, moved, warped) are not quantified.\n\nⅢ. Data Limitations:\n\nThe evaluation datasets lack extreme real-world scenarios, including large occlusions, fast-moving objects, or highly dynamic scenes, which may cause 3D reconstruction or mask tracking failures.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)