MVDG: Efficient Multi-view 3D Disambiguation on Unconstrained Real-World Images

Hanyuan Xiao; Gonglin Chen; Haolin Xiong; Wenbin Teng; Haiwei Chen; Yajie Zhao

cs.CVOct 1, 2026

MVDG: Efficient Multi-view 3D Disambiguation on Unconstrained Real-World Images

Authors: Hanyuan Xiao, Gonglin Chen, Haolin Xiong, Wenbin Teng, Haiwei Chen, Yajie Zhao

Organizations: University of Southern California · Institute for Creative Technologies

Abstract

Illusory matches between distinct yet visually similar 3D surfaces--doppelgangers--remain a fundamental obstacle for large-scale, in-the-wild 3D reconstruction and visual localization. Prior work mitigates this issue with pairwise classifiers, but this design limits multi-view contextual reasoning and incurs O(n^2) inference complexity for downstream structure-from-motion (SfM). We present MVDG, a scalable multi-view disambiguation framework built on the 3D foundation model VGGT, which jointly reasons over an arbitrary number of multiview images. By incorporating 3D-aware multi-view features, our method reduces dependence on pairwise comparisons by encoding and decoding views in a single pass. We further observe that direct multi-view fine-tuning of VGGT can be unstable under noisy supervision; motivated by label ambiguity in Doppelgangers, we construct a pseudo-pairwise training set from AerialMegaDepth and show that fine-tuning on sampled subsets yields stable optimization and strong generalization to held-out scenes. Finally, because full SfM evaluation (even with faster pipelines such as GLOMAP) remains expensive, we process a pseudo-pairwise dataset for efficient validation; we derive a predictive relationship between regular SfM metrics and the classification accuracy on this pseudo-pairwise test. Experiments show that our method achieves comparable pairwise accuracy while improving both SfM accuracy and inference speed over baselines.

Figures & tables

Figure 1 : Overview of MVDG for multi-view 3D disambiguation. MVDGclassifies image pairs with similar 2D appearance that may correspond to different viewpoints. We train a classification decoder and a confidence module to select effective encoder depths adaptively. By exiting at early layers when confidence is high, our method improves efficiency while maintaining accuracy. ( Left ) Red -boxed examples are false pairs (different scene/view), and green -boxed examples are true pairs (same scene/view). ( Right ) We show a comparison of structure-from-motion result without vs. with MVDG to remove visually ambiguous images.

Figure 2 : Ambiguous inputs can mislead 3D scene understanding. We feed VGGT [ 19 ] with multi-view images from a VisymScenes scene [ 22 ] containing repetitive patterns. Among the eight input images, two viewpoint groups are evident to human observers (all from the same physical scene): six red -marked images and two orange -marked images. ( Left ) When all views, including transition geometry, are provided, the backbone robustly disambiguates views and reconstructs a coherent scene. ( Right ) When only two views from different viewpoints are provided, VGGT fails to disambiguate and reconstructs an incorrect joint scene.

Figure 3 : Architecture of the proposed pipeline. Given a reference image ( $I_{r}$ ) and database images ( $I_{1},I_{2}$ ), our method encodes all inputs and extracts geometry-aware tokens to classify whether each database image corresponds to the same scene and viewpoint as the reference. We freeze the pre-trained VGGT encoder and train a DPT-style decoder to obtain per-patch features, which are then average pooled into global descriptors. Positionally encoded descriptors are fed to an MLP-based doppelgangers classifier. To accelerate inference, a confidence-based early-exit module halts unnecessary deeper-layer computation. LN denotes LayerNorm.

	Accuracy $\uparrow$		Precision $\uparrow$		Recall $\uparrow$		$\mathcal{F}_{1}\uparrow$		Inference time (msec) $\downarrow$
	VisymScenes	DG++	VisymScenes	DG++	VisymScenes	DG++	VisymScenes	DG++
SALAD	0.8104	0.6233	0.7695	0.5894	0.8862	0.8128	0.8237	0.6833	9.87
Doppelgangers++	0.9355	0.937	0.9095	0.970	0.9673	0.901	0.9375	0.934	165
Ours	0.9441	0.889	0.9215	0.877	0.9712	0.903	0.9457	0.890	103.92

Table 1 : Comparison of image disambiguation methods on pairwise test datasets.

Figure 4 : Qualitative comparison on Doppelgangers and VisymScenes [ 1 , 22 ] . The predicted classification probabilities of ours and Doppelgangers++ are thresholded at $0.5$ . Because SALAD [ 5 ] outputs static global descriptors, SALAD* denotes cosine similarity thresholded at $0.3171$ , which gives the best $\mathcal{F}_{1}$ score (Sec. 5.3 ). The legend shown in the first top-left negative example applies to all examples; each tile follows the same annotation scheme. Red -boxed examples are false pairs (different scene/view), and green -boxed examples are true pairs (same scene/view).

Table 6

Panel of Figure 5 — Figure 5 : Correlation between pseudo-pairwise accuracy and downstream SfM metrics.

Figure 6 : Qualitative results on SfM using AerialMegaDepth . For visualization, we clip the point cloud to $[-150,150]^{2}$ on the ground plane to focus on the scene center; this removes some points and cameras.

	AUC@3		AUC@5		AUC@10		Inlier ratio
	DG++	Ours	DG++	Ours	DG++	Ours	DG++	Ours
Big Ben	0.326	0.339	0.455	0.537	0.511	0.747	252/748	256/730
Louvre Pyramid	0.830	0.852	0.886	0.896	0.928	0.930	207/296	227/297
Phatheon Paris	0.912	0.910	0.929	0.929	0.944	0.942	491/537	493/536
Ponte Di Rialto	0.068	0.442	0.246	0.466	0.438	0.483	161/329	173/369
Reichstag Building	0.720	0.723	0.832	0.832	0.916	0.914	163/222	161/221

Table 4 : Quantitative SfM evaluation on AerialMegaDepth .

Figure 7 : Qualitative results on the Doppelgangers dataset [ 1 ] . Classification scores are thresholded at $0.5$ : scores $<0.5$ are classified as false pairs, and scores $\geq 0.5$ as true pairs.

Figure 8 : More SfM testing on AerialMegaDepth scenes.

Figure 9 : Heatmaps of encoder-layer attention. We average output patch tokens at each encoder layer and upsample them via bilinear interpolation. The heatmaps show similar attention patterns across $l_{0},\ldots,l_{11}$ .

Used Layers	$[4]$	$[4,11]$	$[4,11,17]$	$[4,11,17,23]$
Training Accuracy	0.9547	0.8929	0.9846	0.8904
Test Accuracy	0.5245	0.6570	0.7028	0.7598

Table 5 : Ablation study on encoder layer selection for classification accuracy.

	AUC@3	AUC@5	AUC@10	Inlier ratio	Inference speed
DPT	0.640	0.723	0.801	0.7369	3.38 sec/it
Encoder	0.643	0.726	0.802	0.7360	3.10 sec/it

Table 6 : DPT-style decoder vs. transformer encoder (averaged on AerialMegaDepth ).

Figure 10 : Frequency of earliest layers meeting the confidence threshold. Statistics are computed on the VisymScenes test set [ 22 ] .

Figure 11 : Fine-tuning with our pseudo-pairwise dataset vs. skipping, on standard SfM metrics.

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XDG: Accelerated Visual Disambiguation

Gonglin Chen, Ben Southall, Hanyuan Xiao, Wenbin Teng+7

1USC Institute for Creative Technologies · University of Southern California · 3SRI International

Structure-From-Motion Perceptual Aliasing Duality Disambiguation

Open paper AskPDF arXiv

Visual aliasing, also known as the doppelganger problem, remains a key challenge for structure-from-motion (SfM): visually similar but physically distinct surfaces can produce incorrect image matches and degrade reconstruction quality. Previous work mitigates this issue with geometry-aware foundation-model features, but places a heavy transformer classifier on top of the backbone, making large-scale disambiguation expensive. We introduce XDG, an efficient visual disambiguation model designed for scalable SfM. Our key observation is that a 3D foundation model already performs the cross-view geometric reasoning necessary for visual disambiguation, so doppelganger classification should adapt the backbone representation directly rather than relearn pair reasoning in a separate heavy decoder. XDG fine-tunes Depth Anything 3 with lightweight LoRA adapters and repurposes its camera tokens as compact pair-level classification tokens. A compact MLP head predicts whether a candidate image pair observes the same 3D surface. Extensive experiments show that XDG provides a favorable accuracy-efficiency tradeoff: it remains competitive with the state-of-the-art disambiguation method across pairwise and reconstruction benchmarks and delivers more than a 3x inference speedup. On individual LaMAR scenes containing thousands of images, XDG saves more than 10 hours of visual disambiguation processing. Code is available at https://github.com/xtcpete/xdg.

Abstract

Explore similar work

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