DDMS: Discriminative Distillation of Multi-view Foundational Features into Single-view Models

Jeong-gi Kwak; Sho Kagami; Yuki Ono; Kwang Moo Yi

cs.CVAug 24, 2026

DDMS: Discriminative Distillation of Multi-view Foundational Features into Single-view Models

Authors: Jeong-gi Kwak, Sho Kagami, Yuki Ono, Kwang Moo Yi

Organizations: University of British Columbia · Sony Semiconductor Solutions Corporation · Sony Corporation

Abstract

Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-view feed-forward geometry estimators. In this work, we demonstrate that by re-distilling these multi-view models---their internal knowledge of 3D geometry---into a single-view estimator, we can obtain enhanced 3D consistent foundational features. Our key idea is to construct a multi-view teacher by fusing pretrained 2D foundation features with multi-view geometric features, and refining the fused representation with a discriminative ranking objective. Through our discriminative distillation framework, we enforce the learned features to be both 3D consistent and locally distinctive, while keeping them aligned with the feature space of the original foundation model to preserve the semantic structure of the pretrained representation. Consistency and local discriminability are critical for 3D computer vision problems such as forming semantic and geometric correspondences across images. To demonstrate the effectiveness of our method, we perform comprehensive experiments spanning multiple angles: direct feature analysis, dense prediction transfer, and explicit 3D lifting and rendering. Across these evaluations, our method consistently produces stronger 3D-aware foundation features that improve multi-view consistency and local discriminability while preserving the semantic transferability of the original representation.

Figures & tables

Figure 1: Our 3D-aware distillation turns pretrained 2D foundation features into representations that are more consistent across views while retaining their semantic utility. The resulting features produce cleaner cross-view correspondences and remain coherent after explicit 3D feature splatting (left). Across 3D-aware finetuning and distillation baselines [ 69 , 68 , 50 ] built from the same 2D foundation backbone [ 40 ] , our representation provides a more balanced profile over geometric correspondence, semantic transfer, dense prediction, and 3D lifting evaluations (right). The radar chart axes use per-metric min–max normalization with the minimum set to 50%.

Figure 2: Overview of our method. We fuse pretrained 2D foundation features [ 40 ] with frozen multi-view geometry features [ 30 ] to construct a multi-view teacher. We refine the teacher with geometry-supervised ranking and feature-space anchoring, encouraging valid 3D correspondences to rank above incompatible candidates while preserving the original semantic feature space. We then distill it into a single-view student, which is used as the final feature extractor at inference time.

		Separation	Geometric correspondence				Semantic matching
	Method	ScanNet [ 10 ]	ScanNet [ 10 ]		Navi-W [ 24 ]		DAVIS [ 42 ]	SPair-71k [ 37 ]
		Margin $\uparrow$	@10px $\uparrow$	@20px $\uparrow$	@0.05 $\uparrow$	@0.1 $\uparrow$	J&F $\uparrow$	@0.05 $\uparrow$	@0.1 $\uparrow$
ViT-B	DINOv2 (baseline)	0.4108	21.72	36.11	57.97	75.94	67.82	52.98	68.56
	Fit3D (ECCV’24)	0.3494	22.89	39.50	31.26	53.81	66.70	30.25	44.86
	MEF (ICLR’25)	0.4288	39.17	55.06	67.71	80.87	66.82	56.78	72.07
	SnD (ICLR’26)	0.4520	28.65	45.67	59.53	76.86	68.05	53.68	69.65

Table 1: Direct feature evaluation compared with recent 3D-aware feature finetuning methods [ 69 , 68 , 50 ] . Benchmarks cover ScanNet [ 10 ] feature separation and geometric correspondence, Navi-Wild [ 24 ] object correspondence, SPair-71k [ 37 ] semantic keypoint matching, and DAVIS [ 42 ] mask propagation. Best and second-best results are highlighted within each backbone group. Our method achieves the best separation, outperforms existing methods by a large margin on ScanNet, and performs strongly in all cases.

Figure 3: Qualitative nearest-neighbor correspondence results on ScanNet [ 10 ] and Navi-Wild [ 24 ] . Green and red lines denote correct and incorrect matches, respectively. Our features produce cleaner correspondences with fewer mismatches in these examples.

Figure 4: Viewpoint-gap analysis for ScanNet [ 10 ] correspondence (ViT-B, PCK@10px and PCK@20px). Our method remains robust as viewpoint changes increase.

Figure 5: Qualitative comparison of semantic segmentation using frozen features with linear probing.

		Semantic segmentation						Depth estimation
		ScanNet [ 10 ]			ScanNet++ [ 67 ]			ScanNet [ 10 ]			NYUv2 [ 52 ]
	Method	mIoU $\uparrow$	mAcc $\uparrow$	aAcc $\uparrow$	mIoU $\uparrow$	mAcc $\uparrow$	aAcc $\uparrow$	AbsRel $\downarrow$	RMSE $\downarrow$	$\delta_{1}\uparrow$	AbsRel $\downarrow$	RMSE $\downarrow$	$\delta_{1}\uparrow$
ViT-B	DINOv2 [ 40 ]	53.69	66.29	77.63	31.69	41.41	81.47	0.1553	0.3718	78.12	0.1705	0.6566	74.90
	Fit3D [ 69 ]	51.35	63.37	76.96	30.58	38.31	84.06	0.2045	0.4599	66.70	0.1975	0.7426	68.03
	MEF [ 68 ]	52.49	65.17	76.93	29.40	38.62	81.43	0.1677	0.3937	75.06	0.1661	0.6222	75.98
	SnD [ 50 ]	56.36	69.40	79.43	31.95	41.67	82.62	0.1449	0.3446	80.89	0.1537	0.6018	78.56

Table 2: Linear probing comparisons. We evaluate semantic segmentation on ScanNet [ 10 ] and ScanNet++ [ 67 ] , and depth estimation on ScanNet [ 10 ] and NYUv2 [ 52 ] . Our method is best or second-best on every segmentation metric and remains competitive with the SnD variants [ 50 ] on depth, while outperforming both variants on ScanNet geometric correspondence in Table 1 .

Figure 6: Qualitative comparison of depth estimation using frozen features with linear probing. Our method provides coherent depth estimates.

Figure 7: Qualitative examples of feature uplifting and rendering on pretrained 3D Gaussians [ 4 ] . PCA-projected 2D features are compared with rendered features using the same uplifting method [ 36 ] . Our features remain more coherent across views and after rendering.

Method	mIoU $\uparrow$	mAcc $\uparrow$	aAcc $\uparrow$	Per-view mIoU $\uparrow$	Multi-view agree. $\uparrow$
DINOv2 [ 40 ]	62.30	74.30	82.69	54.49	77.34
Fit3D [ 69 ]	59.54	71.04	81.45	54.21	80.73
MEF [ 68 ]	61.54	73.73	82.24	54.07	77.71
SnD [ 50 ]	62.79	74.57	82.99	56.51	80.28
SnD [ 50 ] (+blending)	63.26	75.18	83.25	57.14	80.80
Ours	63.32	74.85	83.36	57.79	82.49

Table 3: 3D point segmentation on ScanNet [ 10 ] with a linear probe on features aggregated over views. Per-view mIoU applies the same probe to the feature of each view without averaging, and multi-view agreement is the rate at which two views of the same point predict the same class. Ours matches the strongest baseline after aggregation and is best per view, with the most consistent predictions across views.

Figure 8: Feature consistency between image features and rendered features, measured as nearest-neighbor matching accuracy (PCK@10px) when matching across them. Our method retains the highest accuracy, demonstrating consistency.

Variant	Sep. $\uparrow$	Corr. $\uparrow$	Sem. $\uparrow$	Seg. $\uparrow$
DINOv2 (baseline)	0.4108	21.72	68.56	53.69
Direct FT: $\mathcal{L}_{\mathrm{disc}}$	0.5294	67.14	57.36	31.44
Direct FT: $\mathcal{L}_{\mathrm{disc}}+\mathcal{L}_{\mathrm{anchor}}$	0.4540	63.19	71.70	54.80
w/o multi-view context	0.4535	62.23	70.51	56.71
w/o teacher $\mathcal{L}_{\mathrm{anchor}}$	0.4619	65.14	73.45	54.93
w/o student $\mathcal{L}_{\mathrm{disc}}$	0.4350	44.88	72.51	56.54

Table 4: Ablation study. Sep./Corr. denote ScanNet feature separation and correspondence PCK@10px; Sem./Seg. denote SPair-71k semantic correspondence PCK@0.1 and ScanNet segmentation mIoU.

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: Distribution of per-query feature separation margins on the ScanNet [ 10 ] test pairs. Vertical lines mark the mean of each method.

Method	Mean	Std.
DINOv2 [ 40 ]	0.4108	0.1935
Fit3D [ 69 ]	0.3494	0.1276
SnD [ 50 ]	0.4520	0.1388
SnD [ 50 ] (+blending)	0.3526	0.1233
MEF [ 68 ]	0.4288	0.1709
Ours	0.4632	0.1226

Appendix

Table 5: Mean and standard deviation of the per-query feature separation margins on the ScanNet test pairs.

Block	Stage	Sep. $\uparrow$	Corr. $\uparrow$
13	Early	0.3302	27.94
19	Middle	0.3682	37.03
39	Final	0.0860	13.54

Appendix

Table 6: DA3 feature layer analysis. Frozen DA3-Giant features from early, middle, and final encoder blocks, evaluated directly on ScanNet feature separation and correspondence (PCK@10px).

Model	Scale	Params	Dim.	Mode	Sep. $\uparrow$	Corr. $\uparrow$	Sem. $\uparrow$	Seg. $\uparrow$
VGGT [ 60 ]	ViT-L + Agg.	1.26B	2048	Multi	0.3096	36.98	65.89	53.26
DA3 [ 30 ]	ViT-G	1.14B	1536	Multi	0.3682	37.03	48.74	39.90
DUNE [ 46 ]	ViT-B	86M	768	Single	0.1846	33.10	59.52	47.76
Ours	ViT-B	92M	768	Single	0.4632	66.01	75.46	57.00

Appendix

Table 7: Evaluation of off-the-shelf 3D-aware foundation features. All features are evaluated frozen; Mode indicates multi-view (Multi) or single-view (Single) inference.

Feature	ScanNet	7Scenes	ETH3D
DINOv2 [ 40 ]	15.1 / 26.9	76.1 / 90.3	16.7 / 36.5
Fit3D [ 69 ]	19.0 / 38.0	77.3 / 92.9	16.0 / 38.8
MEF [ 68 ]	30.7 / 48.1	88.3 / 98.4	26.2 / 54.0
SnD [ 50 ]	20.6 / 36.9	81.1 / 92.3	15.2 / 36.9
SnD [ 50 ] (+blending)	18.8 / 34.1	77.0 / 93.0	15.2 / 38.8
Ours	65.9 / 85.4	95.4 / 100.0	49.0 / 80.6

Appendix

Table 8: Downstream camera pose estimation with PnP-RANSAC. Each entry reports pose recall R@10 / R@25 (%), within

10

cm/

10^{\circ}

and

25

cm/

10^{\circ}

Backbone	Model	Sep. $\uparrow$	Corr. $\uparrow$	Sem. $\uparrow$	Seg. $\uparrow$
ViT-L	DINOv2-L-reg	0.4315	23.68	67.83	55.84
ViT-L	+ Ours	0.5413	61.39	77.75	57.91
ViT-B	DINOv2-B-reg	0.4167	26.61	68.21	54.67
ViT-B	+ Ours	0.5127	60.74	75.72	55.72
ViT-B	DINOv3-B	0.3251	30.26	69.93	57.10
ViT-B	+ Ours	0.4455	64.76	73.85	56.92

Appendix

Table 9: Generalization across foundation backbones. Each backbone is compared with DDMS trained on top of it (+ Ours), using the metrics of Table 7 .

	ADE-20K [ 70 ]			Pascal-VOC [ 16 ]			KITTI [ 18 ]
Model	mIoU $\uparrow$	mAcc $\uparrow$	aAcc. $\uparrow$	mIoU $\uparrow$	mAcc $\uparrow$	aAcc. $\uparrow$	RMSE $\downarrow$	AbsRel $\downarrow$	$\delta_{1}$ $\uparrow$
DINOv2	44.29	55.45	79.67	81.30	87.52	95.85	4.0027	0.1228	85.90
Ours	47.14	57.81	82.15	84.97	90.16	96.82	3.7735	0.1207	86.17

Appendix

Table 10: Out-of-distribution dense prediction transfer. We evaluate frozen-feature linear probes on ADE-20K [ 70 ] and Pascal-VOC [ 16 ] for semantic segmentation, and on KITTI [ 18 ] for depth estimation.

Figure 10: Qualitative out-of-distribution transfer with frozen-feature linear probes: semantic segmentation on ADE20K [ 70 ] and Pascal VOC [ 16 ] , and depth estimation on KITTI [ 18 ] .

Figure 11: Additional nearest-neighbor correspondence results on ScanNet [ 10 ] , compared with all baselines. Green and red lines indicate correct and incorrect matches.

Figure 12: Additional correspondence results on Navi-Wild [ 24 ] , where the same object appears under different backgrounds and viewpoints. DDMS is compared with DINOv2.

Figure 13: Additional semantic keypoint matching results on SPair-71k [ 37 ] , where keypoints are matched across different instances of the same category.

Figure 14: Additional semantic segmentation results from frozen-feature linear probing. All methods use the same linear probe.

Figure 15: Additional depth estimation results from frozen-feature linear probing, comparing DDMS with DINOv2.

Figure 16: Additional 3D point segmentation results on ScanNet [ 10 ] . Features are aggregated from posed RGB-D views onto the point cloud and classified with a linear probe.

Figure 17: Additional feature splatting results on pretrained 3D Gaussian scenes. For each method, the original 2D features are shown next to the features rendered from held-out views after uplifting.

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Multi-View Foundation Models

Leo Segre, Or Hirschorn, Shai Avidan

Tel Aviv University

3D Foundation Models Multi-View Amodal Segmentation Foundation Model Feature Learning

Open paper AskPDF arXiv

Foundation models are vital tools in various Computer Vision applications. They take as input a single RGB image and output a deep feature representation that is useful for various applications. However, in case we have multiple views of the same 3D scene, they operate on each image independently and do not always produce consistent features for the same 3D point. We propose a way to convert a Foundation Model into a Multi-View Foundation Model. Such a model takes as input a set of images and outputs a feature map for each image such that the features of corresponding points are as consistent as possible. This approach bypasses the need to build a consistent 3D model of the features and allows direct manipulation in the image space. Specifically, we show how to augment Transformers-based foundation models (i.e., DINO, SAM, CLIP) with intermediate 3D-aware attention layers that help match features across different views. As leading examples, we show surface normal estimation and multi-view segmentation tasks. Quantitative experiments show that our method improves feature matching considerably compared to current foundation models.

Abstract

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