Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation
Authors: David Shavin
Sagie Benaim
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Aware Feature Distillation
Visual Foundation Models
3D Gaussian Splatting
Feed-Forward 3D Reconstruction
DINOv2
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: Splat and Distill (SnD) is a distillation framework that augments the teacher network with a feed-forward 3D reconstruction pipeline during training, resulting in 2D features with state-of-the-art 3D awareness

Abstract: Vision Foundation Models (VFMs) have achieved remarkable success when applied to various downstream 2D tasks. Despite their effectiveness, they often exhibit a critical lack of 3D awareness. To this end, we introduce Splat and Distill, a framework that instills robust 3D awareness into 2D VFMs by augmenting the teacher model with a fast, feed-forward 3D reconstruction pipeline. Given 2D features produced by a teacher model, our method first lifts these features into an explicit 3D Gaussian representation, in a feedforward manner. These 3D features are then "splatted" onto novel viewpoints, producing a set of novel 2D feature maps used to supervise the student model, "distilling" geometrically grounded knowledge. By replacing slow per-scene optimization of prior work with our feed-forward lifting approach, our framework avoids feature-averaging artifacts, creating a dynamic learning process where the teacher’s consistency improves alongside that of the student. We conduct a comprehensive evaluation on a suite of downstream tasks, including monocular depth estimation, surface normal estimation, multi-view correspondence, and semantic segmentation. Our method significantly outperforms prior works, not only achieving substantial gains in 3D awareness but also enhancing the underlying semantic richness of 2D features. Our project page and code are available [here](https://davidshavin4.github.io/Splat-and-Distill/)

Published as a conference paper at ICLR 2026
SPLAT AND DISTILL: AUGMENTING TEACHERS WITH FEED-FORWARD 3D RECONSTRUCTION FOR 3D-
AWARE DISTILLATION
David Shavin & Sagie Benaim
Department of Computer Science
The Hebrew University of Jerusalem
Jerusalem, Israel (david. shavin, sagie. (eteeeeenmmmmmmmmaeee hamnaammmmmmi........... ac..jiacc.....
ABSTRACT
Vision Foundation Models (VFMs) have achieved remarkable success when ap- plied to various downstream 2D tasks. Despite their effectiveness, they often exhibit a critical lack 3D awareness. To this end, we introduce Splat and Distill, a framework that instills robust 3D awareness into 2D VFMs by aug- menting the teacher model with fast, feed-forward 3D reconstruction pipeline. Given 2D features produced by teacher model, our method first lifts these features into an explicit 3D Gaussian representation,
a feedforward manner. These 3D features are then "splatted" onto novel viewpoints, producing set novel 2D feature maps used to supervise the student model, "distilling" ge- ometrically grounded knowledge. By replacing slow per-scene optimization of prior work with our feed-forward lifting approach, our framework avoids feature- averaging artifacts, creating dynamic learning process where the teacher's con- sistency improves alongside that of the student. We conduct comprehensive evaluation on suite of downstream tasks, including monocular depth estima- tion, surface normal estimation, multi-view correspondence, and semantic seg- mentation. Our method significantly outperforms prior works, not only achiev- ing substantial gains in 3D awareness but also enhancing the underlying se- mantic richness of features. Our project page and code are available https:
gi hub. io/ Sp aa-aatt-daaa and-Distill/.
INTRODUCTION
Vision Foundation Models (VFMs) such as DINO Caron et al. (2021) and DINOv2 Oquab et al. (2023) have achieved remarkable success by leveraging vast unlabeled 2D datasets via student- teacher self-distillation paradigm, yielding robust and generalizable features. These features enable state-of-the-art results across a diverse array of downstream tasks such semantic segmentation. Despite these advances, the capabilities of VFMs remain limited for 3D-aware tasks, like depth estimation, surface-normal reconstruction, and feature correspondence. Our work, therefore, aims to enhance the 3D awareness of such vision foundation models.
While several works focus on distilling 2D features into 3D representations, FiT3D Yue et al. (2024) takes the opposite approach: instilling 3D awareness into 2D VFMs by first lifting inconsistent 2D features into explicit 3D representations via per-scene optimization, then rendering views to create a dataset of "consistent" 2D features for fine-tuning. This method is fundamentally limited, as input features from different views are inconsistent El Banani et al. (2024), resulting least- squares compromise across views. You al. (2024) uses different approach, enforcing multi- view feature consistency through correspondences, bypassing explicit reconstruction. While this improves correspondence understanding, its supervision relies enforcing feature similarity at corresponding points, which is insufficient for instilling the dense geometric understanding needed for complex downstream tasks.

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Published as a conference paper at ICLR 2026
<Norm NYUv2
Depth NYOV2
Image
PCA
Seg.
Depth
sNorm
Cores:
Seg. ScanNot
Depth ScanNet
Depth
DINOv2
SchNNet
Seg.
Splat and Distill (Ours)
NYUV2
Coress
60-180
Correc
0-15"
Carres: 15:30
Coores.
30-60'
Figure 1: Splat and Distill (SnD) is a student-teacher distillation framework that augments the teacher with feed-forward 3D reconstruction pipeline during training, resulting in 3D-aware 2D features. Left: Leveraging our approach on DINOv2, results in 2D features that enable state-of-the- art performance on downstream tasks such as monocular depth estimation (Depth), surface normal estimation (sNorm), semantic segmentation (Seg), and multiview correspondence (Corres), Shown here is comparison of SnD (our method) to vanilla DINOv2, and tta approaches for improving 3D awareness, (Yue et al., 2024), and MEF (You et al., 2024), based on DINOv2 VIT-Small model, and considering the NYUv2 Silberman et al. (2012), ScanNet Dai al. (2017) and ScanNet++ Yeshwanth et al. (2023) datasets (see further results in Sec. 4). For visualization. we provide normalized scores (min-mat max per metric, weakest baseline set to 0), using inverse RMSE for depth and normal estimation, loU for segmentation, and Recall for correspondence (higher is better). See additional details in Sec. 4. Right: Visualization of our method compared to DINOv2.
To this end, we propose a fast, scalable alternative that avoids the inefficiencies and inconsisten- cies of optimization-bant understanding. Our key insight is that 3D consistency can be enforced by directly augmenting the teacher's s architecture in student-teacher paradigm. We initialize both the student and teacher mod- els using VFM that we aim to enhance with improved 3D awareness, along with a pre-trained 3D feed-forward reconstruction model, specifically DINOv2 for the former and MVSplat Chen et al. (2024) for the latter. We reconstruct scene appearance from a few context views, and lift seman- ties into it by extracting 2D feature maps from the context views using the teacher, upscaling them with segmentation masks, and attaching them to 3D via pixel-to-Ganee Gaussian correspondences. This allows efficient lifting of 2D features into 3D by attaching each feature to its corresponding Gaus- sian, avoiding slow per-scene optimization as in (Yue et al., 2024). We render features from the 3D scene novel viewpoints and blend them with semantic masks, producing 2D feature maps as supervision for the student model. The student extracts features from these target views and learns to match the teacher's rendered augmented feature maps via gradient descent. The teacher and the student share the same architecture; the teacher's 's weights are updated using the exponential moving average (EMA) of the student's parameters, following the distillation objective of DINOv2.
pipelines Yue et al. (2024), enabling complete, dense geometric scene
This design confers numerous advantages over previous work. First, by iteratively adapting the features fed into the 3D reconstruction model via EMA, our method avoids the static "averaging" of inconsistent features that occurs in optimization based approaches. Second, our framework learns a generalizable model for enforcing 3D consistency from a multitude of diverse scenes. Finally, by replacing this costly optimization with feed-forward lifting mechanism, our method is significantly faster, more efficient, and more scalable, using much fewer Gaussians than previous work.
To demonstrate the efficacy of our approach, conduct comprehensive evaluation suite of downstream tasks. Following established protocols, we probe for 3D awareness through monoc- ular depth estimation and surface normal prediction, which measures 3D awareness via single image, as well as zero-shot multi-view feature correspondence to measure multi-view consistency. To ensure that these geometric gains do come the cost of semantic richness, we also evaluate performance on semantic segmentation. Our method significantly improves on the entire suite of tasks in comparison to state-of-the-ar baselines, enabling enhanced single-view and multi-view 3D consistency well as greater semantic richness. An illustration is provided in Fig. 1.
2 RELATED WORK
Vision Foundation Models (VEMs). Recent advances biTebased VFMs Dosovitskiy et al. (2020) have produced highly transferable visual representations that excel in variety of 2D
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Figure 2: Method Overview. Starting from the LHS, two context views 1st are passed through a teacher network, producing two low-resolution 2D feature maps Fete. Using corresponding se- mantic masks, mask-aware upscaling (Sec. 3.3) produces 2D features phigh of the input resolu- tion. In parallel, pretrained feed-forwar 3D reconstruction model predicts 3D Gaussian primitives {ug,2) Ej. at) using the same context views I (Sec. 3.2). The upscaled 2D feature maps, F are then lifted to these 3D Gaussian primitives, using 2D-3D correspondences, yiclding a feature- augmented GS scene Gj <- {ug, Eg ag) U {fy) (Sec. 3.3). Next, the scene is splatted to target viewpoint, producing 2D feature map, which then blended with the semantic mask of the target view, resulting in 2D features Flgt (Sec. 3.4). Concurrently, as shown on the RHS, the target image 1st (corresponding to the rendered viewpoint) is passed through the student network to obtain its feature map F. Fdgt blend is then downscaled (bilinearly) producing a lower resolution 2D feature map which is compared to F!s to supervise the student via a distillation loss (Sec. 3.5). The teacher's weights are updated as an EMA of the student's weights. Note that SnD is finetuned on ScanNer++.
tasks (Radford et al., 2021: Zhou et al., 2021; Touvron et al., 2022; He et al., 2022). Our work builds upon DINO and DINOv2 Caron et al. (2021); Oquab et al. (2023), which are based on a this framework, student network learns to match the teacher's 's representations in different augmentations of the same image, resulting in embeddings with excellent performance on downstream tasks. Despite their success, recent work has shown that they remain limited in effectiveness in the 3D domain (Wang et al., 2022; Huang et al., 2024).
student-teacher distliistilation framework.
3D Scene Representations. Neural Radiance Fields (NeRF) (Mildenhall et al., 2021) have be- come a cornerstone for photorealistic Novel View Synthesis (NVS), but are hampered by slow ren- dering speeds. Recently, 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023) has emerged as an explicit scene representation offering significantly faster rendering. while also achieving strong re- sults in NVS. While providing high-quality 3D representations, these methods require per-scene optimization and numerous context views, making them unsuitable for our task.
Feed-forward 3D Reconstruction.
feed-forward To address these limits, recent methods
pipelines to directly predict volumetric fields Chen et al. (2021); Yu et al. (2021) or 3D Gaussians Charatan al. (2024); Wewer al. (2024): Chen al. (2024): Szymanowicz et al. (2024) from images. For instance, PixelSplat Charatan et al. (2024) predicts 3D Gaussians using cross-view attention and a Gaussian head, while MVSplat Chen al. (2024) adds a cost-volume geometry en- coder. These works focus on photometric reconstruction, lacking semantics and high-level features. 3D Feature Distillation. Building on these representations, several methods lift 2D features into 3D, enabling open-vocabulary understanding and editing capabilities (Kerr et al., 2023; Zhou et al., 2024; Qin al., 2024; Labe et al., 2024: Levy et al., 2025; Marrie et al., 2025). Most pipelines are optimization-basee requiring slow per-scene fitting to align features with geometry. These methods focus on distilling 2D features into a 3D representation. By contrast, we distill 3D knowledge into 2D features using 3D representations
teachers, enhancing the 3D awareness of 2D features. Enhancing 3D Awareness Of VFMs. A complementary line of research seeks to enhance the 3D awareness of pretrained 2D VFMs (Caron al., 2021; Oquab al., 2023). FiT3D Yue et al. (2024)

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lifts 2D features into 3D scene via optimization (Zhou et al., 2024), renders them from multiple views, and fine-tunes the 2D VFM by supervising its feature maps with rendered features. However, as imput features from different views are inconsistent, optimization inevitably yields a least-squares compromise-a semantic blur averaging the initial errors. MEF You et al. (2024) improves 3D cor- respondence by enforeing feature similarity at corresponding points, but this relational constraint alone cannot instill the full, dense geometric scene understanding needed for complex downstream tasks. Our approach instead introduces 3D awareness within distillation-basee training by aug- menting teachers with feed-forward 3D reconstruction model.
3
METHOD
We now outline our approach, illustrated in Fig. Finally, Sec.3.1 provides our student-teacher distillation approach of our method. Sec.3.2 describes the feed-forward 3D reconstruction pipeline augmenting the teacher. Sec.3.3 explains the process of lifting features into 3D, and Sec.3.4 de- tails the mask-aware feature blending mechanism addressing sparse, irregular viewpoints. Sec.3.5 presents the overall loss formulation. Training and implementation details are in Appendix A.4.
3.1 STUDENT- TEACHER DISTILLATION FRAMEWORK
Our method is built upon the student-teacher self-distillation paradigm popularized by DINO and DINOv2 (Caron et al., 2021; Oquab et al., 2023). The architecture consists of a student network, fa, with parameters fz. and a teacher network, ft. with parameters 9,, which share an identical network structure. A key departure from prior work is our supervisory mechanism. Instead of using 2D data augmentations, we leverage multi-view 3D scene data to instill geometric awareness. Our training data consists of scenes, where each scene S a collection of images and their corresponding camera parameters. That is, S {LI P,) where I, Hxx x3 is given view and P. E R the corresponding camera projection matrix. For each training iteration, we sample scene and draw from a pair of context views, {IITTx P) and a distinct targer view, (I p!t).
3D-Aware Teacher Augmentation. The core of our method lies in augmenting the teacher's out- put to be 3D-aware. This is achieved by generating a supervisory feature map for the target view through a 3D reconstruction and rendering pipeline. First, we use a pre-trained, feed-forward 3D reconstruction model (Sec. 3.2) to generate an explieit 3D representation of the scene, modeled as a set of 3D Gaussians, ggoom from the two context views. Concurrently, we process the same context views with the teacher network fr to extract 2D feature maps, {Fşx E R4x=xC12 the feature maps' spatial resolution. These features are then upscaled H x W 1=1 where h x w listed into 3D space by associating them with 3D Gaussians, yielding 3D feature scene Great (Sec. 3.3). Finally, this 3D feature scene is rendered from the perspective of the target view's camera P producing the teacher's s supervisory feature map, F E # xe' which goes through an additional blending mechanism to further enhance feature map quality (Sec. 3.4).
Student Distillation. The student network fa only observes the 2D target image 1ss and produces its own feature map, F E / The student trained by minimizing the discrepancy between its features and the teacher's 's rendered features, using the distillation loss described in Sec. 3.5.
3.2 FEED-FORWARD 3D RECONSTRUCTION MODEL
To provide a geometric scaffold for lifting 2D features into 3D, our method employs a pre-trained,
feed-forward 3D reconstruction model based on the 3DGS representation (Kerbl et al., 2023). 3DGS models a scene as a collection of anisotropic 3D Gaussians, where each Gaussian G, is parameterized by its geometric and appearance properties: mean position μ4 RR3 covariance matrix Σ, R3x3 (decomposed into scale and rotation), an opacity ag E (0, 1]. and spherical harmonic (SH) coefficients c; for view-dependent color.
Instead of traditional per-scene optimization, we leverage a feed-forwart network, $ geom which directly predicts the 3DGS representation from a sparse set of K context views. Formally, given the context views [[ petx) } (we use K = 2). the model produces a set of M Gaussians that represent the scene's geometry and appearance:
T geom {(T55 )
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Specifically, we instantiate &goom with a pre-trained MVSplat model (Chen et al., 2024). This model first extracts multi-view features, builds a cost volume to estimate per-pixel depth via plane-sweep stereo, and finally unprojects these depth maps to form the 3D Gaussian centers, while other Gaus- sian parameters are extracted from multi-view features using a Gaussian head. This process provides an explicit one-to-one correspondence between pixels in the context views and 3D) Gaussians.
MVSplat used as frozen, off-the-shelf component. Since our objective is to construct 3D feature scene rather than to perform novel view synthesis, we only utilize the geometric parameters (uu, Ei, 0;) of the predicted Gaussians. The appearance parameters (the SH coefficients c;) are disregarded the subsequent feature lifting step, which is detailed Sec. 3.3.
3.3 MASK-AWARE FEATURE LIFTING
Having constructed 3D geometric scaffold from the context views, the next step to lift the semantic features from the teacher network, St. onto this 3D representation. As noted above, this is achieved by processing the same context views {I5" } with the teacher to produce low-resolution feature maps (F Rhx=xx11 f==1- We then associate these features with the 3D Gaussians via the 1, Mixel-t--Gaussiee correspondence provided by the reconstruction model.
A key challenge is the significant resolution mismatch between the teacher's patch-based feature maps (h x w) and the full-resolution context images (H W) from which the Gaussians were derived (a scale of x x14). Naively upscaling the feature maps using bilinear interpolation leads to severe blurring and feature mixing across object boundaries, degrading the final quality.
To address this, we propose to utilize mask-aware upscaling mechanism that leverages instance semantic segmentation masks (available during training) to guide the interpolation. For each pixel u the target high-resolution grid, its feature value Fhigh is computed by interpolating only from neighboring low-resolution feature points e that share the same semantic label. The interpolated feature is:
Fhigh
Wee Floo
eEN(u)
where (u) is the set of neighboring low-resolution feature points, and weights wav are defined as:
if mask(v) mask(u),
Wan'
Bev
of WEN (W)())Amakk(r maak)))((()
0
otherwise,
where War are the standard bilinear interpolation weights and mask(u) is the semantic label of pixel u. This formulation ensures that feature upscaling respects semantic boundaries, producing sharp, high-resolution feature maps Fligh e IR W xC" We demonstrate a quantitative and qualitative analysis of mask-aware upscaling in Sec. and Fig. respectively. Our mask-aware lifting strategy is inspired by the feature lifting approach employed in OccamLGS (Cheng et al., 2024), utilizing semantic masks to guide interpolation and preserve object boundaries.
Finally, using the pixel-to- pixus-ao-Gaussian correspondence from T geam, each feature vector in Fhigh is at- tached to its corresponding 3D Gaussian, resulting 3D feature scene where each Gaussian Gj now endowed with semantic feature vector fy: Gj <- {Mjj Eg, az) {fz}.
3.4 SEMANTIC BLENDING FOR FEATURE REGULARIZATION
Building 3D from sparse and irregularly spaced context views introduce geometric artifacts. When the 3D feature scene is rendered a novel target view, these artifacts may manifest as noise or minor misalignments in the resulting feature map, F'gt Fendered This noisy supervisory signal degrade the quality of the student's 's leamed representations.
To mitigate this, we introduce a semantic blending step that regularizes the rendered feature map by enforcing local consistency within object regions. Inspired by Huang al. (2025), this step smooths the features spatially, guided by instance semantic segmentation masks. For each pixel location u in the rendered feature map, the final blended feature F Fatend()) (u) is computed weighted average of
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the original rendered feature F Frendered (u) and the mean of all rendered features in Mus where My. denotes the set all coordinates the target view sharing the same semantic mask as u.
hleen = a Fendered (u) + + (1--00 a))
Σ
F rendered (U);
(4)
[Mal
PEM.
where E [0.1] is blending factor (we use = 0.5). By confining the averaging to within semantic boundaries, this process corrects for small geometric inconsistencies and produces more coherent supervisory signal, while preserving sharp details at object edges. Visualization of this effect can be seen in the Appendix Fig. 12.
3.5 DISTILLATION OBJECTIVE
The final step is distill the 3D-aware knowledge from the teacher into the student network. The supervisory signal is the blended feature map, F E #fxxx which is the result of the full teacher pipeline noted above. Concurrently, the student network, fer processes the corresponding 2D target view t to produce its own feature map, F E RxxwxC We first downscale the teacher's high-resolution feature map to match the student's output dimensions using bilinear interpolation.
Following the DINO framework (Caron al., 2021; Oquab et al., 2023), both feature maps are passed through shared DINO head which consists of a small MLP. The student's parameters 0₂ are optimized to minimize a distillation loss Edistiti.
min Laisinl (head(F"))) sg (geedd(FFF (tem))) O,
where head(-) is the DINO head, Fiet Mend is the rendered teacher features after blending and down- scaling, sg(.) the stop-gradient operator, and Ldistill the cross-entropy loss between the teacher and student's output distributions. The student's parameters 6, optimized via backpropagation, while the teacher's parameters are instead updated via EMA: 0g < A0, +(1 (1-1)64-6 A)0,, where À E [o. the momentum coefficient. More details are in Appendix A.4.
4 EXPERIMENTS
We evaluate our ability enhance the 3D awareness of DINOv2 features while improving their semantic representation. Specifically, we assess the inference of 3D perceptual properties (surface normal and depth estimation) from single-image features and multi-view feature correspondence. For semantics, we evaluate semantic segmentation. Limitations are provided in Appendix A.8.
Baselines. Our first baseline is the vanilla pre-trained DINOv2 model. We conduct our experi- ments on the small or base variants. We also consider Fit3D Yue et al. (2024), the work most closely related to ours. Consequently, we conducted teacher) using the same subset ScanNet++ data Yeshwanth et al. (2023) as is done by Fit3D, to ensure fair comparison. Fit3D first constructs a dataset of 3DGS scenes with DINOv2 features by following established feature distillation approaches. It then fine-tunes DINOv2 to produce fea- tures that match those rendered from the optimized 3D scenes. Lastly, we consider MEF You et al. (2024), which fine-tunes DINOv2 by enforcing multiview feature correspondence in their training objective. However, we note that MEF requires correspondence annotation, which our method does not use. Implementation details for evaluating the different baselines can be found in Appendix A.6.
finetuning (on pretrained DINOv2 student and
Evaluation Protocol. For monocular depth estimation, surface normal estimation, and semantic segmentation, we consider the linear probing protocol. We also evaluate multi-view consistency by measuring the correspondence between multiple views, where the goal is to identify image patches across views that depict the same 3D point. See evaluation details in Appendix A.5.
Evaluation Datasets. For depth estimation and semantic segmentation, we follow Fit3D and consider indoor scenes from the ScanNet++ validation set Yeshwanth et al. (2023) as well as ScanNet Dai et al. (2017) and NYUv2 Silberman et al. (2012) datasets. These datasets share similar characteristics but employ different sensor modalities. For surface normal estimation, use the NYUv2 dataset where surface normal annotation is curated by GeoNet Ladický et al. (2014). For feature correspondence, we evaluate on the test set of SuperGlue Sarlin et al. (2020),

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Published as a conference paper at ICLR 2026
Table Quantitative comparison for monocular depth estimation, on ViT-Small/Base backbones.
Method
ScanNet++
ScanNet
NYUv2
Rel +
RMSE 4
Rel 4
RMSE 4
Rel
RMSE $
DINOv2
0.2811
0.3777
0.1437
0.2817
0,1476
0.5210
VITs
Fit3D
0.2500
0.3506
0.1375
0.2713
0,1418
0.5075
MEF
0.3085
0.4000
0.1566
0.3042
0.1661
0.5656
Ours
0.2421
0.3299
0.1266
0.2555
0.1406
0.4912
DINOv2
0.2539
0.3435
0.1169
0.2369
0.1375
0.4948
ViTb
Fit3D
0.2420
0.3306
0.1166
0,2346
0.1359
0.4794
MEF
0.2849
0.3726
0.1269
0.2534
0.1537
0.5214
Ours
0.2169
0.2971
0.1113
0.2245
0.1261
0.4596
Image
GT
DINOv2
MEF
Fit3D
Ours
Figure 3: Qualitative comparison for monocular depth estimation using ViT-Small backbone Gro=Ground Truth).
Image
GT
DINOv2
MEF
Fit3D
Ours
NYUv2
Figure 4: Qualitative comparison of surface normals estimation using ViT-Small backbone.
which includes image pairs from ScanNet. To further clude OOD benchmarks: ADE20K Zhou et al. (2017) and Pascal VOC Everingham et al. (2015) for semantic segmentation, and the KITTI Geiger et al. (2013) dataset for depth estimation. For further information on datasets and specific training-test splits employed, please refer Appendix A.7.
robustness and transferability, we in-
Table 2: Quantitative com- parison for surface nor- mal estimation ViT- Small/Base backbones.
4.1 IN-DOMAIN EVALUATION
We evaluate our features on downstream tasks for in in-domain datasets,
NYUv2
Monocular Depth Estimation. Tab. shows quantitative results for monocular depth estimation using RMSE and Abs-Rel metrics. Our method consistently outperforms baselines, with average relative gains on RMSE of 5.90%. 5.82%. and 3.21 % on ScanNet++. Scan- Net, and NYUv2 over the closest baseline. Fig. 3 presents visual comparison our method to baselines on the NYUv2 dataset, where results the ScanNet and ScanNet++ Fig. 8. Our depth maps exhibit more refined structural details and smoother geometric surfaces than baselines.
Method
RMSE $
DINOv2
30.99
Fit3D
30.57
MEF
33.05
Ours
28.93
DINOv2
31.40
Fit3D
30.57
shown in Appendix A.2
MEF
32.60
Ours
29.37

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DINOv2
MEF
Fit3D
Ours
ScanNet
Figure 5: Qualitative comparison of multi-view correspondences using ViT-Small backbone. Lines connect matched points between the two views; color encodes the 2D Euclidean reprojection error computed under the ground-truth pose, with green/red indicating small/large error, respectively.
Table 3: Quantitative comparison for semantic segmentation using ViT-Small/Base.
Method
ScanNet++
ScanNet mloU 1
NYUv2
aAcc f
mloU f
mAcc f
aAcc
mAce f
aAcc 1
mloU
mAcc 1
DINOv2
80.23
29.54
39.11
76.60
51.27
63.28
82,25
64.73
75.56
VITs
Fit3D
83.34
31.77
41.09
78.53
54.50
66.57
83.32
66.33
77.06
MEF
79.45
27.44
36.77
74.63
47.44
58.98
81.02
63.17
74.16
Ours
83.84
31.78
41.42
79.48
56.01
68.10
84.31
67.50
77.96
DINOv2
81.85
31.95
41.69
79.48
56.42
68.22
83.92
67.47
78.02
ViTb
Fit3D
84.90
34.85
44.83
82.25
60.78
72.60
85.54
70.18
80.36
MEF
82.23
31.63
41.50
78.60
54.14
65.83
83.62
67.64
78.12
Ours
84.77
34.07
44.00
83.43
62.64
74.29
86.05
70.60
80.91
Surface Normal Estimation. In Tab.2, we present the RMSE over dense prediction of normal directions on single-view images the NYUv2 dataset. Our method yields an improvement of 5.37% over the closest baseline using DINOv2- 2-2llll and 3.93% using DINOv2-Base Fig. further highlights qualitatively the superior quality of our predictions. Our model produces smoother normal maps (e.g., second row) and demonstrates more accurate under- standing of the 3D scene. For example, when viewing the couch, other methods incorrectly predict normals, suggesting the couch faces the camera. Our model correctly infers the visible surface is the back of the couch and assigns flat, consistent normals.
Image
GT
DINOv2
MEF
Fit3D
Ours
NYUv2
Figure 6: Qualitative comparison for semantic segmentation using ViT-Small backbone.
For multi-view correspondence, we fine-tune DI- NOv2 without semantic blending of the splatted features, as blending tends to smooth feature representations. We consider the recall of the fraction of all proposed correspondences that satisfy an accuracy criterion of ten pixels, see Appendix A.5. In Fig. 7, we observe our method consistently improves recall over baselines across varying viewpoint changes, In Fig. 5, we illustrate this qualitatively.
Multiview Correspondence.
60
HET O1NN22
Semantic Segmentation. provide quantitative comparison for seman- tie segmentation. We report average accuracy (aAce), mean intersection over union (mloU), and mean accuracy (mAcc). Compared to the leading baseline of FTT3D when using
Tab. 3, we
20-
60
(clgreess)
Figure 7: Quantitative comparison of multi-view correspondence for varying viewpoint changes, us- ing ViT-Small/Base backbones on ScanNet.
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Table 4: Quantitative comparison on out-of-domain datasets, using ViT-Base backbone.
Method
ADE20k (seg.)
Pascal VOC (seg.)
KITTI (depth)
aAce 1
mloU |
mAcc f
aAcc f
mloU
mAcc
Rel
RMSE
DINOv2
77.39
40.78
53.31
96.20
83,00
89.36
0.0686
2.3558
Fit3D
82.21
48.29
60.03
96.77
85.08
90.72
0.0679
2.2485
MEF
80.16
45.14
56.15
95.73
80.82
87.67
0.0772
2.4160
Ours
83.24
50.01
61.61
96.96
85.75
91.63
0.0631
2.1741
Table Ablation studies, on ScanNet++ dataset, using the VIT-Small variant.
Segmentation mIoUt
Depth
Ablation
aAec
mAcet
Rell
RMSE,
Without Blending (A)
82.83
30.99
40.80
0.2531
0.3435
Bilinear instead of Masked Upscaling (B)
83.66
31.46
41.01
0.2428
0.3309
Cosine Loss instead of Distillation Loss (C)
83.54
31.27
40.96
0.2421
0.3310
Frozen instead of Learnable Teacher (D)
83.34
31.90
41.88
0.2500
0.3444
Context instead of Novel Views (E)
84.02
32.08
41.74
0.2430
0.3332
SAM Masks instead of Manual Masks (F)
83.49
31.51
41.39
0.2436
0.3328
Feature Rendering Loss (G)
83.06
31.40
40.97
0.2484
0.3430
Basic Variant (H)
81.80
30.66
40.47
0.2741
0.3520
Ours (Full Model)
83.84
31.78
41.42
0.2421
0.3299
DINOv2-Small backbone, we achieve a rela-
tive improvement of 0.03%, 2.77%, and 1.76% on ScanNet++, ScanNet, and NYUv2, considering
mloU.
We observe similar trend with DINOv2- B-see, improving significantly the ScanNet and NYUv2 datasets, with a slight decrease on the ScanNet++ dataset. Fig. 6, we present a qualitative com- parison of the NYUv2 dataset. Our method produces cleaner object boundaries and avoids the fragmented masks present in baselines, as seen in the partition wall in the first row. Additional visualizations, for additional datasets, can be seen in Appendix A.2 in Fig. 9.
4.2 OUT OF DOMAIN EVALUATION
In Tab. 4, we assess method using the DINOv2-base backbone out-of-domain datasets. For segmentation on ADE20K and Pascal VOC, we achieve relative mloU improvements of 3.56% and 0.79% % respectively over closest baseline. Notably, ADE20K contains many outdoor, highly cluttered scenes distinct from our training data. For monocular depth estimation on KITTI Geiger et al. (2013), achieve 3.31 % improvement over Fit3D, demonstrating transfer of 3D spatial awareness from indoor to outdoor scenarios. See Appendix A.2 Fig. 10 for visualizations.
4.3 ABLATION STUDIES AND FEATURE VISUALIZATION
In Tab. 5, ablate our model semantic segmentation and depth estimation. In Ablation A. we consider the effect of removing blending (Sec. 3.4). We also provide a visual illustration in Appendix A.2 Fig. 12. Next, Ablation B, we consider the effect of replacing the mask-aware upscaling with standard bilinear upscaling. Visually, the effect is shown in Appendix A.2 Fig. 11. In Ablation C, we consider the effect of our distillation objective and compare it to using cosine loss on the patch embeddings (without DINO head) instead. In Ablation D, we consider the effect of freezing the teacher, as opposed to updating using EMA, demonstrating the effectiveness of jointly updating the teacher and student. In Ablation E, we consider the effect of rendering features to context views instead a target view located between the two context views. As seen, this is beneficial for depth estimation. Interestingly, segmentation performance improves when rendering to the context views. In Ablation F, we use SAt-extracted masks Kirillov et al. (2023) instead of manually annotated masks for mask-aware upscaling and semantic blending, showing that manual

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annotation has only minor advantage. Note, we don't require consistent class labels across frames. In Ablation G, we replace our student-teacher distillation framework with a direct feature rendering Vanilla DINOv2, it underperforms
loss on fixed teacher. While this improves (Depth RMSE 0.3430 vs. 0.3299), confirming that our soft distillation objective and iterative teacher updates are essential for mitigating artifacts and maximizing geometric awareness. In Ablation H, we evaluate the most basic configuration of our method (Fixed teacher, no mask-aware upscaling, no blending). The performance drop relative the full model (Depth RMSE 0.3520 vs. 0.3299) validates that our architectural components samponents-specfficllly the iterative EMA update and mask-aware lifting are integral to achieving state-of-the--an results.
Full SnD
Feature Visualization. In Appendix A.3, we visualize our features using PCA, further showcas- ing our approach's s advantages. These reveal less noise and clearer semantic boundaries in feature space. K-means further shows that semantically similar objects cluster together while retaining fine details. We also analyze features from two views, in shared and per-view spaces.
CONCLUSION
We introduced Splat and Distill, a novel 3D-aware distillation framework to instill robust 3D aware- ness into 2D VFMs. Our core contribution is the augmentation of the teacher network with fast, foed-forward 3D reconstruction pipeline within a distillation framework. This allows us to lift 2D features from context views into explicit 3D Gaussian representation, splat these features onto novel viewpoints, and distill this geometrically grounded knowledge into a student model. Our method significantly outperforms state-of-the-art baselines a comprehensive suite of downstream tasks, including monocular depth estimation, surface normal estimation, and multi-view correspon- dence, while also enhancing the underlying semantic richness of the original 2D features.
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REPRODUCIBILITY STATEMENT
We provide full implementation details of our method in Appendix A, including architecture spec- ifications, hyperparameters, and training procedures. In addition, we include the source code as supplementary as part of the submission, ensuring full reproducibility of experiments. All datasets used in this work are publicly available online under their respective licenses, and we describe in Appendix A.7 how they are accessed and preprocessed.
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Image
GT
DINOv2
MEF
Fi3DD
Ours
ScanNet+4
Scan Net
Figure 8: Qualitative comparison the task of monocular depth estimation, using ViT-Small backbone for the ScanNet++ and ScanNet datasets.
Image
GT
DINOv2
MEF
FitDD
Ours
ScanNet++
ScanNet
Figure 9: Qualitative comparison on the task of semantic segmentation, using ViT-Small back- bone for the ScanNet++ and ScanNet datasets.
A APPENDIX
We present additional comparisons in Appendix A. Qualitative results are in Appendix A.2, and in Appendix A.3, we provide qualitative analysis of feature representations, comparing our method to DINOv2, Training implementation details are described in Appendix A.4, and evaluation protocols for all downstream tasks are summarized Appendix A.5. Appendix A.6 outlines the baseline evaluation procedures, while Appendix A.7 details all datasets used for training and validation. In Appendix A.8 we discuss the limitations our method. Finally, Appendix A.9 clarifies the use of large language models for literature review and manuscript preparation.
A.1 ADDITIONAL COMPARISONS:
BACKBONES AND CONCURRENT WORK
Task-Specific Backbones (VGGT). To evaluate SnD a backbone within -f the-art pipelines, we integrated our model into VGGT (Wang et al., 2025) for monocular depth estimation on Scan- Net++. Since the pre-trained VGGT relies on ViT-Large (1024-dim) and model is ViT-Small
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Image
GT
DINOv2
MEF
Fit3D
Ours
KIT SALT
ADE20k
Pascal VOC
Figure 10: Qualitative comparison on out-of-domain datasets for two tasks: monocular depth estimation and semantic segmentation, using ViT-Small backbone. Monocular depth estimation evaluated on the KITTI dataset, while semantic segmentation on ADE20K and Pascal VOC.
(a) Masks
(b) Nearest Neighbor
(c) Bilinear
(d) Mask Aware Bilinear
Figure 11: Effect of mask-aware upscaling. PCA upscaled features with different methods, comparing nearest-neighhor bilinear vs. mask-aware upscaling. Using mask-aware upscaling enables clear boundaries between distinct objects.
(384-dim), we added a learnable linear projection (384 1024). Crucially, to isolate the qual- ity of the backbone representations, we froze the entire VGGT architecture (transformer layers, global/frame attention) and fine-tuned only the projection and depth head. As shown Tab. (Left), replacing the DINOv2 backbone with SnD yields improved performance.
compare against DUNE (Sanyıldız et al., 2025), which distills universal encoder. We evaluated the official DUNE weights ScanNet++. specifically the dune-vite sma 4_448, 8. pth variant as it produced better results than the 336px version. As shown in Tab. 6 (Right), SnD significantly outperforms DUNE on both depth estimation and seman- tie segmentation. Unlike DUNE, which inherits 3D inconsistencies from DINOv2, SnD corrects them via feed-forward reconstruction pipeline.
Comparison to DUNE.
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(a) View
(b) Mask
Rendered Image (d) Splatted Features (c) Blended Features
Figure 12: Semantic Blending for Feature Regularization Visualization. We visualize the impact of semantic blending for feature regularization. (a) shows the input view, (b) displays its correspond- ing semantic mask, available during training. (c) presents the reconstructed scene generated by pretrained feed-forward model, highlighting areas of poor reconstruction-
as the misaligned desk. The splatted features (visualized via PCA) are shown in (d), illustrating the inaccuracies in- troduced by flawed reconstruction. (e) demonstrates how semantic blending, guided by the mask, refines these features and produces sharper object boundaries.
Table Left: Comparison of backbones within the VGGT pipeline (fine-tuning only the head/projection). Right: Comparison against DUNE. Both evaluated on ScanNet++ (ViT-Small).
VGGT Backbone Comparison
Comparison vs. DUNE
Method
Depth Abs-Rel +
Depth RMSE
Method
Depth RMSE +
Seg. mloU
VGGT (DINOv2)
0.2220
0.3426
DUNE
0.3929 0.3299
25.77
VGGT (SnD)
0.2117
0.3283
Ours (SnD)
31.78
A.2
ADDITIONAL QUALITATIVE RESULTS
We provide additional qualitative results for the ScanNet and ScanNet++ dataset, on single-view depth estimation, and semantic segmentation results on indoor scenes in Fig. 8, and Fig. 9, respec- tively. In addition, we visualize single-view depth estimation and semantic segmentation results on out-of-domain datasets in Fig. 10.
Depth estimation qualitative results. Fig. 8 presents qualitative results for depth estimation on the ScanNet++ and ScanNet datasets. Our method accurately captures fine details, such as the chair in the second row, and produces smoother surfaces, estimation captures the global structure of a scene, while surface normal prediction provides local orientation cues. Although their performance is often correlated, these tasks rely distinct visual signals and thus offer complementary evidence for 3D understanding (El Banani et al., 2024).
observed on the bed in the third row. Depth
Semantic segmentation qualitative results. Figure 9 shows qualitative results for semantic seg- mentation on ScanNet++ and ScanNet. Our method produces sharper, more coherent masks; for instance, in the third row, it accurately segments chair legs in the dining table image, even under low-light conditions. These results indicate that our model learns representations with enhanced spatial and semantic consistency.
Out-of-domain qualitative results. Although model trained solely the ScanNet++ dataset of indoor scenes, it demonstrates strong generalization to out-of-domain datasets. In Fig. 10, evaluate method the KITTI dataset for depth estimation, observing improved depth smoothness. For semantic segmentation, comparisons on ADE20k and Pascal VOC show that our approach captures fine details, such as the legs of chairs at the dining table (fourth row), and pro- duces cleaner, less noisy outputs, as seen in the background of the dog image (fifth row).
Visualization of mask-aware upscaling. We visualize the impact various upscaling methods in comparison to the mask-aware bilinear upscaling (see Sec. 3.3) used by our method in Fig. 11. Notably, using mask-aware upscaling produces sharp boundaries between objects by leveraging semantic masks to guide interpolation around object boundaries.
Visualization of semantic blending for feature regularization. To analyze the impact our semantic blending (see Sec. 3.4), we demonstrate its effect rendered features visually, using
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PCA. In Fig. 12, showcase example where blending corrects poorly reconstructed scene. Notably, observe the initial poor reconstruction of the chair and its correction after the application of blending.
Image
DINOv2 PCA
Ours PCA
DINOv2 K-means
Ours K-means
Qualitative visualization of DINOv2 features vs. ours on a ViT-Small backbone. Each pair of rows corresponds to scene from the ScanNet++ dataset. PCA and K-means are computed per scene rather than per image.
Figure 13:
A.3 FEATURES QUALITATIVE ANALYSIS
Multi-view features. We provide qualitative analysis of our feature representations using principal component analysis (PCA) and K-means clustering, In Fig. 13, we compare results before and after applying our distillation method. To assess multi-view consistency, we extract features from two distinct views of the same scene from ScanNet++ (Yeshwanth al., 2023). Both PCA and K-means clustering are performed jointly on features from both views, ensuring shared feature basis across perspectives. Our results reveal clear and well-defined boundaries in the PCA plots, well more
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compact clusters in the K-means visualizations, These findings indicate improvements in multi-view consistency and feature semantics.
Single-view features. In Fig. 14 evaluate feature quality across a diverse range of datasets, includ- ing both in-domain and out-of-domain samples. Our analysis reveals improved feature semantics, as evidenced by reduced noise in the PCA projections and more compact clusters in the K-means visu- alizations. Importantly, we find that distinct object semantics present before applying our distillation method are also preserved afterward, across all datasets (for example, in the seventh row, K-means consistently identifies chickens before and after distillation). These results increase the confidence that the proposed approach maintains the semantic integrity of the original model's features while improving its quality via 3D awareness.
A.4 TRAINING IMPLEMENTATION DETAILS
For 3D scene reconstruction, we employ an off-the-shelf, pre-trained MVSplat model Chen et al. (2024), originally trained on the RE10k dataset (Zhou et al., 2018). We further fine-tune this model on ScanNet++ Yeshwanth et al. (2023) to improve its 3D reconstruction for 40,000 iterations to better adapt it to our specific task requirements.
We initialize both the student and the teacher with the pretrained DINOv2 weights available online. In addition, we require a DINO head Oquab et al. (2023). The DINO head maps each backbone feature probability distribution over set of learnable prototypes, implemented as the weights of a final linear layer. These prototypes act as latent surrogate classes, turning the distillation task into classification-like objective without labels. An EMA updated teacher network provides soft targets, and the student is trained to match them via cross-entropy. This design encourages features to organize around diverse prototype centroids, prevents collapse, and yields robust, transferable representations, as refined in DINOv2 with additional stabilization techniques. The DINO head design used in our experiments consists of a multi-layer perceptron (MLP) with 3 layers, where each hidden layer has dimensionality of 2048. A bottleneck layer with a reduced dimension of 256 placed before the output, which helps to control the capacity and regularize the representations. The final output projects features onto 65,536 prototype vectors, which serve as anchor points for the distillation objective. This architectural choice follows the standard DINO framework, facilitating scalable clustering and robust representation learning.
Fine-tuning of the DINOv2 Oquab et al. (2023) backbone is performed using LoRA Hu et al. (2022) with rank of 8 to enable efficient adaptation while minimizing additional parameters. Optimization carried out with a batch size of using the Adam optimizer Kingma & Ba (2014), starting from an initial learning rate of 2 10 and following cosine annealing schedule.
The teacher network is updated every 10 training steps via EMA with a momentum coefficient of 0.999 (Tarvainen & Valpola, 2017). For simplicity, do not incorporate DINOv2-specife compo- nents such as temperature scheduling and momentum centering.
Training is conducted for 50,000 iterations on a single NVIDIA L40S GPU, requiring approximately 18 hours to complete. The primary computational bottleneck of our approach arises from the train- able DINO head and the use of high-resolution input images. Note that the entire 3D reconstruction process and feature extraction from the teacher network are performed with frozen weights, making these steps highly efficient in resources.
A.5 EVALUATION DETAILS
Monocular Depth Estimation. We treat depth estimation classification problem by discretizing the continuous depth range into 256 uniformly spaced bins, following the AdaBins approach Bhat et al. (2021). The input the classification head is constructed by concatenating the global [CLS] token with each patch token from the Vision Transformer, and the resulting features are spatially upscaled by a factor of four. A linear head is then applied to these upscaled features to produce, for each pixel, logits corresponding to each depth bin. To map these logits to depth predictions, we generate bins) linearly spaced depth values between (mindepth) and (maxdepth). The logits transformed into probability distribution across bins by applying ReLU activation, adding small epsilon for numerical stability, and normalizing so that the probabilities sum to one. The final depth for each pixel is calculated the weighted sum of the bin centers, with the weights given
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by the normalized probabilities. This produces continuous depth map, which is then interpolated back to the spatial resolution of the original input image. The probing is done using a classification single GPU to predict the correct depth bin for each patch. We use
loss. for 307,200 iterations AdamW W optimizer Kingma & Ba (2014) with a learning rate of 0.0001, betas (0.9, 0.999), weight decay of 0.01, and batch size of two.
Surface Normal Estimation. For surface normal estimation, we follow the protocol of Bae et al. Bae al. (2021) and employ an uncertainty- normals from frozen features, which are upscaled by a factor of four. The output of the linear layer subsequently interpolated to the original input image resolution. Training is performed for ten epochs with batch size of eight, using the AdamW optimizer and learning rate of 5 x 10 The learning rate schedule consists of linear warmup phase followed by cosine decay(Kingm) & Ba, 2014; Loshchilov & Hutter, 2016). We report the root mean squared angular prediction error as our evaluation metric.
angular loss. We train linear head regress surface
Feature correspondence We follow the protocol established by Probe3D El Banani et al. (2024), To find correspondences between two images, we first extract feature maps from each image using a neural network. Pixel coordinates are projected into 3D space using depth maps and camera intrin- sics, and features at these 3D locations form point clouds. We then match features from the two point clouds by performing k-nearest neighbor search using cosine similarity. A ratio test is applied to filter out ambiguous matches, and the remaining matches are selected as correspondences. For evaluation, we use the ground-truth relative pose between the two views transform the correspond- ing 3D points from one view to the other. These transformed points are then projected back to 2D image coordinates in the target image. We measure correspondence accuracy as the Euclidean dis- lance between each projected match and its ground-truth location in the target image. Performance is reported as recall at a 10-pixel error threshold, and further analyzed according to viewpoint changes. This provides a rigorous assessment of correspondence quality across different scenes and camera poses.
Semantic Segmentation. A linear classifier is trained on top of the frozen patch tokens from the backbone. This classifier produces class scores at a lower spatial resolution, which are then upscaled to the original image dimensions to generate the final segmentation map. The linear layer is trained for 320,000 iterations using a single GPU. We utilize a cross-entropy loss. For optimization, we use AdamW optimizer Kingma& & Ba (2014) with learning rate of 0.001 and a batch size of two.
A.6 BASELINE EVALUATIONS DETAILS
In this paper, we compare pipeline with DINOv2, FiT3D, and MEF. All methods are based VIT architecture and provide publicly available weights for both ViT-S and ViT-B models. However, FIT3D and MEF include additional components beyond the base architecture, necessitating certain adjustments for a fair evaluation.
Unlike our approach, which utilizes features directly extracted from finetuned DINOv2 VIT, FIT3D computes its metrics using a concatenation of features from their model and the original DINOv2. As result, linear probing with FiT3D requires increasing the input dimension of the linear head by the embedding dimension to accommodate the additional channels. Notably. our results for depth estimation and semantic segmentation with FiT3D differ from those published in their paper. These discrepancies can be attributed to variations in dataset splits (except for ScanNet++, where splits are similar) and differences in training procedures- for instance, FiT3D trained probes for fewer training steps but with 8 Nvidia A A10 GPUs, resulting in a larger effective batch size.
Similarly, when comparing with MEF, we had to introduce an additional convolutional layer on top of the patch tokens, as required by their pipeline. In contrast, our method does not require any such modifications and only trains the weights of the original DINOv2 model.
A.7 DATASET DETAILS
Training. Our models are pretrained exclusively on the ScanNet++ Yeshwanth et al. (2023) dataset, following similar protocol to Yue et al. (2024). Specifically, we use training split of 280 indoor scenes for representation learning. To ensure consistency with our baseline Yue et al. (2024), we maintain the same input size at (584 x 876) for ScanNet++. Notably, this resolution is
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Published as a conference paper at ICLR 2026
significantly higher than (180 320) used in MVSplat (Chen et al., 2024). The ScanNet++ dataset provides 3D annotations of the different objects in the scene, which are rendered to 2D and provide weak supervision of semantic guidance during training pipeline.
Evaluation. After finetuning, we assess the learned representations on various downstream tasks using linear probing. This involves creating train-test split to train and evaluate the linear probes, while keeping the backbone model frozen throughout evaluation. For multi-view correspondence, no split is needed, as it performed without additional training.
ScanNet++: For linear probing experiments on ScanNet++, we use the split described in Yue et al. (2024), with 230 scenes 4110,55 views) for training. and report results on the validation set, which consists of 50 scenes (30,638 images).
ScanNet: For depth estimation and semantic segmentation on ScanNet Dai et al. (2017), we use the official acaanet_framee fww.earaaa......... 25kk subset. For efficiency, we downscale the images in ScanNet by factor of two, i.e., (484 968). This subset randomly split into 80% for training and 20% for validation. For multi-view correspondence, (2020), which comprises 1,500 curated image pairs. As this task does not involve linear probing or additional training, the entire set is used solely for evaluation.
utilize the ScanNet Pairs benchmark Sarlin et al.
NYUv2: For depth estimation and semantic segmentation, we use the train-test split provided by Su- teu & neverix (2019) for training and evaluating the linear probes. For surface normal estimation, we follow the original split of 1,449 labeled images with ground-truth surface normal annotations Ladický et al. (2014).
ADE20K: For semantic segmentation evaluation of the ADE20K dataset Zhou et al. (2017), we follow the official train-validation split.
PASCAL VOC: We further benchmark semantic segmentation on the PASCAL VOC dataset Ever- ingham et al. (2015), adhering the official train-test split.
KITT/: For monocular depth estimation, we utilize the KITTI dataset Geiger et al. (2013). Due to the absence of an official split for the depth selection subset, we generate one by randomly assigning 85% of the images to train and 15% to validation using custom script, and use these splits for both training and evaluating the linear probes.
A.8 LIMITATIONS
Our approach, while effective within its design scope, has two main limitations. First, the quality of our supervisory signal depends directly on the performance the feed-forward 3DGS model: when this reconstruction is suboptimal inaccurate, the supervision is degraded and overall performance can suffer. Second, our training currently relies on multiview image datasets, which are relatively limited in availability and diversity. Enabling training on video data would allow leverage substantially larger and more diverse data sources.
A.9
USE OF LARGE LANGUAGE MODELS (LLMS)
Large Language Models (LLMs) were utilized during the development of this work to assist with the identification of related research and to provide support in the writing process, including drafting and refining sections of the manuscript. All substantive scientific contributions, experimental design, and analysis were performed by the authors.
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Published as a conference paper at ICLR 2026
DINOv2 PCA
Ours PCA
DINOv2 K-means
Ours K-means
Image
ScanNet++
Sca
CAR
ADE20K
Paseal VOC
KITTI
Figure 14: Qualitative visualization of DINOv2 features vs. ours on different datasets using a VIT-
Small backbone. We visualize single-image features using PCA and K-means.
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Reviews:
[{"review_id": "YGgcUQyN5A", "reviewer": "Reviewer_C1do", "summary": "This paper introduces SnD, a new pipeline to improve the 3D awareness of 2D foundation features. Similar to previous methods such as FiT3D, SnD uses 3DGS as 3D representation to improve 3D awareness. Instead of per-scene optimization, SnD uses feed-forward 3DGS (MVSplat) to improve efficiency. In addition, a student-teacher framework, similar to DINO, is used to distill the 3D awareness from teacher model. SnD outperforms baselines in various downstream tasks.", "questions": "1. In Table 1, I suggest clarifying that SnD is finetuned on ScanNet++. \n\n2. Semantic mask: As shown in Fig. 11, the mask used is actually instance mask, instead of semantic mask (two monitors have different masks). This should be clarified in Sec. 3.3. \n\n3. Typos: \n    * L123: of are -> are\n    * L294: teacher’s are -> teacher’s parameters are", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well-written and easy to follow.\n\n2. Based on MVSplat, a feed-forward 3DGS method, SnD is more efficient than previous methods that require optimization. \n\n3. SnD outperforms baselines in various downstream tasks, including depth estimation, normal estimation, and semantic segmentation.", "weaknesses": "1. The authors should add an ablation study without student-teacher framework. For example, a potential experiment is finetuning a model with feature rendering loss, similar to Fit3D. Currently, it is unclear to me why student-teacher framework is necessary. \n\n2. The improvement that mask-aware feature lifting brings is not explicit.\n\n3. Quantitative evaluation of multi-view feature correspondence should be added, instead of using visualization only.", "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": "owQheNb6t0", "reviewer": "Reviewer_MPDZ", "summary": "The method extends DINO-style self-distillation by integrating a feed-forward 3D reconstruction pipeline. Two context views are used to build a 3D Gaussian scene via a pretrained model (MVSplat), while the teacher’s 2D features are lifted onto this geometry to form 3D feature Gaussians. These are rendered from a novel viewpoint to generate a 3D-aware supervisory map for the student. Mask-aware feature lifting preserves semantic boundaries, and semantic blending regularizes noisy regions. The student, given only the target image, learns to match the rendered teacher features through a cross-entropy loss, with the teacher updated via EMA. This yields 2D features enriched with 3D geometric awareness.", "questions": "- How does the proposed approach perform when used as a drop-in replacement for DINOv2 within task-specific architectures such as VGGT?\n- The evaluated downstream tasks (e.g., depth estimation) are interesting, but 2D foundation models are rarely applied to them directly. It would be helpful to also include results from task-specific state-of-the-art methods on the same benchmarks as reference to see where the proposed approach stands in comparison.\n- How does the method compares to the missing baselines?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method demonstrates clear performance gains over DINOv2, the baseline framework it builds upon.\n- The use of a feed-forward 3D reconstruction module to inject geometric awareness into 2D foundation models seems a promising and timely research direction.", "weaknesses": "The evaluation setup is limited, as DINOv2 is seldom used directly for downstream tasks. It typically serves as a feature backbone for task-specific heads. Hence, direct comparison against DINOv2 provides a very limited insight. Demonstrating improvements when replacing DINOv2 with the proposed method within state-of-the-art pipelines (e.g., VGGT) would make the results substantially more compelling.\n\nMany recent methods seem to be missing from the comparisons:\n- DINOv3\n- Sarıyıldız, M.B., Weinzaepfel, P., Lucas, T., de Jorge, P., Larlus, D. and Kalantidis, Y., 2025. DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 30084-30094).\n- Govindarajan, H., Wozniak, M.K., Klingner, M., Maurice, C., Kiran, B.R. and Yogamani, S., 2025. CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation. BMVC 2025 (on arxiv since March)\n- Huang, X., Wu, J., Xie, Q. and Han, K., 2025. MLLMs Need 3D-Aware Representation Supervision for Scene Understanding. arXiv preprint arXiv:2506.01946.\n- You, Y., Li, Y., Deng, C., Wang, Y. and Guibas, L., 2024. Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning. ICLR 2025\n\nMinor thing:\n- Section 3.3 closely resembles the feature lifting strategy used in OccamLGS; the authors should probably cite it.", "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": "NSk1yNPQzc", "reviewer": "Reviewer_sbNt", "summary": "The work proposes a distillation framework to enhance 3D awareness of a self-supervised encoder. The idea is to leverage a feed-forward model for multi-view 3DGS inference (pre-trained) to synthesise a 3D-consistent target for a student network. Specifically, an EMA teacher uses segmentation masks to fuse its patch-level representation into 3DGS by guided usampling. The resulting high-resolution feature grid is downsampled for student distillation.\nExperiments on six datasets demonstrate notable improvement of the base model across four tasks: depth estimation, multi-view correspondence, surface normal estimation and semantic segmentation.", "questions": "* What aspect of the framework design encourages improvement on view co-variant tasks (e.g. depth, surface normal estimation)?\n* How would the most basic variant of the proposed framework (no mask-guided usampling, no blending, no EMA) perform?\n* How does the approach work around the issues of a “least-squares compromise” and not “enforcing feature similarity at corresponding points” from previous work?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* Creating a 3DGS representation on-the-fly for 3D-aware feature distillation is a very nice idea.\n* The experimental scope and the demonstrated results are laudable. The approach demonstrates consistent and often significant improvement across all tasks.\n* The approach is relatively simple and leads to representations, which can be used standalone (without the need for concatenating them with the baseline features in evaluation).", "weaknesses": "* Overall, it is unclear where the gains come from. Conceptually, the approach designs a view-invariant representation and uses segmentation masks. This explains the gains on semantic segmentation, which requires view-invariance, but not the 3D-aware tasks (e.g. depth), where the representation is covariant with the camera pose. \n* The ablation study is not very informative, also in the context of the above point. Even the worst configuation here (“without blending”) already outperforms the baseline DINOv2 model by a significant margin. All other configuations lead only to a marginal decrease in the task accuracy. I would like to see the most basic configuration instead: a fixed teacher model, without masked upsampling and blending.\n* Related to the above points, introduction stresses “a least-squares” compromise from previous work, citing multi-view inconsistency of the base feature representation. The argument seems to  imply that there is no feature averaging in this work. However, this is exactly what Eqs. (2,4) do. The only difference is that this averaging occurs within each segment, which is not particularly critical (as experiment in Tab. 5B shows)", "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)