Dens3R: A Foundation Model for 3D Geometry Prediction
Authors: Xianze Fang
Jingnan Gao
Zhe Wang
Zhuo Chen
Xingyu Ren
Jiangjing Lyu
Qiao-Mu Ren
Zhonglei Yang
Xiaokang Yang
Yichao Yan
chengfei lv
Venue: ICLR 2026 Poster (2026)
Keywords: Visual Foundation Model
3D Geometry Prediction
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in dense 3D reconstruction have led to significant progress, yet achieving accurate unified geometric prediction remains a major challenge. Most existing methods are limited to predicting a single geometry quantity from input images. However, geometric quantities such as depth, surface normals, and point maps are inherently correlated, and estimating them in isolation often fails to ensure consistency, thereby limiting both accuracy and practical applicability. This motivates us to explore a unified framework that explicitly models the structural coupling among different geometric properties to enable joint regression. In this paper, we present Dens3R, a 3D foundation model designed for joint geometric dense prediction and adaptable to a wide range of downstream tasks. Dens3R adopts a two-stage training framework to progressively build a pointmap representation that is both generalizable and intrinsically invariant. Specifically, we design a lightweight shared encoder-decoder backbone and introduce position-interpolated rotary positional encoding to maintain expressive power while enhancing robustness to high-resolution inputs. By integrating image-pair matching features with intrinsic invariance modeling, Dens3R accurately regresses multiple geometric quantities such as surface normals and depth, achieving consistent geometry perception from single-view to multi-view inputs. Additionally, we propose a post-processing pipeline that supports geometrically consistent multi-view inference. Extensive experiments demonstrate the superior performance of Dens3R across various tasks and highlight its potential for broader applications.

Published as a conference paper at ICLR 2026
A FOUNDATION MODEL FOR 3D GEOMETRY PREDICTION
DENS3R:
Xianze Fang Qiaomu Ren
Jingnan Gao
Zhe Wang'
Zhuo Chen
Xingyu Ren²
Jiangjing Lyu'st
Zhonglei Yang!
Xiaokang Yang²
Yichao Yan 2.
Chengfei Ly!
Alibbaaa Group.
Shanghai Jiao Tong University.
https :ttps://ght htops://g-lnona github. iD/Den/3R/
Unposed Images Input
High Quality 3D Pointmap
Unified Geometric Dense Prediction
Generalized Input
Robust Dense Prediction
Downstream Applications
Multiviiww
Multi-resolation
Indoor
Outdoor
Object
Surface Reconstruction Segmentation
Figure I: Dens3R is feed-forward visual foundation model that takes unposed images input and outputs high-quality 3D pointmap with unified geometric dense prediction. Dens3R also accepts generalized inputs, supporting both multi-view and multi-resolution inputs. As versatile backbone, Dens3R achieves robust dense prediction under several scenarios and can be easily extended to downstream applications.
ABSTRACT
Recent advances in dense 3D reconstruction have led significant progress, yet achieving accurate unified geometric prediction remains a major challenge. Most existing methods are limited to predicting a single geometry quantity from input images. However, geometric quantities such as depth, surface normals, and point maps are inherently correlated, and estimating them isolation often fails to en- sure consistency, thereby limiting both accuracy and practical applicability. This motivates us to explore a unified framework that explicitly models the structural coupling among different geometric properties to enable joint regression. In this paper, we present Dens3R, a 3D foundation model designed for joint geometric dense prediction and adaptable
wide range of downstream tasks. Dens3R adopts a two-stage training framework to progressively build a pointmap repre- sentation that is both generalizable and intrinsically invariant. Specifically, we design lightweight shared encoder-decoder backbone and introduce position- interpolated rotary positional encoding maintain expressive power while en- hancing robustness to high-resolution inputs. By integrating image- pair matching features with intrinsic invariance modeling. Dens3R accurately regresses multi- ple geometric quantities such as surface normals and depth, achieving consistent geometry perception from single-view propose a post-processing pipeline that supports geometrically consistent multi- view inference. Extensive experiments demonstrate the superior performance Dens3R across various tasks and highlight its potential for broader applications.
multi-view inputs. Additionally, we
Equal Contribution. t: Project Leader. t: Corresponding Author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recovering 3D geometric structures from static images is a long-standing and fundamental prob- lem in computer vision. Classical approaches, such as Structure-from-Mortiee View Stereo (MVS), demonstrate strong performance in controlled settings and have been widely adopted in a broad range of 3D reconstruction applications. However, in unconstrained scenar- ios-where where camera intrinsics, extrinsics, or viewpoint information are unavailable -achieving ac- curate and dense geometric prediction remains highly challenging. These conditions demand more generalizable and robust solutions capable of handling diverse and unstructured visual inputs.
(SIM) and Multi-
Existing methods for dense geometric prediction primarily fall into two categories. The first category mostly adopts generative models, utilizing strong image priors from pre-trained diffusion models or large-scale training datasets for dense prediction. For example, GenPercept Xu et al. (2025) is used for depth prediction, and StableNormal Ye et al. (2024) for normal estimation. This raises a key issue: while image generation tasks typically benefit from their inherent ambiguity and multi-modal output characteristics, geometric prediction is fundamentally different. Geometric prediction is es- sentially a deterministic task that needs closely reflect the structural information of the underlying scene. Moreover, the pixel continuity and spatial smoothness required by geometric representations difficult naturally obtain through standard diffusion sampling mechanisms without structural constraints. Therefore, the direct application of diffusion models in geometric regression tasks faces significant challenges, especially such tasks where a strict one-to-one correspondence between input and output needs to be maintained. Based on this, we adopt a regression-oriente -rieente framework to construct geometric mapping models in more efficient and interpretable way. Furthermore, the aforementioned methods mainly handle only one geometric quantity prediction and cannot general- ize to output multiple geometric quantities in a single forward pass. The second category includes DUS(3R Wang et al. (2024) and its follow-up works Leroy et al. (2024); Wang et al. (2025b;a); LAN et al. (2026). These methods use regression models that can regress 3D point map representations with geometric properties, applied to dense prediction, including image pair matching and depth estimation. However, these methods typically focus on a single prediction task, and other geometric quantities suffer severe performance degradation due to representation influences.
This raises a natural question: can we build a unified model that simultaneously regresses multiple geometric quantities with high quality? We observe that existing methods like DUSt3R, when han- dling dense geometric regression tasks, overlook a crucial geometric information-surfae Traditionally, normals have been used to add high-frequency details to rough geometric structures to enhance rendering quality. However, our research finds that introducing normal information dur- ing geometric prediction can significantly improve the accuracy of point maps, resulting in more detailed and structurally consistent 3D representations. This is mainly because: 1) From the per- spective of normal prediction, the inherent image pair matching capability in dense vision backbone networks helps alleviate monocular ambiguity and improve the stability and accuracy of normal prediction; 2) From the feature modeling perspective, normals possess good intrinsic invariance, which simplifies the mapping learning process and aids in model convergence and generalization. This modeling approach enables the model to simultancously predict multiple geometric quantities (such as depth, normals, and point maps) from single view, effectively reducing dependence on multi-view supervision and simplifying the training process. However, training such a multi-task, multi-output 3D foundation model still faces significant challenges. Geometric quantities are tightly coupled, and how to coordinate these relationships to achieve optimal overall performance requires carefully designed training strategies and architectural support.
normals.
In this paper, we present Dens3R, a foundation model for high-quality geometric prediction. To this end, we design two-stage training framework that gradually builds a versatile pointmap rep- resentation, which generalizes well to various downstream tasks. Specifically, we first construct a dense vision backbone network with multi-task prediction capabilities. This network adopts a shared encoder-decoder architecture, which significantly reduces model parameters while maintaining ex- pressive power. To accommodate high-resolution inputs, we introduce positional encoding, which effectively mitigates prediction degradation caused by increased input resolution. For the training strategy, we propose a novel two-staged approach. In the first stage, the model leverages image pair matching features to leam scale-invariant point maps, capturing con- sistent spatial geometric structures across viewpoints. Subsequently, to fully exploit the one-to-one mapping property in normal estimation, extend the pointmap representation into intrinsically
rotary
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invariant form. This allows the model to independently attend to each viewpoint, thereby improving the accuracy of normal prediction. The learned geometric structures also assist in estimating other geometric quantities, such depth, thereby simplifying their training processes. Finally, we design a simple and efficient post-processing pipeline that supports multi-view inputs during inference, which enhances the geometric consistency of the model in real-world applications. In summary, make the following contributions:
We introduce Dens3R, a dense 3D visual foundation model that demonstrates high-quality performance in various 3D tasks including pointmap reconstruction, depth estimation, normal prediction and image matching under several benchmark evaluations.
We design a novel training strategy with the intrinsic-invarian pointmap and shared Encoder-Decoder visual backbone incorporate surface normals in unconstrained image- based dense 3D reconstruction, simplifying the training complexity of other 3D quantities and achieving better results without requiring excessive computation resources.
We employ a accuracy at higher resolutions and support multi-resolution inputs.
rotary positional encoding to preserve prediction
Extensive experiments on various benchmarks showcase our high high-quality predictions of 31) geometric quantities, which further enable a wide range of applications.
2
RELATED WORKS
2.1 MONOCULAR DEPTII AND NORMAL PREDICTION
Monocular depth prediction has been extensively investigated and demonstrates strong capability in providing geometric priors for multitude of downstream tasks like image understanding and 3D reconstruction. The earliest pioncering researchers Bhat et al. (2021; 2023); Eigen et al. (2014); Yin et al. (2023); Hu et al. (2024); Piccinelli al. (2024) addressed this issue by estimating depth with a metric scale. These methods usually rely heavily on data from specific sensors, which re- stricts the applicability and deteriorates the performance when confronted with complex scenes. Subsequently, deep learning approaches involve predicting relative depth either through direct re- gression Chen et al. (2016; 2020); Godard et al. (2019); Li & Snavely (2018a); Ranftl et (2022); Yang et al. (2024a;b) or via generative modeling based on diffusion priors Fu et al. (2024); Gui et al. (2024); Ke et al. (2024); Wan et al. (2023). While monocular depth estimation has made signifi- cant strides, accurate 3D shape reconstruction from depth maps remains fundamentally dependent on precise camera intrinsic parameters. Meanwhile, normal maps serve as a supervision for neural scene representation, bridging 2D and 3D worlds. 'hee accurate estimation of the normal map can open up broader applications like material decomposition and relighting. On one hand, regression- based methods Eftekhar et al. (2021); Bansal et al. (2016); Wang et al. (2015); Ranftl et al. (2021) utilize large-scale training datasets for robust estimation. DSINE Bae & Davison (2024) proposes to leverage the per-pixel ray direction and try model the inductive biases for surface normal estima- tion correctly. On the other hand, diffusion-based methods Long et al. (2024); Fu et al. (2024); Ye al. (2024) adapt the pretrained diffusion model a geometric cues predictor. Geowizard includes a geometry switcher to disentangle mixed-sourced data into distinct sub-distributions for normal prediction. StableNormal repurposes the diffusion model for deterministic estimation tasks and can estimate sharp normals steadily. Nevertheless, these normal estimation methods often suffer from monocular ambiguity, leading to inaccurate and inconsistent results for complex scenes. In contrast, our method allows the communication between 3D geometric representation and normal prediction without known camera poses. This not only resolves the ambiguity but also achieves accurate 3D reconstruction with accurate normals.
2.2 IMAGE PAIR MATCHING 3D
Dense matching Edstedt et al. (2023; 2024); Efe et al. (2021); Melekhov et al. (2019); Truong et al. (2020; 2021; 2023); Zhu & Liu (2023); Sarlin et al. (2020); Sun et al. (2021) has been proved to be effective in many scenarios and results in top performance in many benchmarks. However, these approaches cast matching as a 2D problem, which restricts the application for visual localization. Thus anchoring image correspondence in 3D space is essential when these 2Dsba methods fall
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Images
Visual Transformer Backbone
Prediction Heads
Prediction Outputs
Hoad
Vir Enonder
Transformer Decoder
Fod,
Head
MALTA
weights
Weights
Headeeua
VIT Encoder
Transformor Decoder
Headwwskie
Feoz
Pointmap
Depth
Matching
Training Modules A
One-to-baany
Starlodessurie dant Puintrap
Training Modules ib
One-to-One
Inrissictmariant
Training Modules
db
Encoder
Decoder
Pess
Out,
Normal
Head
Head
Headgace
Headhwschnn
Headguod
Head.umui
Feng
Outy
Head
Training Stage 1: t:Scall-Invariant Pointmap
Training Stage Intrinsic-levariant Pointmap
Heads Training
Figure 2: Overview of Dens3R. We propose Dens3R, dense visual transformer backbone featuring a shared encoder-decoder architecture and multiple task-specific heads for geometric prediction. To train this foundation model, we adopt two-stage strategy. In Stage 1, learn scale-invariant pointmap by enforcing cross-view mapping consistency across multiple viewpoints. In Stage 2, incorporate surface normals and leverage one-to-one correspondence constraints transform the representation into an intrinsic-invariant pointmap. Built upon this unified backbone, additional geometric prediction heads and downstream task branches can be seamlessly integrated support wide range of applications.
short. Early methods Bhalgat al. (2023); He al. (2020); Wang et al. (2020a); Yao et al. (2019); Yifan et al. (2022); Zhou et al. (2021); Toft et al. (2020) leverage epipolar constraints in order to improve accuracy or robustness. Recently, researchers Zhang et al. (2024); Wang et al. (2023a) leverage diffusion models for pose estimation and demonstrate promising results by incorporating 3D geometric constraints into estimation formulation. MASt3R Leroy et al. (2024) retrieves cor- respondences via 3D reconstruction from uncalibrated images by explicitly training local features for pairwise matching. However, MASt3R only grounds image-pair matching and overlooks other geometric predictions like depth and normal, while Dens3R achieves unified geometric predictions and better matching.
2.3 DENSE UNCONSTRAINED GEOMETRIC REPRESENTATIONS
Neural scene reconstructions Mildenhall al. (2020); Wang et al. (2021a); Kerbl et al. (2023); Bar- ron et al. (2021); Martin-Brualla et al. (2021); Barron et al. (2023); Yariv et al. (2021); Lu et al. (2024); Yu et al. (2024); Wang et al. (2023b) usually require the camera intrinsic parameters and poses for optimization. The reconstruction quality of these methods is highly dependent on the curacy of the camera intrinsics and poses. Later methods Smart et al. (2024); Ye et al. (2025); Hong al. (2024) propose optimize the scene without known camera poses, but these methods usually take longer time and sacrifice reconstruction quality. To bypass estimation of camera parameters and poses, DUS(3R Wang et al. (2024) proposes directly map two input images in single forward pass, leading to more straightforward geometry representation. Subsequently, Spann3R Wang & Agapito (2025) and Fast3R Yang al. (2025) augment DUSt3R to process an ordered set images. MoGe Wang et al. (2025b) further proposes affine-invarinl pointmaps for monocular geometry es- timation. VGGT Wang et al. (2025a) utilizes pointmaps and multiple prediction heads to predict geometric quantities from multi-view images input. However, the aforementioned methods overlook the normal attribute and fall short in prediction for complex scenarios. In contrast, our model takes advantage of pointmap representation and employs several prediction heads including the normal head to achieve unified geometric predictions.
METHOD
This work aims to utilize a single model to predict various geometric data from unconstrained im- ages, including 3D pointmaps, depth maps, normal maps, and image-pair matching. To this end, we built a backbone network based on dense visual transformers and designed input configurations that adapt multi-resolution and multi-view requirements (Sec. 3.1). Since achieving accurate results through direct training with a single model is challenging, we adopted a two-stage training approach. In the first stage, train the backbone and heads to obtain scale-invariamt pointmaps. In the second
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stage, fine-tune the backbone on this foundation to obtain Finally, we further fine-tune the prediction heads for each downstream task to adapt to different ap- plication scenarios. Meanwhile, extending the model inputs to multi-view images in the inference stage significantly improves the overall inference quality. (Sec. 3.3).
pointmaps (Sec. 3.2).
3.1 MODEL FORMULATION
Shared Backhone. Motivated by recent advances in 3D vision Wang et al. (2024); Leroy et al. (2024); Wang et al. (2025a); Jin et al. (2025), we aim to build a foundation model capable of pre- dicting diverse geometric quantities across different scenes and tasks. To this end, we adopt dense visual transformer as the backbone, learning from rich 3D annotated data. Given an image pair of image sequence (T.)²= E x Dens3R's 's dense visual transformer is a function f that maps the input to corresponding set of 3D quantities per frame:
(I) where C. E R2 is the camera parameters including both intrinsics and extrinsics, D, E R is the depth map, N₁ R3*H* is the normal map, and M, E RCXHxW is the mmae--aiir-mtchinn with C-dimensional features.
P,, D., N,, M,) = f((4))=1),
The overall architecture is illustrated in the upper part of Fig. 2. Similar to prior DUSt3R- Rbased approaches Wang et al. (2024); Leroy et al. (2024); Wang et al. (2025a;b), we first employ shared- weight encoder process input image sequences and extract image features Fea,, which are then fed into the decoder. Unlike previous works, our approach introduces a novel weight-sharing mech- anism within the decoders, allowing the backbone to better capture spatial relationships across view- points and to model the holistic 3D scene structure. Given the need to predict wider range of geo- metric outputs, this design also significantly reduces memory and computational overhead, keeping the training and inference efficient. Moreover, the shared-weight strategy facilitates high-resolution input processing while effectively preventing memory overflow.
Multi-resolution Input. Existing methods represented by DUS(3R perform excellently at fixed res- olutions (such as 512), but their prediction accuracy significantly decreases when processing higher- resolution inputs. The main challenge for this issue lies in the rotary positional encoding (RoPF) used in their ViT structure, which becomes unstable when inferring images beyond the training res- olution range. Inspired by context window extension techniques in LLMs Chen et al. (2023), we incorporate the position-interpolated RoPE into the ViT as simple yet effective improvement. We adapt the idea from context window to image resolution in the image domain, addressing the insta- bility at higher resolutions. Considering the smooth properties of trigonometric functions in RoPE, interpolation is more stable than direct extrapolation when handling high resolutions. Specifically, let the original RoPE be R, the input sequence length be L, and for any RoPE embedding vector x, we obtain new encoding representation R' through interpolation. That is:
(a m) = R(x, mL T
where m is the position index and L' is the longer sequence. This position-ineerpolation encod- ing strategy significantly enhances the model's robustness under high-resolution inputs, effectively avoiding the performance degradation caused by RoPE extrapolation.
3.2 FOUNDATION MODEL TRAINING
The main challenge in training 3D geometric foundation models lies in the coupling among multi- ple prediction outputs, where mutual interference often leads to performance degradation. Existing methods typically focus on only one or two geometric tasks, resulting in poor generalization to oth- ers. To this end, we propose to build upon a unified geometric representation since all geometric rep- resentations are inherently interconvertible. We adopt two-stage training paradigm, progressively learns a strong geometric prior, which can be efficiently transferred to a variety of 3D geometry prediction tasks via lightweight fine-tuning.
Scale-Invariant Pointmap Training, In the first stage, we train the VIT backbone, pointmap head, and matching head to obtain a scale-invariant pointmap P.. Following MASt3R'ss Leroy et al. (2024), we adopted (1) local 3D regression loss Epe loc (2) Global 3D Regression Loss Cps glh (3) Pointmap Normal Loss Lpe (4) Pixel Matching Loss Lmutch- The details are as follows:
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(1) Local 3D Regression Loss Cpa For predicted camera, the local 3D regression loss to quantify the pointmap in its coordinate frame. We apply mask derived from the ground- truth data to the pointmap and only evaluate the valid points when calculating the loss. We also employ normalization factor to handle the scale ambiguity between ground-truth and the predicted pointmaps. We set the factor že as the average distance of all valid points in Uzh camera coordinate frame to the origin:
2v
plu masked
+
p2.v masked
{1,2},
(3)
2, pLe masked
+
p2.e masked
v E {1.2}-
where 2, is the corresponding factor of the ground-truth. Then the local 3D regression loss can be formulated as:
1
Cpes loc
masked
masked Zn
U {1,2),
where pn,m denotes the pointmap from camera expressed in the coordinate frame of camera m.
(2) Global 3D Regression Loss Lps.gib- The global 3D regression loss applied to quantify the
pointmap expressed in another camera's coordinate frame. This loss function simultaneously op- timizes for objectives. not only constrains the network fit the pointmap shape of the image, but also aligns the pointmap to another paired image. The global regression loss is formulated as:
1
1
Cle
p masked
-
puit macked v, (1,2), 1 11,
(5)
where and & is the normalization factor of the pointmap and the ground-truth.
(3) Pointmap Normal Loss Lpss n- To train an intrinsic-invariant pointmap from the scale-invariant pointmap, we use a pointmap normal loss to encourage the pointmap learn smooth surface and sharp edge, making the pointmap perceives the normal information and the intrinsic-invariant property- Suppose N is the ground-trutt -ruuh view-space normal expressed in its own camera coordinate frame and N is the ground-trute normal expressed in another camera coordinate frame, the pointmap normal loss is the absolute error loss between the transformed normal and the ground-truth normal:
Sps (N Nrw.0) + (N ,N,"),, v,
where the N is the normal transformed from the local pointmap and No. is the normal trans- formed from the global pointmap.
(4) Pixel Matching I Loss Lmaach- We utilize the pixel matching loss proposed in MASt3R Leroy et al. (2024) to learn accurate image-matching. This loss is based on the infoNCE Oord et al. (2018) loss and ensures that each pixel's descriptor in the first image match at most one pixel's descriptor in another image. Suppose Mi (i.j) is the set of ground-trute -ruth correspondences where the ith pixel in the first image matches the jen pixel in another, the loss can then be formulated as:
Σ log sT,1
8T(1,)
Lmatch =
ExEPi Sy (k, + log ΣkEPa 87 ki,
(JJEM
s-(i,) exp D DJ).
where is hyper-parameter, and D; and Dj are the corresponding descriptors in each image.
With the above losses, we summarize the training objective as:
Cetagel = Cps loc + Cp glb + 1p2 + My
(8)
where the loss weights 7h. 72, and T73 are set as 1.0, 0.1 and 0.075, respectively. After training, obtained scale-invariant pointmap capable of capturing rich spatial information. However, as shown in Fig. 3, the accuracy of normals obtained directly from the pointmap at this stage is still not ideal.
Intriisi--Ivarrinn Pointmap Training. Although the paint-based representation learned in the first stage achieves good performance, remains limited in its ability to generalize to other

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Input
Pointmap Normal
MoGe Normal
Ours
Figure 3: Normal comparison. We demonstrate that the normal derived directly from the scale- invariant pointmap and MoGe both are not accurate enough.
tasks- particulllyll surface normal estimation. Existing methods often struggle with monocular ambiguity normal prediction, leading to inaccurate and inconsistent results.
To this end, expand the pointmap representation in the second stage. proposing intrinsic- invariant pointmap. This representation inspired by the affine-invrriant formulation of MoGe Wang et al. (2025b), which disentangles shift factors from pointmaps. For a given depth map, multiple valid solutions can exist due to shift/scale ambiguities the 3D coordinates. In contrast, surface normals provide an intrinsic, locally deterministic geometric property; given an underlying surface, there is an exactly one corresponding normal map, as also discussed in works such as Sta- bleNormal Ye et al. (2024) and DSINE Bae & Davison (2024). We use this property improve geometric consistency by anchoring the pointmap to more deterministic geometric interpretation, which also improve the stability normal estimation effectively.
Specifically, we introduce high-quality normal supervision based on the first stage's point map, and jointly fine-tune the encoder-decoder module, point map prediction head, and newly added normal prediction head to achieve end-to-end optimization. In terms of supervision mechanism, we adjusted the initial "one-to-many" mapping (one image corresponding to multiple view supervisions) to a "one-to-one" mapping, enabling the model to independently optimize normal prediction under single viewpoint. This strategy not only significantly reduces the ambiguity brought by multi-view supervision but also simplifies the training process and improves training efficiency and stability. In addition, it enables the model to independently optimize geometric prediction under a single viewpoint and to leverage additional high-quality monocular data during training.
We observe that the commonly-used confidence loss in previous works Wang et al. (2024); Leroy al. (2024); Wang et al. (2025a) tends cause models to ignore complex scenarios such re- flective surfaces and low textured areas. However, naively removing the loss without additional con- straints leads to degraded performance, since previous models rely heavily on confidence weighting for point-view regression. In contrast, by utilizing the deterministic nature of normals. we obviate the need rely on additional views, which further enables stable and accurate prediction.
For detailed implementation, we explicitly connect normal to the pointmap representation, that is
P." P. ① n,
where + represents feature concatenation operation. The normal prediction head is connected after the initial point map training completed, allowing the model to consistently output coherent mal mappings from the same input image, thereby internalizing this intrinsic invariance in the point map and maintaining geometric consistency across different views.
In the second stage, we add a normal loss L, for finetuning.
(5) Predicted Normal Loss L.. Apart from the Aatrinsic-ivvarinn pointmap, also design normal head to predict the view-space normal of each frame in input image pairs. We also use the L loss supervise the normal prediction:
C. = (NV"
N":"), [[""",
E {1,2).
(10)
where N is the ground-truth normal and N is the direct prediction of the normal prediction head. The complete training objective for training stage as follows:
Estage2 Epts.oe + >1 Eps.gh + >2Zpusn AzG...
(1I)
where the loss weights 71 A2. and X are
1.0, 0.1 and 1.0, respectively.
To further improve the performance of Dens3R on high-resolution inputs, we introduce a coarse-to- fine training strategy. Specifically, we first fine-tune the model 512 resolution images establish

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Input
Ours
StableNormal
GeoWizard
Lotus
DSINE
DIOFF
Qualitative comparison normal prediction. DensäR generates more accurate and de- tailed normal maps than previous methods for both object-centric and unbounded scenes,. Our method is capable of predicting accurate normals for reflective surfaces and in backgrounds.
Figure 4:
stable geometric prior, and then fine-tune on 1024 resolution images to further improve the pre- diction accuracy. In addition, combining high-resolution data also significantly improves the fidelity of point-based based representations, ultimately enhancing the overall quality of dense prediction.
3.3 MODEL INFERENCE
Heads Training. After training, we fine-tune it for downstream tasks by optimizing task-specific prediction heads on top of the frozen backbone network. Training only these DPT heads enables extension to various tasks such as depth estimation, normal estimation, matching estimation, and even segmentation and object detection. It is noteworthy that the depth head instantiated in Stage 1, similar to the depth branch in MAS13R Leroy et al. (2024), and is trained jointly within our multi- task objective. At the model architectural level, however, Dens3R differs from DUSt3R Wang et al. (2024) and MAS13R Leroy al. (2024) by using shared decoder rather than separate decoders for main and a reference view. This design removes the need to explicitly define main and reference views and alleviates the reliance selecting fixed reference view. It also improves training efficiency, since predictions are obtained from a single forward pass instead of two passes with view swapping in previous 3R-based methods. Building this, after introducing the one-to-one mapping in Stage 2, depth prediction can be optimized at the single-view level. Similarly, we can fine-tune all the DPT heads separately with additional monocular datasets in this final heads-training stage.
Multi-view Inputs. To enable Dens3R efficiently process multi-view inputs during inference, design simple yet effective post-processing step. This step ensures both computational efficiency in multi-view data processing and the consistency and accuracy of results. Specifically, based on Dens3R's 's high-precision image pair matching predictions, we establish geometric mappings be- tween different viewpoints by constructing and optimizing a dense correspondence network across views. This approach effectively guides the model to understand geometric consistency between multiple viewpoints and accurately captures spatial relationships between views. Additionally, significantly improves the performance and stability of multi-view processing. In practice, first compute matches in one-versus-al all strategy using our model, and then triangulate these matches to obtain multi- view point clouds, following the MASt3R pipeline Leroy et al. (2024). We can also uti- lize the MASt3R-SfM for surface reconstruction. This pipeline inherits MASt3R's 's ability to handle large-scale scenes with hundreds of images.
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NYUv2
ScaiÑes Mcd
Sindel
DIODE
Methed
Maan
Mall
Moan
Mod
DSINE
18.6
9.9
56.1
186
9.9
56.1
18.8
8.3
64.1
14.9
28.1
31.5
23.0
14.5
19.6
Losus
17.5
866
58.7
18.1
58.2
19.2
66.2
35.7
20.5
32.9
GeoWirard
20.4
47.0
21.4
139
57.1
19.3
58.8
41.6
118
23.0
24,0
StoteNormal
197
53.0
181
56.0
173
8.1
66.7
35.0
19.5
26.9
36.1
Ours
161
169
86.0
160
722
300
21.4
283
43.0
Table I: Quantitative comparison of normal prediction. We report the mean and median angular emros with each cell colored to indicate the best and the second Dens3R achieves accurate normal prediction for both indoor and outdoor scenes. "We utilize Lotus-G for fair comparison.
Mean AUC85 f
Real AUC
Simulate AUC
Method
GL3
BLE
ET1
ETO
KIT
WEA
SEA
NIG
MUL
SCE
ICL
GTA
SIFT
31.8
43.5
33.6
49.9
48.7
35.2
21.4
44.1
14.7
33.4
7.6
14.8
43.9
SuperGiuue
34.3
43.2
34.2
58.7
61.0
29.0
28.3
48.4
18.8
34.8
2.8
15.4
36.5
LoFTR
39.1
50.6
43.9
62.6
61.6
35.9
26.8
47.5
17.6
41.4
10.2
25.6
45.0
DKM
51.2
63.3
53.0
73.9
76.7
43.4
34.6
52.5
24.5
56.6
32.2
42.5
61.6
ROMA
53.2
61.8
53.8
76.7
82.7
43.2
36.7
53.2
26.6
60.7
33.8
45.4
64.3
MASER
59.9
57.8
52.3
66.2
78.1
46.2
52.8
70.5
43.7
70.1
53.9
60.1
67.7
Ours
64.5
61.3
59.2
74.7
81.1
55.6
57.4
71.7
50.4
71.3
53.7
66.3
71.7
Table 2: Benchmark on image matching on ZEB dataset. We report the AUC values with each cell
colored to indicate the best and the second
4
EXPERIMENTS
4.1 NORMAL AND MATCHING PREDICTION
We evaluate our Dens3R on several surface normal prediction datasets that include both indoor and outdoor scenes. We compare our method with regression-basee methods such as DSINE Bae & Davison (2024) and diffusion-based methods like StableNormal Ye et al. (2024), GeoWizard Fu et al. (2024) and Lotus He al. (2025). Quantitative results are shown in Tab. 1, where Dens3R outperforms other methods across multiple benchmarks. Qualitative comparisons Fig. 4, also demonstrating that Dens3R generates more accurate and detailed normal maps. On the DIODE dataset, method produces more accurate normals for reflective surfaces (e.g., win- dow) and finer details in backgrounds and tree structures. On in-the-wild scenes, Dens3R handles both object-centric and unbounded scenarios, producing more stable and intricate surface normals. Our method effectively reduces the ambiguity from monocular estimation, enabling more accurate and detailed predictions across various settings.
provided in
For the image-matching task, we evaluate our method on the ZEB benchmark as shown in Tab. 2. We compare our method with previous dense matge-natching methods and MASt3R Leroy et al. (2024). It can be seen that our method yields higher accuracy and surpasses previous methods across nearly all datasets, demonstrating our superior performance across various evaluation protocols.
4.2 POINTMAP AND DEPTHI PREDICTION
For monocular depth prediction and pointmap prediction, we evaluate our model on several datasets containing both indoor and outdoor scenes. We compare our method with MoGe Wang et al. (2025b), VGGT Wang al. (2025a), MASt3R Leroy al. (2024) and DUSt3R Wang et al. (2024). The qualitative comparison is shown in Fig. 5. Our method achieves high-quality pointmap predic- tion and depth estimation with the intrinsie-invariant pointmap and the novel training strategy. As for pointmap prediction, MoGe and VGGT often fail to recover depth for reflective surfaces and tend to produce flattened pointmaps background regions. In contrast, our method accomplishes to predict accurate depth with high-quality pointmaps. Moreover, Dens3R yields more stable and high-quality predictions than MASt3R. Our method also generates more accurate depth maps than DUSt3 which can be reflected from the depth predictions for the Chandeliers.
CONCLUSION
We propose Dens3R, a 3D foundation model for dense geometric prediction that jointly regresses multiple geometric quantities, including depth, surface normals, and pointmaps, from unconstrained

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Published as a conference paper at ICLR 2026
Input
Ours
MoGe
VGGT
MASER
DUSIER
Figure 5: Qualitative comparison of depth maps and pointmaps. We compare method with previous DUSt3R-based methods and demonstrate high-quality depth prediction results. Dens3R also reconstructs more stable and accurate pointmap than previous methods.
image inputs. Unlike previous approaches that estimate geometry in isolation, Dens3R explicitly models the structural coupling among these properties to ensure consistency and improves overall accuracy. We utilize two-stage training framework with coarse-to-fin strategy and build an curate intrinsic-invariant pointmap representation. In addition, we design lightweight encoder- decoder architecture and resttion-iteeroolated rotary positional encoding enable scalable and high-fidelity inference for high-resolution inputs. Moreover, Dens3R incorporates a geometrically consistent post-processing pipeline for multi-view inputs. Extensive experiments demonstrate our superior performance across various 3D prediction benchmarks and highlight the potential as ver- satile backbone for broader downstream applications.
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A APPENDIX
In addition to the results presented in the main paper and appendix, we also provide further exper- iments and visualizations the Supplementary Materials, including unified geometric prediction from monocular inputs, image-matching visualizations, and multi-view reconstruction results.
A.1 ABLATION STUDY
We present high-quality geometric predictions for high-resolution inputs and various scenarios in Fig. and Fig. 7. We then conducted comprehensive ablation studies for our key components: the position-interpole interpolated rotary positional encoding, the fine training strategy.
training and the coarse-to-
NYUv2
ScanNet
IBims
Sintel
outdoor
Mcan
811.25
Mean
611.25
Mcan
611.25
Mcan 4
811225 f
Mcan J.
611250
IIT
17.8
50.6
18.6
49.4
20.2
56.8
35.9
18.9
23.5
33.7
w/o C2F
17.6
50.5
17.8
58.8
18.6
63.9
35.8
22.3
21.6
40.2
Ours
16.1
62.5
16.9
64.0
16.0
72.2
30.7
28.9
20.8
43.0
Table 3: Normal quantitative metrics for ablation. We demonstrate that both the Atrrinic--ivaaian training and coarse-to- fine strategy contributes to accurate normal predictions.
Input
Normal
Depth
Pointmap
View
Details
Figure 6: High-quality geometric predictions for high-resolution (2K) inputs. Please zoom in to better observe the fine-grained details.
Position-Interpooltee Rotary Positional Encoding, Dens3R can support multi multi-resolution image inputs. With the position-interpolane interpolated rotary positional encoding and the coarse-to-fine training strategy, our method can prevent performance degradation when handling high-resolution inputs. As shown in Fig. 8a, we can generate accurate and well-structured pointmaps with the position- interpolated RoPE, preventing the model from producing overlapping inconsistent pointmaps at higher resolutions.
Peerinsic-Invarian Training. Our approach first learns scale-invariant pointmap, which is then transformed into an intrinsic-invariant pointmap via subsequent autrinic-invaaiian training. We find that jointly training the pointmap and normal the initial scale-invarian stage leads to instability and poor convergence. This is because pointmaps and normal maps lie in different data domains, and coupling their supervision potentially increases training complexity. While Geo Wizard Fu et al.
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Figure 7: High-quality unified geometric predictions for various scenarios. We demonstrate accurate normal and depth predictions with high-quality 3D pointmaps for challenging object-centric, indoor and outdoor scenes.
Setting
Compute Cost
Memory Cost
Network Params
w/o Shared
1.362 TFlops
4.6 GB
737.591 M
w/ Shared
1.362 TFlops
4.1 GB
624.152 M
Table 4: Ablation on shared encoder-decoder structure. We conduct experiments for both of the model on image pairs with 512 resolution. With the shared encoder-decoder structure, our model yields lower memory cost and less network parameters.
(2024) addresses this domain gap with task switcher, we adopt two-stage training scheme to learn an intrinsicinvarriant pointmap, ensuring stable learning. As shown in Tab. 3, Tab. 6 and in Fig. 8b, the performance of the model will degrade without the Mtrinsic-invarian training.
We also go beyond purely image-domain monocular normal estimation, where methods like Sta- bleNormal Ye et al. (2024) operate on single view and therefore still suffer from monocular am- biguity. We concatenate pointmap and normal features so that the normal head can exploit the multi-view geometric information encoded in the Stage pointmap, which helps resolve ambiguity that cannot be solved from single image alone. At the same time, the normal predictions provide additional geometric information from the normal domain that refine the pointmap representation bidirectional mecha-
shown in Fig. 11. We therefore view the pointmap normal interaction nism: the multi-view pointmap supplies information that helps the normal head resolve monocular geometric ambiguities, while the normals, in turn, regularize and refine the 3D geometric represen- tation.
Coarse-to-Finee Training. Our model is trained on diverse training dataset of varying quality. To better utilize the full training set, we implement a coarse-to-fine training strategy that gradually increases resolution and data fidelity. In the coarse stage, we the max resolution of the training images as 512 and enable all the training data. In the fine stage, we increase the image resolution to 1024 pixels and restrict training to the nigh-resolution data only. As demonstrated Tab. and in Fig. 8b, this strategy improves prediction accuracy, particularly for high-resolution outputs.
Shared Encoder-Decoder Backbone Ablation Dens3R employs a dense visual transformer back- bone designed to capture spatial relationships across viewpoints and capture the global 3D geometric information of scenes. Different from previous methods, both the encoder and decoder components in our architecture share weights. The comparison of the network parameters and the memory cost is shown in Tab. 4. Since our model deals with more 3D quantities than previous methods, the frame- work initially requires higher memory cost. Employing the shared encoder structure also resolves this issue, reducing the memory cost and network parameters without losing the prediction quality.
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Input
we Position Interpoatte
Input
512
Manhrare
10X
Aeforence Frams
(a)
High-resolution inference comparison. Our
(b) Normal comparison for ablation. The intrinsic- invariant training enables accurate normal prediction and the coarse-to-fine training enhances details.
method supports high-rcsolution input and generates accurate and well-structured pointmaps.
Input
Prediction
Input
Input
Normal
Normal
(c) Segmentation results. Our model can be easily extended segmentation tasks by training new pre- diction head with the backbone frozen.
(d) Normal supervision results. We demonstratc the effectiveness of using our normal the supervision of surface reconstruction.
Figure 8: Ablation and downstream applications.
A.2 DOWNSTREAM APPLICATIONS
Segmentation Head Training, Dens3R serves as a visual foundation model that can be finetuned for several downstream tasks. We demonstrate this by training a new prediction head for segmentation task while keeping our backbone frozen. As shown in Fig. 8c, the segmentation head can generate accurate results, with much more effortless training than large segmentation model.
Surface Reconstruction. Dens3R can improve surface reconstruction quality by its sharp and accurate normals. We demonstrate this by utilizing our predicted normals as the supervision for NeuS Wang et al. (2021a) training. The results showcased in Fig. 8d. It can be seen that the final reconstruction results are improved due to the strong normal prior provided by our Dens3R.
Dens3R can also facilitate surface reconstruction by providing accurate 3D priors, including point maps, depth, and normals. We implement an end-to-end automated surface reconstruction pipeline following AutoRecon Wang et al. (2023c). We showcase the high-quality reconstruction results in Fig. 9.
A.3 IMPLEMENTATION DETAILS
Datasets. To train the visual foundation model, we collect and reorganize large-scale training dataset containing various data types. The dataset includes indoor scenes, outdoor scenes, and object-level data. It is noteworthy that the quality of training data has substantial impact on model performance, We then make the most of high-quality synthetic data in the training process for more accurate and robust predictions. We divide all the data into three types based their quality. Data of type is collected from synthetic rendering process with the highest quality. Data of type also originates from synthetic rendering, but they possess certain quality issues like insufficient resolu- tion or absence of background or imprecise original 3D models, etc. Data of type obtained from the real world using cameras and depth sensors. We also carefully allocate the proportions of each
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Input
Mesh
Input
Mesh
Figure 9: High-quality automated surface reconstruction results. We implement an end-to-end auto-
mated surface reconstruction pipeline using Dens3R and showcase the results.
Applied Losses
Image Pairs
Dataset
Type
Ratio
Cyas
Ematch
Co
Hypersim Roberts et al. (2021)
L8M
6.77%
UnrcalStereo4K Tosi et al. (2021)
A
/
0.9M
6.77%
MatrixCity Li et al. (2023)
/
/
0.7M
6.77%
Infinigen Raistrick ct al. (2024)
V
4
2.8M
6.77%
Behavior Li et al. (2022)
V
V
6.8M
6.77%
Structure3D Zheng et (2020)
A
V
0.2M
4.06%
GTASEM Wang Shen (2020)
13.53%
GTAV Richter et al. (2016)
V
V
0.6M
13.53%
VirtualKitti Gaidon et al. (2016)
V
4.0M
13.53%
IRS Wang et al. (2021b)
74K
0.41% %
UrbanSyn Gómez al. (2025)
7.0K
0.41%
Spring Mehl et al. (2023)
10K
0.41%
ScanNet++ Yeshwanth et al. (2023)
3.5M
1.35%
ABO Collins et al. (2022)
B
2.0M
1.35%
GOhjaverseXI. Deitke al. (2023)
6.8M
1.35%
ThingsThin Schröppel et al. (2022)
V
0.3M
1.35%
BlendedMVVS Yao et (2020)
1.IM
1.35%
Habitat Savva et al. (2019)
1.3M
0.68%
Taskonomy Zamir et al. (2018)
1.8M
0.68%
ARKitScenes Baruch et al. (2021)
B
2.2M
0.68%
Tartanair Wang et al. (2020b)
B
4.5M
0.68%
Synthia Ros et al. (2016)
2.6M
0.68%
KenBurns Niklaus et al. (2019)
0.3M
0.68%
MegaDepth & Snavely (2018b)
L&M
1.35%
Waymo Sun al. (2020)
1.1M
1.35%
Co3dv2 Reizenstein et al. (2021)
1.2M
1.35%
WildRGBD Xia ct al. (2024)
1.IM
1.35%
NianticMapPree Arnold al. (2022)
3.7M
1.35%
DL3DV Ling et (2024)
1.2M
1.35%
Indoor Cho al. (2019)
0.68%
ArgoverseStereo Chang et al. (2019)
4.0K
0.68%
Table 5: Training dataset information. We reorganize large-scale training dataset and divide the data into three types based on their quality. We also showcase the training objectives we apply during training, the number of image pairs and the corresponding dataset ratio.
dataset to attain the optimal model training performance. We summarize and present this dataset information in Tab. 5.
Training Details. During our coarse-to-fine training, we first utilize all the images with 512 reso- lution and train our model for about two weeks in the coarse-stage training. Then only utilize the images from type A dataset and minor portion of type B dataset and set the image resolution to 1024 for the fine-stage training. We utilize 32 Nvidia H20 GPUs for both the coarse and fine stage training. As for model inference, our model only requires a single Nvidia RTX3090 GPU for 1024-resolution image inputs.
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Input
Stage1 Pointmap
Stage2 Pointmap
Prediction Head
Figure 10: Normal prediction comparison of different training stages.
A.4 NORMAL AND DEPTH COMPARISON
Dens3R predicts robust and accurate normal and depth for various scenarios. As shown in Fig. 10, we demonstrate that the intrinsic-ines -nvvrriat training assists the pointmap to capture the geometric information from normal. Then the normal prediction head further predicts sharper edges and more accurate results.
We provide the normal prediction comparison of the Kitti dataset in Fig. 13. It can be seen that method generates the most accurate and sharp normals. We also provide more comparison of normal map prediction in Fig 15 using in-the-wild images and in Fig. 16 using DL3DV dataset. can be seen that our method predicts sharper and more accurate normal across various scenarios. We also compare our method with the normal map derived from the pointmap of DUSt3R Wang et al. (2024), MASt3 Leroy al. (2024) and the predicted depth map of MoGe Wang et al. (2025b), the results are shown in Fig. 14. It can be seen that Dens3R can handle normal predictions for reflective surfaces and accomplishes generate richer details. We also provide the full quantitative comparison in Tab. 6 which are partly shown in Tab. in the main paper correspondingly.
We provide the quantitative depth comparison in Tab. 7. It can be seen that our method achieves accurate results in depth estimation. We also provide additional qualitative depth prediction com- parison in Fig. 17, it can be seen that our method generates the most accurate depth maps even for reflective surfaces. Since VGGT Wang et al. (2025a) also predicts multiple quantities including depth and matching, we further compare our predicted depth map with VGGT. We demonstrate more accurate depth predictions of NYUv2 dataset in Fig. 18. We also showcase the accurate prediction of both indoor scenes of NYUv2 dataset and outdoor scenes of Kitti dataset. can seen Fig. 19 and Fig. 20 that our model also achieves accurate human depth estimation that can be further utilized for detection and autonomous driving.
A.5 CAMERA POSE ESTIMATION COMPARISON
Dens3R can also perform accurate camera pose estimation through a single feed-forward pass. We conduct extended experiments to demonstrate its accuracy. We utilize the map-free bench- mark Aroold et (2022) following the MASt3R protocol Leroy et al. (2024), which is a challeng- ing dataset aiming at localizing the camera in metric space given single reference image without any map. We present the camera pose estimation (Map-free relocalization) comparison in Tab. 8. It can be seen that Dens3R outperforms previous methods in nearly all the metrics, demonstrating highly accurate camera pose estimation results.
A.6 IMAGE MATCHING COMPARISON
For image-matching, apart from the ZEB dataset, we also provide the quantitative comparison of the Scannet-1500 dataset in Tab. 9 and the MegaDepth- 1500 dataset in Tab. 10. The comparisons on the ScanNet- 1500 and the MegaDepth- 1500 benchmarks further demonstrate our superior performance over pervious DUS(3R- method MASt3R Leroy et al. (2024) and VGGT Wang et al. (2025a).
A.7 HIGH-RESOLLUTI INFERENCE COMPARISON
We showcase more comparison of high-resolution inputs with DUSt3R Wang et al. (2024) and VGGT Wang et al. (2025a) in Fig. 21. It can be seen that our method can handle higher-resolution
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Method
Mean +
Med 4
811.25 1
822.5 f
830 1
NYUv2 (indoor)
DSINE
18.6
9.9
56.1
76.9
82.6
Lotus
17.5
8.6
58.7
76.4
82.0
GeoWizard
20.4
11.9
47.0
73.8
80.8
StableNormal
19.7
10.5
53.0
75.9
81.7
DUSt3R
18.5
9.5
55.2
74.6
81.2
MASt3R
25.2
14.9
40.6
63.3
71.7
Ours-Stagel
17.8
11.1
50.6
75.4
82.8
Ours
16.1
7.4
62.5
78.8
84.0
ScanNet (indoor)
DSINE
18.6
9.9
56.1
76.9
82
Lotus
18.1
58.2
75.3
80.8
WeoWiz
21.4
13.9
37.1
71.7
79.7
StableNormal
18.1
10.1
56.0
78.8
84.
DUSt3R
19.4
8.9
57.0
73.8
79.6
MASt3R
28.1
16.6
37.6
59.2
67.7
Ours-Stagel
18.6
11.4
49.4
75.1
81.8
Ours
16.9
7.1
64.0
78.1
82.7
IBims-l (indoor)
DSINE
18.8
64.1
78.6
82.2
Lotus
19.2
5.6
66.2
74.9
78.1
Geo
19.7
9.7
58.4
77.6
81.6
StableNormal
17.2
8.1
66.7
81.1
84.6
DUSt3R
21.9
8.2
57.9
71.7
76.7
MAS13
29.8
16.6
39.4
58.9
66.4
Ours-Stagel
20.2
9.3
56.8
73.2
78.3
Ours
16.0
4.3
72.2
80.1
83.0
Sintel (outdoor)
DSINE
34.9
28.1
21.5
41.5
52.7
Lotus
35.7
28.0
20.5
41.8
52.8
GeoWizard
41.6
34.3
11.8
31.8
43.9
StableNormal
35.0
27.0
19.5
42.4
54.6
DUS(3R
49.7
42.8
11.6
26.2
35,9
MASt3R
48.9
40.4
13.0
29.6
39.1
Ours-Stagel
35.9
27.6
18.9
41.5
53.5
Ours
30.7
21.4
28.9
51.9
62.2
DIODE-outdoor (outdoor)
DSINE
22.0
14.5
39.6
67.5
75,4
Lotus
24.7
15.9
32.9
63.9
71.9
GeoWizard
27.0
19.8
24.0
56.6
68.9
StableNormal
26.9
16.1
36.1
60.6
67.5
DUSt3R
28.1
17.5
32.1
58.2
66.5
MASt3R
29.0
18.4
31.5
56.8
65.4
Ours-Stagel
23.5
16.7
33.7
63.2
72.9
Ours
20.8
12.8
43.0
70.7
77.0
Table 6: Full quantitative comparison of normal prediction. We report the mean and median angular errors with each cell colored to indicate the best and the second
Method
NYUv2
DIODE-indoor
DIODE-ouldeer
5
REL_
GenPercept
0.052
0.214
96.7
99.3
99.8
0.107
0.924
89.1
96.0
98.1
0.727
3.571
67.3
84.2
90.6
Lotus"
0.053
0.262
96.5
99.1
99.7
0.111
1.123
88.7
96.0
98.4
0.488
9.960
63.3
71.8
DepthAnythingV2
0.049
0.204
97.3
99.3
0.878
92.5
97.3
98.6
0.705
5.525
83.4
89.7
DUSER
0.046
0.197
97.1
99.3
99.8
0.083
0.375
92.0
97.7
99.0
0.451
5.217
67.7
84.3
90.7
VGGT
0.400
90.6
MoGe
0.035
0.167
97.9
99.4
98.9
0.080
0.879
92.6
97.3
98.7
0.578
5.177
72.8
86.7
91.9
Ours
0.042
0.189
97.5
99.3
99.8
0.072
0.372
93.7
97.5
98.8
0.387
4.740
72,2
87.0
92.3
Table 7: Quantitative comparison monocular depth prediction. We report the relative point (REL), root mean square error (RMSE) and the percentage of inliers 81, 82, 03 with each cell colored to indicate the best and the second *We utilize Lotu-G disparity model for comparison.
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Method
Reproj. Ervor 4.
Precision
AUC
Median Error (m))..
Median Error (*)
Pose Precision
Pose AUC f
DUSGR
125.8 px
45.2%
0.704
1.10 m
9.4"
17.0%:
0.344
MASGR
57.2
75.9%
0.934
0.46
3.0"
51.7%
0.746
VGGT
48.8
78.9%
0.789
0.36
3.6"
57.7%
0.577
Ours
30.4 px
82.1%
m
65.5%
0.852
Table 8: Camera pose estimation results of the Map-free dataset. We report the metrics with each cell colored to indicate the best and the second
Method
AUC@5
AUC@10 1
AUC@20 +
ROMA
31.8
53.4
70.9
VGGT
33.9
55.2
73.4
MASt3
62.4
77.4
86.9
Ours
65.6
80.3
89.2
Table Two-view matching comparison on ScanNet-1500 Dataset. We report the AUC values with each cell colored to indicate best and the second Our method achieves state-of-the-art for two-view matching, surpassing all the previous methods.
Method
AUCâ5
AUC@10
AUC@20* 1
SP+SG
42,2
61.2
76.0
SP+LG
49.9
67.0
80.1
LoFTR
52.8
69.2
81.2
MASt3R
73.3
84.1
90.9
Ours
73.9
84.4
91.2
Table 10: Two-view matching comparison on MegaDepth- 1500 Dataset. We report the AUC values with each cell colored to indicate the best and the second Our method also achieves state-of-the- art for the two- wiww matching using the McgaDepth- 1500 Dataset.
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Input
Ours
Ours Stage1
Figure 11: Pointmap comparison of Stage and Stage 2. We demonstrate that the normal predictions
provide additional geometric information that refine the pointmap representation.
Input
GT
Ours
Figure 12:
Limitations. Despite that method outperforms previous methods in geometrie pre- dictions, the prediction quality for thin structures still require further improvement.
inputs without causing degenerated predictions like previous methods with our proposed position- interpolated rotary positional encoding. We also provide additional qualitative comparisons in Fig. 22 demonstrate the effect of rosition-interpolatee RoPE. can be observed that adding this design on top of DUSt3R already improves the inference quality for higher-resolution inputs. We also empirically validate that when the network has not been exposed to high-resolution inputs during training, using
RoPE alone is not sufficient because it has no guidance on the "correct" way to extrapolate. In our full model, we combine it with the intrinsic-invariant pointmap and coarse-to-fine training scheme, and we obtain more accurate reconstructions at high resolution. We believe these results show that our use of iaatiioninterpplatted RoPE not a trivial plug-in, but natural component of dedicated training and representation framework for high- resolution inference.
A.8 LIMITATION
Although Dens3R outperforms previous methods in geometric predictions, predicting accurate re- sults for inputs with thin structures remains significant challenge. Restricted by the network's 's limited capacity and the presence of noisy training data, our method may predict inaceurate results for these inputs. As shown in Fig. 12, the prediction quality for thin structures still require further improvement.
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Input
Ours
GeoWizard StableNormal
Lotus
DSINE
Figure 13: Normal comparison of Kitti dataset. We present normal comparison of outdoor scenes, our method produces more accurate and sharper normals than previous methods.
Input
DUSt3R
MASt3R
MoGe
Ours
Figure 14: Normal comparison with DUS(3R, MASt3R and MoGe. We provide more normal com- parison with the normal maps derived from DUS(3R, MASt3R and MoGe. Dens3R yields sharper and more accurate predictions.
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Input
Ours
StableNormal GeoWizard
Lotus
DSINE
Figure 15: More qualitative comparison of normal map. We provide more normal comparison of both object-centric and human scenes. Dens3R is able to produce more accurate and sharper results
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Input
StableNormal
Figure 16: More qualitative comparison of normal map. We provide more normal comparison of both indoor and outdoor scenes. Dens3R is able to produce sharper and more accurate results and surpasses previous methods.
Input
Ours
MoGe
VGGT
MASI3R
DUSE3R
Figure 17: Additional depth comparison. We provide more depth comparison with previous methods and our method can predict more accurate and detailed results.
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Input
GT
VGGT
Ours
Input
VGGT
Ours
Figure 18: Additional depth comparison with VGGT. We compare our depth prediction results with VGGT and Dens3R demonstrates more robust and accurate predictions.
Input
VGGT
Ours
Input
VGGT
Ours
Figure 19: Additional depth comparison of indoor scenes with VGGT, Dens3R demonstrates more accurate results for human depth estimation.
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Input
VGGT
Ours
Figure 20: Additional depth comparison of outdoor scenes with VGGT. We compare our depth prediction results of autonomous driving dataset. Our methods achieves much more accurate pre- dictions.
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Input
DUSt3R
VGGT
Ours
512
Main Frame
1024
Reference Frame
512
Main Frame
1024
Reference Frame
512
Main Frame
1024
Reference Frame
Figure 21: Additional high-resolution inference comparison. We provide more nigh-resolution infer- ence results to demonstrate the effectiveness of the proposed rosition-interpolatee rotary positional encoding. We present the pointmap of the main frame and our method accomplishes to prevent the degeneration problem that occured in previous methods.
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Input
DUSI3R
DUSI3R PI-RoPE
Ours
512
Main
1024
Reference Frame
512
Main Frame
1024
Reference Frame
512
Main Frame
1024
Reference Frame
Figure 22: Additional high reshutosol inference comparison. We empirically validate that using position-interpolatee RoPE alone is not sufficient for high-resolution inference.
31


Reviews:
[{"review_id": "cXtcuxDvIS", "reviewer": "Reviewer_4czy", "summary": "This paper proposes Dens3R, a model that predicts 3D geometry such as pointmaps, surface normals, depth, and image correspondences from unposed images. It uses a single transformer with shared weights and a two-stage training process: first learning scale-invariant pointmaps, then refining them into intrinsic-invariant ones using surface normal supervision. The model also adapts position-interpolated rotary positional encoding (RoPE) for high-resolution inputs and shows strong results in normal prediction and image matching.", "questions": "1. Can the authors include standard depth metrics on datasets like NYUv2 or ScanNet?\n2. Are the reported results from the unified model or after separate fine-tuning for each task?\n3. How is the depth head trained, and is the matching loss still used in Stage 2?\n4. Could the paper provide ablation results showing the effects of Stage 2, the normal loss, and RoPE interpolation?\n5. What is the procedure and computational cost for multi-view inference?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Addresses an important goal of predicting multiple 3D properties within one unified model.\n* The two-stage training design is well-motivated and helps reduce monocular ambiguity.\n* The position-interpolated RoPE is a simple and practical improvement for handling high-resolution data.\n* Strong empirical results support the model’s effectiveness on normal estimation and matching tasks.", "weaknesses": "* Missing quantitative evaluation for depth prediction, which weakens the claim of a unified geometric model.\n* Lacks ablation studies to verify the contribution of Stage 2, the normal loss, and RoPE interpolation.\n* The description of the “Heads Training” process is unclear, especially regarding when and how the depth head is trained.\n* Multi-view inference and computational cost are only briefly mentioned.", "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": "LI17I5XqHk", "reviewer": "Reviewer_EqXv", "summary": "The paper presents a geometric foundation model by designing a feed-forward framework based on the point map representation as in Dust3R, while also enabling a list of downstream tasks such as depth & normal prediction and semantic segmantation. A intrinsic invariant pointmap is proposed to enhance the geometric robustness during training by introducting a normal prediction head to recover the geometric details. The interpolatied RoPE is employed to handle multi-resolution visual input. Extensive qualitative and quantitative experimental results demonstrate the effectiveness of the paper.", "questions": "On the normal prediction head, the authors mentioned to replace the 'one-to-many' mapping to 'one-to-one' mapping. Does this mean infer on the single image feature instead of using cross attention to aggregate the features from multiple views? \n\nOverall I think the paper proposed a technically feasible system, however some technical motivations and details look unclear to me. If the authors can address my concerns, I would consider to change my rating.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "(1) Overall, the paper is well presented and the qualitative images used for demonstrating the geometric details are impressive.\n\n(2) Different from previous baseline methods such as Dust3R and Mast3R, the proposed framework is capable of predicting high-fidelity normal maps to recover the local geometric details, which is important for downstream computer graphics related applications.", "weaknesses": "(1) The motivation of designing a 'intrinsic invariant' pointmap is unclear to me. The pointmap is trained in a scale-invaraint manner by normalizing its geometric scale factors. Besides, the reason behind introducing the 'pointmap - normal' feature concatenatation can resolve the 'intrinsic-invaraint' ambiguity also remains unclear. Does it indicate that the normal could be further regularized by utilizing the information in the normalized pointmap?\n\n(2) Although the positional-intropolated RoPE is technically reasonable, I think the novelty here is limited thus hard to be claimed as a technical innovation. By adapting different resolution images during training and inference, it is necessary to deal with the positional embedding in ViT with the flexible sequence length. So interpolating RoPE is a natural choice instead of a novelty. \n\n(3) One highlight of this method is the high-quality normal map prediction. However, the baseline methods used for comparison are trained on single view, leading to a unfair compairson. Since the paper follows the Dust3R's framework, a more practical baseline design is to compare the normal extracted from Dust3R's point map, which also uses a pointmap representation and trained on multiple views.", "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": "sODXWkuZ7L", "reviewer": "Reviewer_V6Ty", "summary": "This paper presents Dens3R, a regression-based 3D foundation model that unifies the prediction of multiple geometric quantities including depth, surface normals, and pointmaps from unposed image inputs. The method extends prior DUSt3R/MASt3R frameworks with a shared encoder-decoder backbone, a two-stage training strategy, and position-interpolated rotary positional encoding to improve robustness and multi-task consistency.", "questions": "Can the authors clarify how much of the observed improvement comes specifically from the inclusion of surface normal supervision versus other training refinements?\n\nHow does the proposed “intrinsic-invariant pointmap” differ mathematically from the scale-invariant version used in Stage 1 — is it a new representation or mainly a training objective modification?\n\nSince the positional interpolation for RoPE is borrowed from prior work, did the authors conduct ablations to quantify its contribution relative to baseline DUSt3R at higher resolutions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Well-engineered system that integrates multiple known effective components into a unified framework.\n\nDemonstrates consistent performance improvements across several 3D geometry benchmarks (depth, normal, matching).\n\nThe paper is technically sound and clearly written, with solid experimental validation.\n\nThe incorporation of normal prediction and staged training improves empirical robustness and output consistency.", "weaknesses": "The core architectural and methodological ideas (multi-task learning, two-stage training, positional interpolation) are not novel and have been widely explored in prior work.\n\nThe backbone and representation design largely follow DUSt3R/MASt3R, with limited conceptual innovation.\n\nThe claim of being a “foundation model” is overstated, as the work focuses on supervised dense regression without demonstrating large-scale generalization or transfer capabilities.", "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": "3KOyicCrDp", "reviewer": "Reviewer_b7qA", "summary": "Dens3R is a visual foundation model for dense 3D geometry prediction from unposed images. It jointly regresses consistent pointmaps, depth, and surface normals using a novel two-stage training framework. This approach builds an intrinsic-invariant pointmap by incorporating normals, ensuring high-quality, unified geometric perception across various tasks.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The model demonstrates SOTA performance across diverse benchmarks, including both indoor and outdoor scenes. Compelling visualizations showcase its superior accuracy and detail in depth and normal prediction, validating its effectiveness and robustness as a powerful geometry perception tool that consistently outperforms specialized methods.\n\n2. A key strength is the well-motivated approach of jointly optimizing inherently correlated geometric quantities like depths and normals. Instead of predicting them in isolation, this unified framework explicitly models their structural coupling, ensuring geometric consistency.\n\n3. The paper introduces an innovative two-stage training strategy. By leveraging surface normals—an intrinsic property—in the second stage, the model learns a representation robust to camera parameters and scale. This elegantly resolves monocular ambiguity and significantly boosts overall prediction accuracy and stability.\n\n4. The paper is backed by comprehensive experiments, including thorough ablation studies that validate each key component. A particularly impressive highlight is its transferability to downstream tasks. The excellent performance on semantic segmentation (Fig. 8c) with a frozen backbone powerfully substantiates its claim as a versatile foundation model.", "weaknesses": "The network structure resembles DUST3R and may have sub-optimal performance when reconstructing long sequence inputs.", "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)