Depth Anything 3: Recovering the Visual Space from Any Views
Authors: Haotong Lin
Sili Chen
Jun Hao Liew
Donny Y. Chen
Zhenyu Li
Yang Zhao
Sida Peng
Hengkai Guo
Xiaowei Zhou
Guang Shi
Jiashi Feng
Bingyi Kang
Venue: ICLR 2026 Oral (2026)
Keywords: Depth Estimation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Depth Anything 3 uses a single vanilla DINOv2 transformer to take arbitrary input views and outputs consistent depth and ray maps, delivering leading pose, geometry, and visual rendering performance.

Abstract: We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. 
In pursuit of minimal modeling, DA3 yields two key insights:
a single plain transformer (e.g., vanilla DINOv2 encoder) is sufficient as a backbone without architectural specialization, and a singular depth-ray prediction target obviates the need for complex multi-task learning. Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Anything 2 (DA2).
We establish a new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 35.7\% in camera pose accuracy and 23.6\% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets.

DEPTH ANYTHING 3: RECOVERING THE VISUAL SPACE FROM ANY VIEWS
Haotong Lin
Sili Chen
Yang Zhao' Xiaowei Zhou² Guang Shi' Jiashi Feng' Bingyi Kang" li
Jun Hao Liew Donny Y. Chen" Zhenyu Li'
Sida Peng"
Hengkai Guo
'ByteDance Seed
ZZhejiang University *ByteDance Intelligent Creation
Equal contribution
Project lead & Corresponding author
Input: Any number of images with without camera poses
Depth Anything a single transformer model
HRoomm Tscnne poaaa Samuter samdurt bro HiRoam Thomes
Output: Point cloud
eeHL
Geo. & Pose
Depth Ray Maps
Accuracy
Seeo EAMAPE Drq
DA3
P1B
VGGT
Geometry Rendering
DA3-Tedcher
DAJ
DAZ
Mono. Depth Accuracy
Figure 1: Given any number of images and optional camera poses, Depth Anything 3 reconstructs visual space, producing accurate depth and ray maps that fuse into a consistent point cloud. substantially outperforms VGGT in multi-view geometry and pose accuracy; with monocular inputs, also surpasses Depth Anything while matching its detail and robustness.
ABSTRACT
We present Depth Anything 3 (DA3), a model that predicts spatially consistent ge- ometry from arbitrary number of visual inputs, with without known camera poses. In pursuit of minimal modeling, DA3 yields two key insights: a single plain transformer (e.g., vanilla DINO encoder) is sufficient backbone without archi- tectural specialization, and singular depth-ray prediction target obviates the need for complex multi-task learning. Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Any- thing (DA2). We establish new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering. On this benchmark, DA3 sets new sfate-of-the-art across all tasks, surpassing prior SOTA VGGT by average of 35.7% in camera pose accuracy and 23.6% in geometric accuracy. Moreover, outperforms DA2 in monocular depth estimation. All models are trained exclusively public academic datasets.

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INTRODUCTION
The ability to perceive and understand 3D spatial information from visual input is cornerstone of human spatial intelligence (Arterberry and Yonas, 2000) and a critical requirement for applica- tions like robotics and mixed reality. This fundamental capability has inspired wide array of 31D vision tasks, including monocular depth estimation, Structure from Motion (Snavely et al., 2006), Multi-View Stereo (Seitz et al., 2006) and Simultaneous Localization and Mapping (Mur-Artal et al., 2015). Despite the strong conceptual overlap between these masks-often differing by only a single factor, such the number of input views- the prevailing paradigm has been to develop highly spe- cialized models for each one. While recent efforts (Wang et al., 2024c; 2025a) have explored unified models to address multiple tasks simultaneously, they typically suffer from several key limitations: they often rely on complex, bespoke architectures, are trained via joint optimization over tasks from scratch, and consequently cannot effectively leverage large-scale pretrained models.
In this work, we step back from established 3D task definitions and return to a more fundamental goal inspired by human spatial intelligence: recovering 3D structure from arbitrary visual inputs, be it a single image, multiple views of a scene, or video stream. Forsaking intricate architectural engineering. we pursue a minimal modeling strategy guided by two central questions. First, is there a minimal set of prediction targets, or is joint modeling across numerous 3D lasks necessary? Sec- ond, can a single plain transformer suffice for this objective? Our work provides an affirmative answer to both. We present Depth Anything 3, a single transformer model trained exclusively for joint any-view depth and pose estimation via a specially chosen ray representation. We demon- strate that this minimal approach is sufficient to reconstruct the visual space from any number of images, with or without known camera poses.
Depth Anything 3 formulates the above geometric reconstruction target dense prediction task. For given set of N input images, the model is trained to output N corresponding depth maps and ray maps, each pixel-aligned with its respective input. The architecture to achieve this begins with a standard pretrained vision transformer (e.g.. Oquab et al. 2023), as its backbone, leveraging its powerful feature extraction capabilities. To handle arbitrary view counts, we introduce a key modifi- cation: an input-adaptive cross-view self-attention mechanism. This module dynamically rearranges tokens during the forward pass in selected layers, enabling efficient information exchange across all views. For the final prediction, we propose new dual DPT head designed to jointly outputs both depth and ray values, by processing the same set of features with distinct fusion parameters. To enhance flexibility, the model can optionally incorporate known camera poses via a simple camera encoder, allowing it to adapt to various practical settings. This overall design results in clean and scalable architecture that directly inherits the scaling properties of its pretrained backbone.
We train Depth Anything 3 via a teacher-student paradigm to unify diverse training data, which is necessary for generalist model. Our data sources include varied formats like real-world depth camera captures (e.g., Baruch et al. 2021), 3D reconstruction (e.g., Reizenstein et al. 2021), and synthetic data, where real-world depth may be of poor quality (Fig. 7). To resolve this, adopt a pseudo-labeling strategy inspired by prior works (Yang et al., 2024a;b). Specifically, we train a powerful teacher monocular depth model on synthetic data to generate dense, high-quality pseudo- depth for all real-world data. Crucially, to preserve geometric integrity, we align these dense pseudo- depth maps with the original sparse or noisy depth. This approach proved remarkably effective, significantly enhancing label detail and completeness without sacrificing the geometric accuracy.
To better evaluate our model and track progress in the field, we establish a comprehensive benchmark for assessing geometry and pose accuracy. The benchmark comprises 5 distinct datasets, totaling over 89 scenes, ranging from object-level to indoor and outdoor environments. By directly evalu- ating pose accuracy across scenes and fusing the predicted pose and depth into a 3D point cloud for accuracy assessment, the benchmark faithfully measures the pose and depth accuracy of visual geometry estimators. Experiments show that our model achieves state-of ofthe-ot- performance on 18 out of 20 settings. Moreover, on standard monocular benchmarks, our model outperforms Depth Anything 2 (Yang al., 2024b).
To further demonstrate the fundamental capability of Depth Anything 3 in advancing other 3D vi- sion tasks, introduce challenging benchmark for feed-forware novel view synthesis (FF-NVS), comprising over 160 scenes. We adhere to the minimal modeling strategy and fine-tune our model with an additional DPT head to predict pixel-aligned 3D Gaussian parameters. Extensive experi-
2

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Single Transformer
Lp
Teacher Model
Image Patch Embed
(Vanilia DINO)
Depth
Dual Dual-DPr DPT Head
LM
Cp
L,
N ai Neg W.
NxHxwxx
Within-view atti,
Camera (f (faaailable
Learnable camera token
L
nofinot availablee)
Cross self attn.
Figure 2: Pipeline of Depth Anything Depth Anything employs single transformer (vanilla DINOv2 model) without any architectural modifications. To enable cross-view reasoning, an input- adaptive cross-view view self-attention mechanism is introduced. A dual-DPT head used to predict depth and ray maps from visual tokens. Camera parameters, if available, are encoded as camera tokens and concatenated with patch tokens, participating in all attention operations.
ments yield two key findings: fine-tuning a geometry foundation model for NVS substantially outperforms highly specialized task-specific models (Xu et al., 2025e); 2) enhanced geometric re- construction capability directly correlates with improved FF-NVS performance, establishing Depth Anything 3 as the optimal backbone for this task.
DEPTH ANYTHING 3
We tackle the recovery of consistent 3D geometry from diverse visual imputs single image, multi- view collections, or videos and optionally incorporate known camera poses when available.
2.1 FORMULATION
We denote the input as I = ( with each I, E RxxWx33 For N. = this is a monocular image, and for No > it represents a video or multi-view set. Each image has depth D; e "x camera pose R; t.]. and intrinsics K, The camera can also be represented V; RR with translation t, R33 rotation quaternion qi Rt. and FOV parameters f, E R.2. pixel p = 1)) projects to a 3D point P = (X, 2,1) by
P R. (u, v) K p) t;;
through which the underlying 3DD visual space be faithfully recovered.
Depth-ray representation. Predicting valid rotation matrix R, is challenging due to the orthog- onality constraint. To avoid this, we represent camera pose implicitly with a per-pixel ray map, aligned with the input image and depth map. For each pixel p. the camera ray by its origin t E R33 and direction d E TR3 r (t, d). The direction is obtained by backproject- ing p into the camera frame and rotating M E ie xW x6 stores these parameters for all pixels. We do not normalize d, so its magnitude preserves the projection scale. Thus, 3D point in world coordinates is simply P = t+ D(u, v) dd. This formulation enables consistent point cloud generation by combining predicted depth and ray maps through element-wing operations.
TR6 is defined
the world frame: d RK 'p. RK-1p
The dense ray map
Minimal prediction targets. Recent works aim to build unified models for diverse 3D tasks, of- ten using multitask learning with different targets- for example, point maps alone (Wang et al., 2024b), or combinations of pose, local/global point maps, and depth (Wang et al., 2025a;b; Yang et al., 2025a). However, point maps inherently entangle depth and camera information into a single representation, which makes them less effective than disentangled depth predictions for geometry estimation (Wang et al., 2025a; Yang et al., 2025a). Consequently, prior works introduce additional depth heads alongside point maps, creating redundancy in the prediction targets. In contrast, periments (Tab. 5) show that a depth-ray representation forms minimal yet sufficient disentangled target set for capturing both scene structure and camera motion, outperforming point map-based
3

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alternatives. However, recovering camera pose from the ray map at inference is computationally costly. We address this by adding a camera head, De, which has minimal computational overhead. This transformer operates on camera tokens to predict the field of view (f E RR), rotation quaternion (q E R²), and translation (t. E IR$). Since it processes only one token per view, the added computational cost is negligible.
2.2 ARCHITECTURE
We now detail the architecture of Depth Anything 3, which illustrated in Fig. 2. The network composed of three main components: single transformer model as the backbone, optional camera encoder for pose conditioning, and a Dual-DPT head for generating predictions.
Single transformer backbone. We use Vision Transformer with blocks, pretrained on large- scale monocular image corpora (e.g., DINOv2 Oquab et al. 2023). Cross-view reasoning is enabled without architectural changes via an input-adaptive self-attention, implemented by rearranging input tokens. We divide the transformer into two groups of sizes L, and Lg. The first L, layers apply self-attention within each image, while the subsequent Lg layers alternate between cross-view and within-view attention, operating
all tokens jointly through tensor reordering. In practice, we set with I, T.g + T.g- As shown in our ablation study Tab. 6, this configuration provides the optimal trade-off between performance and efficiency compared to other arrangements. This design is input-adaptive with a single image, the model naturally reduces to monocular depth estimation without extra cost.
Tig: 7/
Camera condition injection. To seamlessly handle both posed and unposed inputs, we prepend each view with camera token C, If camera parameters (K,, R,, t.) are available, the token is obtained via a MI.P & C; = (f, qé, t.). Otherwise, a shared learnable token ej is used. Concate- nated with patch tokens, these camera tokens participate in all attention operations, providing either explicit geometric context or a consistent learned placeholder.
Dual-DPT head. For the final prediction stage, we introduce Dual-DPT head that jointly outputs dense depth and ray values, of- fering both strong and efficient (Tab. 5) re- sults. Backbone features are first processed through shared reassembly modules, then split into two branches with distinet fusion layers for depth and rays, followed by separate out- put layers. Both branches thus operate on the same processed features, differing only in the fusion stage, which promotes interaction be- tween tasks while avoiding redundant represen- tations. Our model also outputs confidence map for depth following Wang al. (2024b).
Reassemble
Fusion
Fusion
Feature tokens
Reassemble
Fusion
Reassemble
Fusion
Fusion
Reassemble
Fusion
Fusion
Output Layer
Output Layer
Figure 3: Dual-DPT Head. Two branchs share
reassembly modules for better outputs alignment.
2.3 TRAINING
Teacher-student learning paradigm. Our training data comes from diverse sources, including real-world depth captures, 3D reconstructions, and synthetic datasets. Real-world depth often noisy and incomplete (Fig. 7), limiting its supervisory value. To mitigate this, we train a monocular relative depth estimation "teacher" model solely on synthetic data generate high-quality pseudo- labels. These pseudo-depth maps are aligned with the original sparse or noisy ground truth via RANSAC least squares, enhancing label detail and completeness while preserving geometric aceu- racy. We term this model indoor, outdoor, object-centric, and diverse in-the-wild scenes to capture fine geometry. We detail teacher design in the appendix.
trained on a large synthetic corpus covering
Training objectives. Following the formulation in Sec. 2.1, our model Fo maps an input to a set of outputs comprising a depth map D, a ray map R, and an optional camera pose è: Fo I + (D, R. é}. The gray color indicates that è is an optional output, included primarily for practical convenience. Prior to loss computation, all ground-truth signals normalized by common scale
4

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factor. This scale is defined the mean & norm of the valid reprojected point maps P, step that ensures consistent magnitude across different modalities and stabilizes the training process. The overall training objective is defined as weighted sum of several loss terms:
L L= D) + CM (R. M) (D @ d + +t,P) BCo v) + al aL D,D).
In practice, we set a = 1 and 3 = 1. LD is a confidence-award loss following Wang et al. (2024b). Lgrad is taken from Yang et al. (2024b), penalizing the depth gradients. This loss preserves sharp edges while ensuring smoothness planar regions. We detail the loss function in the appendix.
2.4 FINETUNING FOR FEED-FORWARD NOVEL VIEW SYNTHESIS
Inspired by human spatial intelligence, we believe that consistent depth estimation can greatly en- hance downstream 3D vision tasks. We choose feed-forwan novel view synthesis (FF-NVS) as the demonstration task, given growing attention driven by advances in neural 3D representations and its relevance to numerous applications. Adhere to the minimal modeling strategy, we perform FF-NVS by fine-tuning with an added DPT head (GS-DPT) to infer pixcl-aligned 3D Gaussians.
GS-DPT Head. Given visual tokens for each view extracted via single transformer backbone (Sec. 2.2), GS-DPT predicts the camera-space 3D Gaussian parameters {o,, q Sj. c; f==11 H where 0N q E H. 8; E R c E R& denote the opacity, rotation quaternion, scale, and RGB color of the i-th 3D Gaussian, respectively. Among them, σ; is predicted by the confidence head, while others are predicted by the main GS-DPT head. The estimated depth is unprojected to world coordinates to obtain the global positions P. RS of the 3D Gaussians, These primitives are then rasterized to synthesize novel views from given camera poses. We detail our loss functions in the appendix.
VISUAL GEOMETRY BENCHMARK
We further introduce visual geometry benchmark to assess geometry prediction models. It directly evaluates pose accuracy, depth via reconstruction accuracy and visual rendering quality.
Pose accuracy. Our benchmark covers 5 datasets: HiRoom (an internal high-fidelity room dataset), ETH3D (Schops et al., 2017), DTU (Aanæs et al., 2016), 7Scenes (Shotton et al., 2013), and ScanNet++ (Yeshwanth et al., 2023), containing 29, 11, 22, 7, and 20 scenes, respectively. These span object-centric indoor and outdoor. HiRoom and the benchmark will be released publicly, ScanNet++ is not a zero-shot dataset, as it has been widely used for training since DUSt3R. Although comparisons are biased, we retain for completeness since subsequent methods also adopt We report Auc3 and Auc30, which measure relative rotation and translation score (higher is better).
Geometry accuracy. Using the same datasets, we assess depth accuracy via reconstruction. Un- like Wang et al. (2025a), which aligns predicted depths to ground truth with scale and shift and then reconstructs the scene with ground-truth poses, we reconstruct using both predicted poses and depths. The resulting point cloud is aligned to ground truth by applying evo (Umeyama, 2002) to match predicted poses with ground-trutt -trtth poses. We report F-Score for all datasets except Chamfer Distance for DTU, following prior work (Yu et al., 2022).
Visual rendering quality. We evaluate visual rendering quality on diverse large-scale scenes. We introduce a new NVS benchmark built from three datasets, including DL3DV (Ling et al., 2024) with 140 scenes, Tanks and Temples (Knapitsch al., 2017a) with 6, and MegaDepth (Li and Snavely, 2018) with 19, each spanning around 300 sampled frames. Ground truth camera poses, estimated with COLMAP, are used directly to ensure accurate and fair comparison across diverse models. We report PSNR. SSIM, and LPIPS metrics rendered novel views using given camera poses.
4 EXPERIMENTS
Training datasets, baselines, implementation details and more ablations provided in the appendix.
5

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Table 1: Comparisons with SOTA methods on pose accuracy. We report both Auc3 1 and Auc30
i metrics. The top-3 results are highlighted as first second and third
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
Methods
Auc3
Auc30
Auc3
Auc30
Auc3
Auc30
Auc3
Auc30
Auc3
Auc30
Colmap
13.0
19.0
4.75
41.3
87.2
87.4
22.3
62.0
13.3
19.9
Glomap
31.9
42.6
8.37
21.7
96.8
96.9
24.1
85.0
20.8
42,9
DUSI3R
17.6
54.3
4,30
27.3
4.00
74.3
6,90
61.6
8,10
33.9
Fast3R
25.9
77.0
8.10
44.4
9.50
79.1
19.0
78.6
17.9
72.5
MapAnything
17.9
82.8
19.2
77.4
6.50
72.7
12.6
79.7
20.2
84.1
Pi3
67.0
94.8
35.2
87.3
62.5
94.9
25.5
86.3
50.7
92.1
VGGT
49.1
88.0
26.3
80.8
79.2
99.8
23.9
85.0
62.6
95.1
DA3-Giaant
81.7
96.4
39.3
90.6
85.6
94.9
29.2
86.8
83,2
98.0
DA3-I Large
37.9
84.5
19.0
81.7
58.4
95.3
25.1
85.4
46.9
92.1
DA3-Base
12.8
79.8
13.6
74.0
31.4
90.8
17.2
81.1
16.2
77.5
DA3-Small
3.40
64.6
4.89
51.9
9.46
82.2
6.19
71.8
2.86
51.8
Reference video
VGGT
Ours
GT
Figure 4: Comparisons of pose estimation quality. Camera trajectories for two videos shown. Ground-truth trajectories are derived using COLMAP on images with dynamic objects masked.
4.1 VISUAL GEOMETRY ESTIMATION
Pose estimation. As shown in Tab. and Fig. 4, we compare our method against both classical SfM pipelines (COLMAP Schönberger and Frahm (2016), GLOMAP Pan et al. (2024b)) and feed- forward methods (Wang et al., 2024b; 2025a; Yang et al., 2025a; Wang et al., 2025d; Keetha et al., 2025) using Auc3 and Auc30 metries across five datasets.
Classical methods excel on dense, well-textured scenes like DTU, where GLOMAP achieves top performance (Aue3: 96.8). However, they struggle significantly challenging scenarios with sparse views, textureless regions, or dynamic content. For instance, on HiRoom, COLMAP achieves only 13.0 Aue3 compared 81.7 from our method. Similarly, on ScanNet++, COLMAP obtains 13.3 Auc3 versus 83.2 from ours.
Our DA3-Giant model establishes new SOTA results across nearly all metries, with particularly strong performance challenging datasets. On Auc3, our model delivers at least an 8% relative improvement over all feed-forwatt competitors, and on ScanNet++ achieves 33% relative gain over the second-best feed-forward model. To qualitatively assess pose quality, we visualize predicted camera trajectories on two in-the-wild dynamic videos Fig. 4. Our trajectories are smooth and closely align with the ground truth, whereas VGGT and Pi3 exhibit substantially noisier paths.
Geometry estimation. As shown in Tab. 2, we compare method against both classical strucure-from-mmotie pipelines (COLMAP Schönberger and Frahm (2016), GLOMAP Pan et al. (2024b)) and state-of-the the-t-rrtt feed-forward reconstruction methods under two distinct conditions: pose-free setting where camera parameters are unavailable, and a pose-conditioned setting where they known.
Classical methods perform competitively on extremely dense datasets like DTU, where GLOMAPPPPcchMtthhSeerr achieves 1.62 mm chamfer distance. However, their performance degrades significantly on datasets that are even slightly sparser. For example, on ETH3D, COLMAP+PM achieves F-score of only 20.7 (ours: 74.4), and HiRoom, it drops 16.8

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Table Comparisons with SOTA methods on reconstruction accuracy. For all datasets except DTU, we report the F-Score (F1 1). For DTU, we report the chamfer distance (CD J. unit: mm). w/o p. and w/ p. denote without pose and with pose, indicating whether ground-truth camera poses
are provided for reconstruction. The top-3 results are highlighted as first second and third
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
Methods
w/o p.
w/
w/o p.
w/ p.
w/o p.
w/ p.
w/o p.
w/ p.
w/o p.
w/
Co
16.8
21.9
20.7
25.7
1.67
1.64
40.0
37.3
13.7
19.6
Glomap+PM
30.7
41.1
24.1
36.3
1.62
1.59
43.9
47.6
16.5
22.4
DUS(3R
30.1
39.5
19.7
7.60
7.97
39.8
18.9
27.3
Fast3R
40.7
48.2
38.5
50.3
6.88
8.20
41.0
49.8
37.1
53.7
MapAnything
32.4
69.2
54.8
71.9
7.91
3.97
44.8
55.2
39.4
71.3
Pi3
75.8
85.0
72.7
80.6
3.28
1.72
44.2
57.5
63.1
73.3
VGGT
56.7
70.2
57.2
66.7
2.05
1.44
47.9
51.4
66.4
70.7
DA3-Giam
89.3
95.2
74.4
85.8
1.92
0,91
52.0
52.3
76.4
79.2
Da3eLarge
48.2
85.7
57.3
79.1
3.45
2.48
48.7
48.7
58.9
72.9
DA3-Base
18.6
71.7
52.8
66.6
5.14
1.99
37.8
47.2
39.7
66.3
DA3-Small
12.9
43.1
39.4
58.2
5.12
4.05
30.8
39.5
24.2
45.7
RGB
Ours
VGGT
PI3
Fast3R
MapAnything
Figure 5: Comparisons of depth quality. Compared with other methods, our depth maps exhibit finer structural detail and higher semantic correctness across diverse scenes.
(ours: 89.3). This highlights the limitations classical methods in handling sparse-view scenarios, where correspondence matching becomes unreliable.
In contrast, our DA3-Gaint establishes new SOTA across nearly all scenarios, outperforming all feed-forward competitors in all five pose-free settings. On average, DA3-Gaint achieves relative improvement of 23.6% over VGGT and 16.7% over Pi3. Fig. 5 and Fig. 6 visualize our predicted depth and recovered point clouds. The results are not only clean, accurate, and complete, but also preserve fine-grasn eme-grained geometric details, clearly demonstrating superiority over other methods.
Even more notably, our much smaller DA3-Large (0.30B parameters) demonstrates remarkable ef- ficiency. Despite being 3x smaller than VGGT (0.90B parameters), it surpasses VGGT in five out of the ten settings, with partieularly strong performance on ETH3D.
When camera poses are available, both our method and MapAnything can exploit them for improved results, and other methods also benefit from ground-truth pose fusion. Our model shows clear gains on most datasets except 7Scenes, where the limited video setting already saturates performance and reduces the benefit of pose conditioning. Notably, with pose conditioning, performance gains from scaling model size are smaller than in pose-free models. indicating that pose estimation scales more strongly than depth estimation and requires larger models to fully realize improvements.
Monocular depth accuracy also reflects geome- try quality. As shown in Tab. 3, on the standard monocular depth benchmarks reported in Yang et al. (2024b), our model outperforms VGGT and Depth Anything 2. For reference, also include the results of our teacher model.
Table 3: Monocular depth comparisons. t
Method KITTI NYU SINTEL ETIISD DIODE
Rank
94.6
97.9
77.2
95.2
2.60
VGGT
91.7
97.9
67.9
95.3
3.75
DA3
95.3
97.4
75.5
98.6
95.4
2.20
Teacher
97.2
97.9
81.4
99.8
96.6
1.00
Visual rendering. To fairly evaluate feed- forward novel view synthesis (FF-NVS), we compare against three recent 3DGS mod-
els els-pixelSplat (Charatan et al., 2024), MVSplat (Chen al., 2024), and DepthSplat (Xu

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Reference
Ours
VGGT
Pi3
Fast3R
Figure 6: Comparisons of point cloud quality. Our model produces point clouds that are more geometrically regular and substantially less noisy than those generated by other methods.
Table 5: Ablations of prediction-target combinations. Note that all experiments in this table do not have camera condition token. The best and second best are highlighted.
Avg
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
Methods
Auc3
F1
Auc3
F1
Auc3
F1
Auc3
CD
Auc3
F1
Auc3
F1
ped depth
31.6
51.5
35.9
42.7
20.3
64.6
4.219
21.7
45.5
30.8
53.3
14.1
40.2
10.8
16.5
9.9
48.0
23.3
5.316
13.0
38.5
13.3
41.0
depth
ped
22.6
42.9
9.1
12.8
19.0
60.4
42.3
4.918
20.8
43.4
22.0
43.0
depth + ray
36.0
56.4
48.7
60.3
25.5
65.4
46.5
3.919
24.0
46.5
35.5
53.4
depth + ray + ped
36.4
56.5
52.3
61.4
22.6
64.2
43.0
4.158
27.3
48.5
36.7
51.8
depth + ray + cam
35.1
51.7
37.2
45.4
22.3
59.4
56.3
3,066
25.7
45.6
34.1
56.5
al.. 2025c) and further test alternative frameworks by replacing our geometry backbone with Fast3R (Yang et al., 2025b), MV-DUS(3R (Tang et al., 2025), and VGGT (Wang et al., 2025a). All models are trained on DL.DV-10K training set under unified protocol and evaluated on our benchmark (Sec. 3).
As shown in Tab. 4, all models per- form substantially better on DL3DV than on the other datasets, suggesting that 3DGS-based NVS is sensitive to trajectory and pose distributions standardized by DL3DV, rather than scene content. Comparing the two groups, soometry-model-tee frameworks consistently outperform specialized feed-forward models, demonstrating that a simple back- bone plus DPT head can surpass complex task-specific designs. The advantage stems from large-scale pretraining, which enables better generalization and scalability than approaches relying on epipolar transformers, cost volumes, cascaded modules. Within this group, NVS performance correlates with geometry estimation capability, making DA3 the strongest back- bone. Looking forward, expect FF-NVS can be effectively addressed with simple architectures leveraging pretrained geometry backbones, and that the strong spatial understanding of DA3 will benefit other 3DD vision tasks.
Table 4: Comparisons with SOTA methods on NVS task. We report NVS comparsions with exisiting forward 3DGS models and counterparts using other backbones.
In-domain Dataset
Out-of-domain Datasets
Methods
DL3DV
Tanks& Temples
MegaDepth
PSNRT
LPIPS
PSNR1
LPIPS
PSNR LPIPS]
pixelSplat
16.55
0.480
13.81
0.558
13.87
0.561
MVSplat
18.13
0.393
14.81
0,508
14.67
0.533
DepthSplat
19.24
0.322
15.80
0.418
15.90
0.450
Fast3R
19.30
0.320
16.24
0.409
16.43
0.421
MV-DUS&BR
20.01
0.294
17.04
0.370
16.20
0.437
VGGT
20.96
0.253
17.18
0.347
16.45
0.417
DA3
21.33
0.241
18.10
0.311
17.89
0.351
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Table 6: Ablations for single transformer. We evaluate three architectural designs with comparable model sizes. The best and second best are highlighted.
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
Methods
Auc31
FIf
Auc31
FIf
Auc31
CDJ
Auc31
F11
Auc31
FI1
Proposed Arch.
39.2
47.0
21.0
55.4
45.8
3.82
26.2
47.6
30.3
51.1
b. VGGT Style
3.72
14.5
2.31
27.4
1.38
6.93
0.97
21.4
2.03
12.2
Full Alt.
24.7
29.3
13.1
51.9
44.6
4.23
21.1
48.6
27.7
47.5
4.2 SUFFICIENCY OF THE DEPTH--AY REPRESENTATION
To validate our depth-ray representation, we compare different prediction combinations in Tab. 5. All models use a ViT-L backbone with identical training settings (view size: 10. batch size: 128, steps: 120k). We evaluate four prediction heads: 1) depth for dense depth maps: 2) ped for direct 3D point clouds; 3) cam for 9-DoF camera pose (t, q. f); and 4) our proposed ray, predicting per-pixel ray maps (Sec. 2.1). The ray head uses Dual-DDP architecture, while ped uses separate DPT head. For models without ped, point clouds are obtained by combining depth with camera parameters from ray or cam.
Point cloud prediction is insufficient. Directly predicting point clouds (pcd) performs poorly (Avg: 31.6 Auc3, 51.5 F1), as the point cloud representation inherently lacks the geometric struc- ture needed for accurate camera pose estimation. Adding point cloud supervision to the depth-ray model (depth + ray + ped) yields marginal improvements on some datasets but actually degrades performance others, achieving only 36.4 Auc3 and 56.5 F1 average.
Depth-ray representation excels. Our minimal depth + ray configuration achieves strong and balanced performance (36.0 Auc3, 56.4 F1), significantly outperforming depth cam (14.1 Auc3, 40.2 FI) by over 155% relative gain in Auc3. Notably, depth ray also surpasses depth + ray + ped despite being simpler, demonstrating that the point cloud head introduces unnecessary complexity without consistent benefits. The depth-ray formulation effectively captures both metric depth and camera geometry in a unified framework.
We adopt depth + ray- + cam as our final configuration. Since the auxiliary cam head incurs neg- ligible computational overhead (~0..1% % of backbone cost) and significantly faster than extracting camera parameters from ray maps through optimization (0.46ms v.s 8. 8.60ms on an A100 GPU), we include no practical cost.
4.3 SUFFICIENCY OF A SINGLE PLAIN TRANSFORMER
We compare a standard VIT-L backbone with a VGGT-style architecture that stacks two distinct transformers, tripling the block count. For fair capacity comparison, the VGGT-style model uses smaller ViT-B backbones, yielding a similar parameter size to our ViT-I.. Our backbone supports two attention strategies: Full Alt., which alternates soossview//vithinview attention in all layers (L = Lg). and our default partial alternation. As shown in Table 6, the VGGT-style model drops 79.8% of our baseline performance, confirming the superiority of single-transformer design at similar scale. We attribute this gap to full pretraining of our backbone versus two-thirds untrained blocks in VGGT. Moreover, the Full Alt. variant degrades across nearly all metries except F1 on 7Scenes indicatindniinggnn that partial alternation is the more effective and robust strategy.
RELATED WORK
Multi-view visual geometry estimation. Traditional systems (Schönberger and Frahm, 2016; Schönberger et al., 2016) decompose reconstruction into feature detection and matching, robust relative pose estimation, incremental or global SIM with bundle adjustment, and dense multi-view stereo for per-view depth and fused point clouds. These methods remain strong well-textured scenes, but their modularity and brittle correspondences complicate robustness under low texture, specularities, large viewpoint changes. Early learning methods injected robustness the com- ponent level: learned detectors (DeTone et al., 2018), descriptors for matching (Dusmanu et al., 2019), and differentiable optimization layers that expose pose/depth updates to gradient flow (He

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et al., 2024; Guo et al., 2025: Pan et al., 2024a). On the dense side, cost-volume networks (Yao et al., 2018; Xu et al., 2023; 2025b) for MVS replaced hand-crafted regularization with 3D CNNs, improving depth accuracy especially at large baselines and thin structures compared with classical PatchMatch. Early end-to-end approaches (Teed and Deng, 2018; Wang et al., 2024a) moved be- yond modular SfM/MVS pipelines by directly regressing camera poses and per-image depths from pairs of images. These approaches reduced engineering complexity and demonstrated the feasibility of learned joint depth pose estimation, but they often struggled with scalability, generalization, and handling arbitrary input cardinalities.
turning point came with DUSt3 (Wang et al., 2024b), which leveraged transformers to directly predict point map between two views and compute both depth and relative pose in a purely feed- forward manner. This work laid the foundation for subsequent transformer-basee methods aiming to unify multi-view geometry estimation at scale. Follow-up models extended this paradigm with multi-view inputs (Yang et al., 2025a; Wang et al., 2025b; Tang et al., 2025), video input (Zhang et al., 2025a; Wang et al., 2025b; Murai et al., 2025), robust correspondence modeling (Leroy al., 2024), camera parameter injection (Jang et al., 2025; Keetha et al., 2025), view synthesis (Zhang et al., 2025b), and multi-mode prior prompting (Liu et al., 2025). Among these, Wang et al. (2025a) push accuracy to a new level through large-scale training, a multi-stage architecture, and redundancy in design. Parallel to this transformeer-entriic evolution, a complementary line of works (Zhang et al., 2024; Lu et al., 2025) explores diffusion models for ray-based camera estimation. These methods perform inference via iterative sampling, incurring higher computational cost than single- pass predictors, In contrast, we focus on minimal modeling strategy built around a single, simple transformer.
Monocular depth estimation. Early monocular depth estimation methods relied on fully super- vised learning on single-domain datasets, which often produced models specialized to either indoor rooms (Silberman et al., 2012) outdoor driving scenes (Geiger et al., 2013). These early deep models achieved good accuracy within their training domain but struggled to generalize to novel environments, highlighting the challenge of cross-domain depth prediction. Modern generalist ap- proaches (Yang et al., 2024a;b; Wang et al., 2025c; Bochkovskii et al., 2024; Yin et al., 2023; Ke al., 2024) exemplify this trend by leveraging massive multi-dataset training and advanced archi- tectures like vision transformers (Ranftl et al., 2021) or DiT (Peebles and Xie, 2023). Trained on millions of images, they learn broad visual cues and incorporate techniques such as infirnninvariant depth normalization. Recent efforts further push toward pixel-perfect and uubitrary-resolution depth estimation (Xu et al., 2025a; 2026; Yu et al., 2026). producing fine-grained, dictions through pixel-space diffusion or neural implicit representations. In contrast, our method is primarily designed for unified visual geometry estimation task, yet still demonstrates competitive monocular depth performance.
pre-
6 CONCLUSION AND DISCUSSION
Depth Anything 3 shows that plain transformer, trained on depth-and-ray targets with teacher- student supervision, can unify any-view geometry without ornate architectures. Scale-aware depth, per-pixel rays, and adaptive cross-view attention let the model inherit strong pretrained features while remaining lightweight and easy to extend. On the proposed visual geometry benchmark the approach sets new pose and reconstruction records, with both giant and compact variants surpassing prior models, while the same backbone powers efficient feed-forward novel view synthesis model.
We view Depth Anything as step toward versatile 3D foundation models. Future work can extend its reasoning to dynamic scenes, integrate language and interaction cues, and explore larger- scale pretraining to close the loop between geometry understanding and actionable world models. We hope the model and dataset releases, benchmark, and simple modeling principles offered here catalyze broader research on general- purpose 3D perception.
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A METHOD DETAILS
A.1 DERIVING CAMERA PARAMETERS FROM THE RAY MAP
Given an imput image / E #xxx3 the corresponding ray map denoted by M 12 xxxx This map comprises per-pixel ray origins, stored in the first three channels (M(;;;;; 3)), and ray directions, stored the last three (M(:;,,::)
First, the camera center te is estimated by averaging the per-pixel ray origin vectors:
H W
t. HXW
w(h,w,33)
(1)
To estimate the rotation R and intrinsics K, we formulate the problem as finding a homography H. We begin by defining an "identity" camera with an identity intrinsics matrix, K For given pixel its corresponding ray direction in this canonical camera's coordinate system is simply d, = K p p. The transformation from this canonical ray d, the ray direction d in the target camera's i coordinate system is given by d KRd,- This establishes direct homography relationship, H KR. between the two sets of rays. We can then solve for this homography by minimizing the geometric error between the transformed canonical rays and set of pre-computed target rays, M(h, w, :). This leads to the following optimization problem:
H
W
H arg min
Hph. x M(h, w, 3):]
(2)
This is a standard least-squares problem that can be efficiently solved using the Direct Linear Trans- form (DLT) algorithm (Abdel-Aziz et al., 2015). Once the optimal homography H* is found, recover the camera parameters. Since the intrinsics matrix K is upper-triangular and the rotation matrix R is orthonormal, can uniquely decompose H" using RQ decomposition obtain K, R.
A.2 CAMERA HEAD RAY-BASED POSE ESTIMATION
Our model includes both a ray prediction head and an optional camera head for pose estimation. The ray prediction is essential for achieving high pose accuracy during training, as provides dense per-pixel geometric supervision. The camera head, while optional, offers significant advantage for inference speed.
Computational efficiency. Directly predicting camera parameters from the camera head is sub- stantially faster than solving for pose from the ray map. Specifically, direct camera head prediction takes only 0.46 whereas solving pose from the ray map using the DLT-based optimization de- scribed in Sec. A.1 requires approximately 8.60 ms (measured on an A100 GPU, averaged over a 49-image scene; for reference, the feature forward pass with image token attention takes 43 ms). This represents an 18.7x x speedup.
Accuracy trade-off. While the camera head provides faster inference, ray-based pose estimation achieves slightly higher accuracy. As shown in Tab. 7, extracting pose from ray maps yields an average Auc3 of 68.0 compared to 63.8 from the camera head. However, the camera head still de- livers strong performance across all datasets, making it a practical choice for applications requiring real-time inference.
In our final model, we include both heads: the ray head provides dense geometric predictions es- sential for training and high- aegh-accrrcyy applications, while the camera head enables faster inference when speed is prioritized. Since the camera head processes only one token per view, its computa- tional overhead is negligible (~0..1% 1% of backbone cost).
A.3 PIXEL-WISE RAY ORIGIN PREDICTION
In ray map representation, each pixel predicts both ray origin and ray direction. An al- ternative design would be to predict a single global ray origin (camera center) using an MLP. We experimentally observed that predicted per-pixel ray origins exhibit extremely small variance and
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Table 7: Comparison between camera head and ray-based pose estimation for Dia-Giant. Time is measured for pose extraction per view on an A100 GPU.
Method
Time (ms)
Avg
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
Camera head
0.46
63.8
81.7
39.3
85.6
29.2
83.2
Ray-based
8.60
68.0
88.6
42.4
89.3
29.6
89.9
Table 8: Ablation study on ray origin prediction. We compare per-pixel ray origin prediction (depth ray cam) with single MLP-based global prediction (depth ray-st cam).
Method
Avg
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
depth ray cam
35.1
37.2
22.3
56.3
25.7
34.1
depth ray-st cam
32.2
28.7
22.7
48.8
27.0
33.9
essentially constant across pixels. As shown in Tab. 8, replacing per-pixel prediction with a sin- gle MLP-based prediction (depth + ray-st + cam) leads to performance drop (average Auc3: 35.1
+ 32.2), suggesting that the dense formulation provides better performance.
A.4 DETAILS TRAINING OBJECTIVE
We define the loss terms in Equation 2.3 as follows.
CD D: D.) = Zu
Σ mp (De.p DD - Dpl de log Dc.p) PEN
where Da.p denotes the confidence of depth Dp. All loss terms are based on the l norm, with weights set to and B 1. The gradient loss, Lyaad penalizes the depth gradients:
Laga = IVV V.D + +V, V,DI11
where Vo and V, are the horizontal and vertical finite difference operators. This loss preserves sharp edges while ensuring smoothness in planar regions.
A.5 DEPTH ANYTHING TEACHER MODEL
As shown in Fig. 7, the real-world datasets are of poor quality, thus we train the teacher model exclusively synthetic data to provide super- vision for real-world data.
DL3DV Colmap
CošdV2 Colmap
Data scaling. Following DA2, train the teacher model exclusively on synthetic data to achieve finer geometric detail. However, the synthetic datasets used in DA2 are relatively limited. In DA3, substantially expand the training corpus to include: Hypersim (Roberts al., 2021), TartanAir (Wang et al., 2020), IRS (Wang et al., 2019), vKITTI2 (Cabon al.. 2020), BlendedMVS (Yao al., 2020),
WildRGBO LIDAR
Figure 7: Poor quality real-world datasets. We show some examples of the poor quality real- world datasets.
SPRING (Mehl et al., 2023), MVSSynth (Huang et al., 2018). UnrealStereo4K (Zhang et al., 2018) (018), GTA-SfM (Wang and Shen, 2020), TauAgent (Gil et al., 2021). KenBurns Niklaus al. (2019), MatrixCity (Li et al., 2023), EDEN (Le et al., 2021). ReplicaGSO (Straub et al., 2019a), Urban- Syn (Gómez et al., 2025), PointOdyssey (Zheng et al., 2023), Structured3D (Zheng et al., 2020), Objaverse (Deitke et al., 2023), Trellis (Xiang al., 2024), and OmniObject (Wu et al., 2023). This collection spans indoor, outdoor, object-centric, and diverse in-the-wild scenes, improving general- ization of the teacher model.
Depth representation. Unlike DA2, which predicts scale-shift ihift-invariant disparity, our teacher out- puts scale-shif--nnvariant depth. Depth is preferable for downstream tasks, such metric depth
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estimation and multiview geometry, that directly operate in depth space rather than disparity. To address depth's reduced sensitivity for near-camera regions comparing to disparity, we predict ex- ponential depth instead of linear depth, enhancing discrimination at small distances.
Training objectives. For geometric supervision, addition to a standard depth-gradient loss, we adopt ROE alignment with the global-local loss introduced in Wang et al. (2025c). To further refine local geometry. we introduce a -etince-weeighted surface-norme loss. For each center pixel, sample four neighboring points and compute unnormalized normals n4, We then weight these normals by:
W1
n2
n;
(4)
which downweights contributions from neighbors farther from the center, yielding mean normal closer to the true local surface normal:
7m
(5)
n2
1=0
The final normal loss
LN = El Eiim: nem)
E( n:)
(6)
1=0
where & denotes the angular error between normals. Ground truth is undefined in sky regions and in background areas of object-only datasets. To prevent these regions from degrading the depth prediction and facilitate downstream use. we we jointly predict a sky mask and an object mask aligned with the depth output, supervised with MSE loss. The overall training objective is
= LT = C&Lyrad + fagl + LN + Luky + Lobj
where 0.5. Here, fgad fgl. Lay, and Lobj denote the gradient loss, global- local loss, sky-mask loss, and object- mask loss, respectively.
A.6 VISUAL RENDERING DETAILS.
The NVS model is fine-tuned with two training objectives, namely photometric loss (i.e., LMSE and LLPIP) on rendered novel views and ocale-shift-invariin depth loss Lp on the estimated depth of observed views, following the teacher-student learning paradigm (Sec. 2.3).
B VISUAL GEOMETRY BENCHMARK
B. MORE DETAILS ABOUT EVALUATION PIPELINE
Pose estimation. For each scene, select all available images; if the total number exceeds the limit, we randomly sample 100 images using a fixed random seed. The selected images are then processed through feed-forward model to generate consistent pose and depth estimations, after which the pose accuracy is computed.
Geometry estimation. For the same image set, we perform reconstruction using the predicted poses together with the predicted depths. To align the reconstructed point cloud with the ground- truth, we employ evo (Umeyama, 2002) to align the predicted poses to the ground-truth poses, obtaining a transformation that maps the reconstruction into the ground-truth coordinate system. To improve robustness, we adopt RANSAC-
alignment procedure. Specifically, we repeatedly apply evo on randomly sampled pose subsets and evaluate each candidate transformation by count- ing the number of inlier poses, where inliers are defined as those with translation errors below the median of the overall pose deviations. The transformation with the largest inlier set is then chosen and applied to fuse the aligned predicted point cloud with the predicted depth maps by TSDF fu- sion. Finally, reconstruction quality assessed by comparing the aligned reconstruction with the ground-truth point cloud using the metrics described in Sec. B.3.
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Visual rendering. For each testing scene, the number of images typically ranges from 300 to 400 across all benchmark datasets. We sample one out of every 8 images as target novel views for evaluation. From the remaining viewpoints, dataset and apply farthest point sampling, considering both camera translation and rotation distances, select 12 images input context views. For DL3DV, use the official Benchmark set for testing. For Tanks and Temples, all Training Data scenes are included except Courthouse, For MegaDepth, we select scenes numbered from 5000 501 8, as these are most suitable for NVS.
COLMAP camera poses provided by each
B.2 POSE METRICS
For assessing pose estimation, we follow the evaluation protocol introduced in Wang al. (2025a; 2023) and report results using the AUC. This metric is derived from two components: Relative Rota- tion Accuracy (RRA) and Relative Translation Accuracy (RTA). RRA and RTA quantify the angular deviation in rotation and translation, respectively, between two images. Each error is compared against set of thresholds obtain accuracy values. AUC is then computed as the integral of the accuracy- accuracy-threshole threshorllld curve, where the curve is determined by the smaller of RRA and RTA at each threshold. To illustrate performance under different tolerance levels, primarily report results thresholds of 3 and 30.
B.3 RECONSTRUTION METRICS
Let G denote the ground-truth point set and R. the reconstructed point set under evaluation. We measure accuracy using dist(R -> 9) and completeness using dist(G -> R.) following Aanæs et al. (2016). The Chamfer Distance (CD) is then defined as the average of these two terms. Based on these distances, we define the precision and recall of the reconstruction R with respect to distance threshold d. Precision is given by R. Σ di + g) < d/. and recall by s Σ dist(G; >
R)- < d). where H-] denotes the Iverson bracket Knapitsch al. (2017b). To jointly capture both measures, we report the Fi-score, computed as F1 2xprccision xrecal
precision recall
B.4 DATASETS
Our benchmark is built on five datasets: HiRoom, ETH3D (Schops al., 2017), DTU (Aanæs et al., 2016), 7Scenes (Shotton et al., 2013), and ScanNet++ (Yeshwanth et al., 2023). Together, they cover diverse scenarios ranging from object-centric captures to complex indoor and outdoor environments, and are widely adopted in prior research. Below, we present more details about the dataset preparation process.
HiRoom Blender-rendered synthetic dataset comprising 30 indoor living scenes created by professional artists. We threshold of 0.05m for the Fl reconstruction metric calculation. For TSDF fusion, we set the parameters voxel size to 0.007m.
ETH3D provides high-resolution indoor and outdoor images with ground-truth depth from laser sensors. We aggregate the ground-trute depth maps with TSDF fusion for GT 3D shapes. We select 11 scenes: courtyard, electro, kicker, pipes, relief, delivery area, facade, office, playground, relief 2, terrain, for the benchmark. All frames are used in the evaluation. We use threshold of 0.25 for the F1 reconstruction metric calculation. For TSDF fusion, we set the parameters voxel size to 0.039m.
iTU is an indoor dataset consisting of 124 different objects, each scene is recorded from 49 views. provides ground-truth point clouds collected under well-controlled conditions. We evaluate models on the 22 evaluation scans of the DTU dataset following Yao et al. (2018). We adopt the RMBG 2.0 Zheng et al. (2024) remove meaningless background pixels and the default depth fusion strategy proposed in Zhang et al. (2023), All frames are used in the evaluation.
7Scenes is challenging real-world dataset, consisting of low-resolution images with severe motion blurs for in-door scenes. We follow the implementation in Zhu et al. (2024) to fuse RGBD images with TSDF fusion and prepare ground-truth 3D shapes. We downsample the number of frames for each scene by 11 to faciliate evaluation. We use a threshold d 0.05m for the Fl reconstruction metric calculation. For TSDF fusion, we set the parameters voxel size 0.007m.
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ScanNet++ is extensive indoor dataset providing high-resolution images, depth maps from iPhone LiDAR, and high-resolution depth maps sampled from reconstructions of laser seans. We select 20 scenes for the benchmark. As depth maps from iPhone LiDAR lack of invalid ground-truth indicators, we use depth maps sampled from reconstructions of laser scans as ground-truth depth by default. We aggregate the ground-truth depth maps with TSDF fusion for GT 3D shapes. We downsample the number of frames for each scene by 5 to faciliate evaluation. We use threshold d of 0.05m for the Fl reconstruction metric calculation. For TSDF fusion, we the parameters voxel size to 0.02m.
C EXPERIMENTAL SETUP
C.I TRAING DATASETS
We provide our training datasets in Table Note that for datasets with potential overlap between training and testing (ScanNet++ and DL3DV), we ensure strict separation at the scene level, i.e., scenes in training and testing are mutually exclus
Table 9: Datasets used in Depth Anything 3, including number of scenes, data type, and usage.
Usage
Dataset
#Scenes
Data Type
HiRoom (ours)
29
Synthetic
ETH3D (Schops et al., 2017)
11
LiDAR
Pose-geometry
DTU (Aanæs al., 2016)
22
LiDAR
benchmark
7Scenes (Shotton al., 2013)
7
LiDAR
ScanNet++ (Yeshwanth et al., 2023)
20
LiDAR
AriaDigitalTwin (Pan et al., 2023)
237
Synthetic
AriaSyntheticENNV (Pan et al., 2023)
99950
Synthetic
ArkitScenes (Baruch et al., 2021)
4388
LiDAR
BlendedMVS (Yao al., 2020)
503
3D Recon
Co3dv2 (Reizenstein et al., 2021)
30616
Colmap
DL.3DV (Ling al., 2024)
6379
Colmap
HyperSim (Roberts et al., 2021)
344
Synthetic
MapFree (Amold et al., 2022)
921
Colmap
MegaDDpthh (Li and Snavely, 2018)
268
Colmap
MegaSynth (Jiang et al., 2025)
6049
Synthetic
cose-geometry
MvsSynth (Huang et al., 2018)
121
Synthetic
Training
Objaverse (Deitke et al., 2023)
505557
Synthetic
Omniobject (Wu al., 2023)
5885
Synthetic
PointOdyssey (Zheng et al., 2023)
44
Synthetic
Replica MAPP (Straub al., 2019b)
17
Synthetic
ScanNet++ (Yeshwanth et al., 2023)
230
LiDAR
ScenenetRGBD (McCormac et al., 2017)
16866
Synthetic
TartanAir (Wang et al., 2020)
355
Synthetic
Trellis (Xiang et al., 2024)
557408
Synthetic
viitti2 (Cabon al., 2020)
50
Synthetic
WildRGBD (Xia et al., 2024)
23050
LiDAR
NVS Training
DL3DV (Ling et al., 2024)
10015
Colmap
Tanks and Temples (Knapitsch et al., 2017a)
6
Colmap
NVS Benchmark
MegaDepth (Li and Snavely, 2018)
19
Colmap
DL3DD (Ling al., 2024)
140
Colmap
C.2 TRAINING DETAILS
We train our model on 128 H100 GPUs for 200k steps, using an 8k-step warm-up and a peak learning rate of 2 10-1 Training image resolutions randomly sampled from 504 x 504, 504 378,
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Table 10: Comparison of Models with Parameters and Running Speed. The maximum number of images was tested on an 80 GB A100 GPU. If we store some intermediate tokens in CPU memory, we could process many more images. The running speed was measured on an A100 GPU with scene of 32 images, and we report the average speed per image.
Parameters
Model
Max of Images
Running Speed
Backbone
DualDPT
CameraHead
(GGT((efereece)
400-500
0.91B
0.064B
0.22B
34.1 FPS
DA3--Giant
900-1000
1.1308
0,050B
0,488
37.6 FPS
DA3-Large
1500-1600
0.300B
0,047B
0.21B
78.37 FPS
DA3-Base
2100-2200
0.086B
0.045B
0.12B
126.5 FPS
DA3-Small
4000-4100
0.022B
0.043B
0.03B
160.5 FPS
Table 11: Ablation studies on teacher model geometry. Depth-based geometry achieves d1 com- parable to disparity, while attaining the best AbsRel and SqRel among the three geometry represen- tations.
Geometry 61 (1)
AbsRel (J)
SqRe1 (1)
Disparity
0.919
0.095
1,033
Pointmap
0.912
0.096
0.693
Depth 0.918
0.089
0.637
504 x 336, 504 x 280, 336 x 504. For the 504 x 504 resolution, the number of views is sampled uniformly from [2. 18). The batch size is dynamically adjusted to keep the token count per step approximately constant. Supervision transitions from ground-truth depth to teacher-moder labels at 120k steps. Pose conditioning is randomly activated during training with probability 0.I.
C.3 BASELINES
VGGT (Wang et al., 2025a) is an end-to-end transformer that jointly predicts camera parameters, depth, and 3D points from equivariant design to recover many views. Pi3 (Wang et al., 2025d) further adopts permutation- cameras and scale-invariant point maps from unordered images. MapAnything (Keetha et al., 2025) provides feed-forward framework that also take camera pose as input for dense geometric prediction. Fast3R (Yang et al., 2025b) extends point-map regression hundreds or even thousands of images a single forward pass. Finally, DUSt3R (Wang et al., 2024c) tackles uncalibrated image pairs by regressing point maps and aligning them globally. Our method is similar to VGGT (Wang al., 2025a), but adopts new architecture and a different camera representation, and it is orthogonal to Pi3 (Wang et al., 2025d).
D ADDITIONAL ANALYSIS
We present additonal analysis on Parameters, max number of images and running speed in Tab. 10
E ADDITIONAL EXPERIMENTS
E TEACHER MODEL
Teacher model training. We ablate the teacher using VIT-L backbone with batch size 64. Eval- uation follows the DA2 benchmark protocol, and we additionally report Squared Relative Error (SqRel), defined as the mean squared error between predictions and ground truth normalized by the ground truth. Across geometries (Tab. 11), depth emerges the most effective target compared with disparity and point maps. For training objectives (Tab. 12), the full teacher loss proposed in this work outperforms both the DA2 loss and variant without proposed normal-loss term. Finally, data scaling contribute notably to performance (Tab. 13): upgrading datasets from V2 to V3 and adopting multi-resolution training strategy yield consistent improvements in the teacher's final metrics.
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Table 12: Ablation studies on teacher model loss. The full teacher-loss configuration yields the strongest performance, outperforming the other two loss variants across all metrics.
Loss
AbsRel (J)
SqRel (1)
MAE-Loss
0.918
Teacher-Loss w/o Dist. Nor.
0.918
0,087
0.600
Full teacher-loss
0.919
0.087
0.596
Table 13: Ablation studies on teacher model data, V2 denotes the datasets used to train the DA2 teacher model. V3 denotes those used for the DA3 teacher model. Traning with V3 datasets and multi-resolution strategy improves teacher model performance.
Data
61 (1)
AhsRel (L)
SqRel (L)
V2
0.919
0.087
0.596
V3
0.929
0.079
0.508
V3 multi-res.
0.938
0.072
0.452
E.2 ADDITIONAL ABLATIONS FOR DEPTH ANYTHING
Dual-DPT Head. We assess the effectiveness of the dual-DPT head via an ablation in which two separate DPT heads predict depth and ray maps independently. Results are reported in Tab, 14, item (d). Compared with the model equipped with the dual-DPT head, the variant without it shows consistent drops across metries, confirming the effectiveness of our dual-DPT design.
Teacher model supervision. We ablate the use of teacher model labels as supervision, with quan- titative results reported in Tab. 14, item (c). Training without teacher labels yields a slight improve- ment DTU but leads to performance drops on 7Scenes and ScanNet++. Notably, the degradation pronounced on HiRoom. We attribute this to HiRoom's 's synthetic nature and its ground truth containing abundant fine structures; supervision from the teacher helps the student capture such de- tails more accurately. Qualitative comparisons in Fig. 8 corroborate this trend: models trained with leaeher-label supervision produce depth maps with substantially richer detail and finer structures.
Image
w/o Teacher
w/ Teacher
Figure 8: Comparison of teacher-label supervision. Supervision with teacher seeberr-geeerted labels yields depth maps with substantially richer detail and finer structures.
Pose conditioning. To assess the pose-conditioning module, we ablate it on the ViT-L backbone and report results in Tab. 14, items (f) and (g). Unlike other entries in the table, these two are evaluated with ground-truth pose fusion (marked with "*"), whereas the rest use predicted pose fu- sion. Across metrics, configurations with pose conditioning consistently outperform those without, confirming the effectiveness of the pose-conditioning module.
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Table 14: More ablations for single transformer. We evaluate the effects of the dual-DPT head, teacher label supervision, and the pose conditioning module. Methods marked with are evaluated with ground-truth pose fusion.
HiRoom
ETH3D
DTU
7Scenes
ScanNet++
Methods
Auc31
FI
Auc31
F1
Auc31
CD2
Auc31
FI
Auc31
FI
Full Model
39.2
47.0
21.0
55.4
45.8
3.82
26.2
47.6
30.3
51.1
d. w/o Dual DPT
5.59
11.5
13.6
33.4
21.7
5.14
14.2
49.4
26.5
46,6
c. w/o Teacher
11.2
16.0
16.2
57.6
52.5
3.29
23.3
40.3
26.2
47.7
E. w/o Pose Cond.*
65.8
63.2
3.65
58.4
62.8
w/ Pose Cond.*
73.8
70.9
2.14
46.0
65.7
E.3 ADDITIONAL COMPARISONS FOR VISUAL RENDERING
Additional implementation details. We retrain all compared feed-forward 3DGS models, ensur- ing that the training configuration matches the testing setup by using 12 input context views selected through farthest point sampling. We apply engineering optimizations such as flash attention and fully shared data parallelism enable all models to process 12 input views efficiently. Depth train- ing loss are incorporated for all baselines to ensure stable training and convergence. All models are trained 8 A100 GPUs for 200K steps with a batch size of except for pixelSplat, which trained for 100K steps due to rather slow epipolar attention. All results are reported at H x W 270 x 480.
Visual quality analysis. We present visual comparisons with other models Fig. under novel view synthesis settings. As illustrated, simply augmenting our DA3 model with 3D Gaussian DPT head yields significantly improved rendering quality over cxisting oftte-of-thearr approaches. Our model demonstrates particular strength in challenging regions, such as thin structures (e.g.. columns in the first and third scenes) and large-scale outdoor environments with wide-baseline input views (last two scenes), shown in Fig. 9. These results underscore the importance of robust geometry backbone for high-quality visual rendering, consistent with our quantitative findings in Tab, 4. We anticipate that the strong geometric understanding of DA3 will also benefit other 3D vision tasks.
DISCLOSURE
The authors acknowledge the use of large language models (LLMs) solely for grammar checking and language refinement of this manuscript. The input images in the teaser demo were extracted from a publicly available YouTube video (Drones, 2024), credited the original creator.
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Inputs
MVsplat
DepthSplat
Fast3R
VGGT
DA3 (Ours)
Ground Truth
Figure 9: Qualitative comparisons with state-of-the-an methods for visual rendering. The first column shows the selected input views, while the remaining columns display novel views rendered by comparison models and ground truth. For each scene, two rendered novel viewpoints are pre- sented in consecutive rows. The first three scenes are from DL3DV, the following two are from Tanks and Temples, and the last three from MegaDepth. Compared to other methods, model consistently achieves superior rendering quality across diverse and challenging scenes.
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Reviews:
[{"review_id": "88WiRwkmUt", "reviewer": "Reviewer_xgar", "summary": "The paper presents Depth Anything 3, a single model that unifies geometric understanding across any number of views. The method jointly predicts a depth map and a ray map, using a ViT backbone with input adaptive cross view attention. A Dual DPT head shares reassembly modules and branches only at the final fusion stage to jointly infer depth and rays. Experiments across diverse benchmarks show consistent state of the art results in pose estimation, geometric reconstruction, and feed forward novel view synthesis, demonstrating strong accuracy and efficiency.", "questions": "I wonder whether Depth Anything 3 is the most suitable title. The previous work is called Depth Anything v2, so if the intention is to follow the series it would be better to use Depth Anything v3 for consistency. Additionally, both v1 and v2 focus primarily on depth prediction. The current title can easily be read as another improvement targeted at depth prediction. It may be worth considering an alternative title that more clearly conveys the contribution..", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper presents a thoughtful analysis of what modalities are truly necessary for strong vision understanding tasks. It argues that depth together with a ray map is a minimal and sufficient target set. The ablation in Table 5 convincingly supports this claim by outperforming alternatives. Although recent work MapAnything also discusses incorporating ray maps into a unified representation, it is a contemporaneous work and does not needed to be considered here. \n\n2. The Dual DPT head is well designed. By sharing reassembly modules and branching only at the final fusion stage, the approach enforces pixel level alignment while avoiding redundant representations, which benefits both accuracy and efficiency. \n\n3. The experimental study is extensive and persuasive. The method is validated across pose estimation, geometric reconstruction, and feed forward novel view synthesis, consistently achieving SOTA results.", "weaknesses": "I did not find any major weaknesses. While I know recent advances in this area, I am not fully confident about all technical nuances and distinctions among closely related methods. I am open to perspectives from other reviewers and will continue to track the discussion.", "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": "LVNsYgomgc", "reviewer": "Reviewer_vvhC", "summary": "This paper introduces Depth Anything 3. Different from Depth Anything and Depth Anything v2 that can only work with single image, Depth Anything 3 is able to process any number of images. Different from previous methods such as DUSt3R and VGGT, Depth Anything 3 simplify the architecture, making it more scalable to numerous images. In addition, a teacher-student paradigm is used to provide high-quality data. Depth Anything 3 achieves state-of-the-art performance in various tasks, including pose estimation, 3D reconstruction, and rendering.", "questions": "1. L142: the equation looks wrong. $P$ denotes the 3D point in world coordinate frame, $D_i(u,v) K_i^{-1} p$ denotes the 3D point in camera local frame. To make the equation correct, $R_i, t_i$ should represent camera pose (transformation from camera to world), instead of extrinsics (world to camera). \n\n2. Sec. 2.4: Is GS-DPT head the only optimizable module, i.e. backbone is fixed?\n\n3. L967: On ETH3D, is the tolerance 0.25 meter? Could the authors provide individual performance on each scene since the scale of scene vary a lot?\n\n4. Typo:\n    * L823: a “identity” camera -> an “identity” camera", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The architecture of Depth Anything 3 is simpler than previous methods. Depth Anything 3 uses a single vision transformer, while previous methods typically use vision transformer and following self- & cross-attention. Input-adaptive self-attention is used in vision transformer to enable cross-view attention without introducing new attention layers. With a simpler structure, Depth Anything 3 is able to process more images, which is meaningful for the future research. \n\n2. Extensive and thorough evaluation. Performance of pose estimation, 3D reconstruction, and rendering are thoroughly evaluated, where Depth Anything 3 achieves state-of-the-art performance. \n\n3. Ablation study of depth-ray representation shows that it explicitly outperforms previous representations, e.g. depth+pcd+cam used by VGGT.", "weaknesses": "1. In Table 1 and Table 2, I recommend adding some state-of-the-art methods that are not feed-forward models. This can help the readers have a better understanding of the performance difference between different methods. For example, classical pipelines generally outperform feed-forward models in 3D reconstruction.\n\n2. If the teacher is not used, would the performance degrade explicitly? Currently, I am not sure if the mainly improvement is from the powerful teacher.", "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": "lDUXJ3Wugl", "reviewer": "Reviewer_4H8r", "summary": "The authors address the problem of 3D geometry estimation. They argue that depth and ray predictions constitute the minimal set of 3D predictions necessary for rendering 3D geometry, demonstrating this is the optimal choice. They extend existing transformer architectures by introducing a cross-view interaction transformer layer to handle multi-view inputs. Their method achieves significantly superior performance compared to existing models in both pose estimation and geometry estimation.", "questions": "Please see the weakness section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper utilized depth and ray map representations to enable full 3D reconstruction from an arbitrary number of input images.\n- Discovered an effective architecture design that outperforms previous methods while requiring minimal modifications to DINOv2.\n- The paper demonstrates their model's effectiveness across various experimental settings.", "weaknesses": "- **Unclear advantage of depth+ray over point map:** To my knowledge, point maps can effectively represent various 3D information such as depth and pose, and a point map is essentially a combination of depth and ray maps. However, Table 5 shows that point maps hurt pose accuracy. What is the reason for this performance degradation? This finding appears to contradict the ablation study in VGGT, which argues that point map accuracy increases with multimodal outputs. I would like to see a more comprehensive analysis explaining why the combination of ray and depth maps outperforms point maps.\n- **Missing ablation studies with point maps:** I am curious about additional experiments in Table 5 calculate the metrics using point map representations not using ray map and depth map. Specifically, what are the results when training with: (1) point maps only, and (2) point maps combined with ray maps and depth maps?\n- **Pixel-wise ray map origin justification:** I understand that the origin of the ray map is identical for each image. Is there a specific reason to set the ray map origin in a pixel-wise manner? What is the benefit of this design choice?\n- **Distinction between camera head and ray predictions:** What is the precise difference between the camera head and ray predictions? I am also curious about the performance gap between these two approaches. To my knowledge, camera parameters can generate ray maps, and conversely, ray maps can also be used to estimate camera parameters. Could you clarify the relationship and trade-offs between these representations?", "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": "DbbRKpAX0D", "reviewer": "Reviewer_Yy63", "summary": "The paper presents Depth Anything 3 (DA3), which learns to predict dense depth maps and ray maps for estimating 3D geometry and camera poses. DA3 focuses in simplifying geometric understanding both from its model architecture, and also by using depth ray as representation for prediction. The architecture simply adopts a pre-trained DINOv2, which has been powerful in various 2D and 3D tasks, and rearranges the tokens for certain layers to compute the full attention for image tokens across different frames, allowing the information exchange between views. Furthermore, DA3 predicts camera rays instead of point maps, which consists of the camera origin and the direction for each of the pixels, and thoroughly demonstrates that camera rays serve as better representations compared to point clouds.", "questions": "1. Considering that all pixels from the same camera should have identical origins, it is interesting to see that all of the pixels are required to predict the origin in a dense manner, in addition to the ray direction. Is this simply a design choice, or does this have impact on training? It would also be interesting to see the variance of the predicted origin across each pixels within a single image, and study whether averaging all of the pixels for the pose estimation strategy is helpful.\n\n2. The idea for the architecture seems to share some ideas with ViTDet[1], as it modifies existing pre-trained ViTs to alternate between local/global attentions. However, it is interesting to see that limiting the blocks for cross-view attention was more effective for DA3, as opposed to the results from ViTDet, apart from the obvious efficiency gains. Could the authors provide further analysis on why Full Alt. performs worse? Could it be from the gap between the pre-training and the downstream task, where drastically modifying all of the layers within DINOv2 to all handle cross-view attention be problematic?\n\n[1] Li, Yanghao, et al. \"Exploring plain vision transformer backbones for object detection.\" European conference on computer vision. Cham: Springer Nature Switzerland, 2022.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper shares an interesting finding that understanding 3D geometry can be done in a simplistic manner, especially without specialized architecture for incorporating multiple views or enforcing geometric constraints. Despite that it has been a recent trend for applying more and more generic architecture in 3D tasks, it is very intriguing to see that it could be done within a pre-trained DINOv2 by tweaking some of its attention layers.\n\n2. The paper formulates a novel framework for predicting camera rays, which is the ray connecting the camera origin and pixels in the image plane. The results and the ablations thoroughly show that the combination of depth and camera ray suffices for effectively understanding 3D geometry, \n\n3. The proposed method establishes state-of-the-art performance across various geometry tasks, while having similar parameter counts. Furthermore, the method also shows strength in efficiency thanks to its simplistic architecture with having only few layers for cross-view understanding.\n\n4. The paper proposes a benchmark for evaluating visual geometry, and introduces HiRoom dataset which the authors will release in the future.", "weaknesses": "1. Some of the claims are not well justified, or seems to be overclaiming in some points.\n- L.157-158 (While point maps are insufficient to ensure consistency, redundant targets can improve pose accuracy but often introduce entanglement that compromises it.) What does the authors intend with \"entanglement\"? Does this mean that the results deduced from different heads could be problematic (e.g. pose from pose prediction head v. pose from point maps)? The authors should better elaborate the problems of \"redundant\" prediction to better establish their motivation for predicting camera rays.\n- The camera head, despite being optional, is specified to have 0.48B parameters for the Giant variant. Despite the authors show that the computation is negligible as it only has few tokens, having a camera head that has nearly half of the parameter count of the backbone does not seem \"lightweight\".\n\n2. It is a bit unclear on why depth+ray is more effective compared to point maps. To the reviewer's understanding, depth and ray actually seems like decoupling the point map prediction from Dust3r into two separate predictions. Why would this be better, asides from the empirical results?\n\n3. Although the teacher model seems crucial, the ablations seems to be missing.", "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 (Oral)