DA$^{2}$: Depth Anything in Any Direction
Authors: Haodong Li
Wangguandong Zheng
Jing He
Yuhao LIU
Xin Lin
Xin Yang
Ying-Cong Chen
Chunchao Guo
Venue: ICLR 2026 Poster (2026)
Keywords: Panoramas
Depth (Distance) Estimation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: DA$^{2}$ is a zero-shot, end-to-end panoramic depth estimator trained on large-scale curated data with a sphere-aware ViT to reduce distortions. It runs efficiently and surpasses prior methods by a large margin both qualitatively and quantitatively.

Abstract: Panorama has a full FoV (360$^\circ\times$180$^\circ$), offering a more complete visual description than perspective images.
Thanks to this characteristic, panoramic depth estimation is gaining increasing traction in 3D vision.
However, due to the scarcity of panoramic data, previous methods are often restricted to in-domain settings, leading to poor zero-shot generalization.
Furthermore, due to the spherical distortions inherent in panoramas, many approaches rely on perspective splitting (\textit{e.g.}, cubemaps),
which leads to suboptimal efficiency.
To address these challenges, we propose $\textbf{DA}$$^{\textbf{2}}$: $\textbf{D}$epth $\textbf{A}$nything in $\textbf{A}$ny $\textbf{D}$irection, an accurate, zero-shot generalizable, and fully end-to-end panoramic depth estimator.
Specifically, for scaling up panoramic data, we introduce a data curation engine for generating high-quality panoramic depth data from perspective, and create $\sim$543K panoramic RGB-depth pairs, bringing the total to $\sim$607K.
To further mitigate the spherical distortions, we present SphereViT, which explicitly leverages spherical coordinates to enforce the spherical geometric consistency in panoramic image features, yielding improved performance.
A comprehensive benchmark on multiple datasets clearly demonstrates DA$^{2}$'s SoTA performance, with an average 38\% improvement on AbsRel over the strongest zero-shot baseline.
Surprisingly, DA$^{2}$ even outperforms prior in-domain methods, highlighting its superior zero-shot generalization.
Moreover, as an end-to-end solution, DA$^{2}$ exhibits much higher efficiency over fusion-based approaches.
Both the code and the curated panoramic data have be released.
Project page: https://depth-any-in-any-dir.github.io/.

Published as a conference paper at ICLR 2026
DA DEPTH ANYTHING IN ANY DIRECTION
Haodong Li
Wangguandong Zheng', Jing He?, Yuhao Liu', Xin Ving-CCog Chen Chunchao Guo
Xin Lin', Xin Yang"
'Tencent Humyuan UUC San Diego 3 HKUST(GZZ) 4 HKUST
hal211 @ucsd. edu; yångcongchendust.. chunchagguo@gmail..co
Figure 1: Teaser of DA?. Powered by scage training data from our panoramic data curation en- gine, and the distortion cistortion-aware aware Sphere ViT, DA² predicts dense distance from a single 360 panorama, with remarkable geometric fidelity. The reconstructed 3D structures exhibit sharp geometric details and robust performance across diverse scenes, highlighting DA strong zero-shot generalization.
ABSTRACT
Panorama has full FoV (360 x 180%), offering a more complete visual descrip- tion than perspective images. Thanks to this characteristic, panoramic depth esti- mation is gaining increasing traction in 3D vision. However, due to the scarcity panoramic data, previous methods are often restricted to in-domain settings, leading poor zero-shot generalization. Furthermore, due the spherical distor- tions inherent in panoramas, many approaches rely on perspective splitting (e.g., cubemaps), which leads suboptimal efficiency. To address these challenges, we propose DA Depth Anything in Any Direction, an accurate, zero-shot generaliz- able, and fully end-to-end panoramic depth estimator. Specifically, for scaling up panoramic data, we introduce data curation engine for generating high-quality panoramic depth data from perspective, and create ~543K panoramic RGB depthepth pairs, bringing the total to ~607.. To further mitigate the spherical distortions, we present Sphere which explicitly leverages spherical coordinates enforce the spherical geometric consistency in panoramic image features, yielding improved performance. A comprehensive benchmark on multiple datasets clearly demon- strates DA SoTA performance, with average 38% improvement AbsRel over the strongest zero-shot baseline. Surprisingly, DA? even outperforms prior in-domain methods, highlighting its superior zero-shot generalization. Moreover,

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Published as a conference paper at ICLR 2026
an end-to-end solution, 2 exhibits much higher efficiency over fusion-based approaches. Both the code and the curated panoramic data have be released. Project page:
INTRODUCTION
Unlike the commonly used perspective images, panorama offers an immersive 360 x 180 view, capturing visual content from any direction. This wide FoV makes panorama an essential visual rep- resentation in computer vision, empowering a variety of exciting applications, such as AR/VR (Chen) al. 2023) and immersive visual generation (Yang et al.) 2025a Kalischek et al. 2025). However, immersive visual (2D) experiences alone are not enough. To push the new frontier of panoramic application scenarios, high-quality depth (3D) information from panoramas is crucially needed for 3D reconstruction and more advanced features such as 3D scene generation (Skywork AI, 2025] [i et al., 2025 Lu et al. 2025), physical simulation (Shah et al., 2025). etc. Inspired by this, we focuses estimating scale-invarian distancd from each panorama pixel to the sphere center (i.e., the 360 camera) in an end-to-end manner, with high-fidelity and strong zero-shot generalization.
Panoramic depth estimation is particularly valuable for applications requiring comprehensive spatial awareness. However, capturing or rendering panoramas is much more challenging than perspective images, panoramic depth data is much more limited in both quantity and diversity. Consequently, early methods were largely trained and tested in in-domain settings, with highly limited zero-shot generalization. Given the wealth of high-quality perspective depth data, is it possible to transform them into panoramic? Motivated by this, we propose data curation engine, transforming perspec- tive samples into high-quality panoramic data. Concretely, given a perspective RGB image with known horizontal and vertical FoVs, we first apply tion to map the image onto the spherical space. However, due to the limited FoV of perspective images (with a typical horizontal range of -0--90%, only small portion of the spherical space can be covered (as highlighted in Fig. 3 left sphere). Thus, such a P2E projected image can be viewed as an "incomplete" panorama. Then, panoramic out-painting will be performed to generate a "complete" panorama to match the input of our model, using an image-to-pannranee
(P2E) projec-
out-painter: FLUX-I2P (BFL, 22224 Tencent 2025). For the associated GT depth, we apply only the P2E projec- tion without out- out-painting, due to concerns on the absolute accuracy of out-painted depth. Overall, this data curation engine substantially boosts the quantity and diversity of panoramic data, and sig- nificantly strengthens the zero-shot performance of DA2, as shown in Fig. and Tab. 2
Panoramas typically use equirectangular projection (ERP to represent the 360 x 1880 visual space. However, 3D spherical space cannot be "losslessly" projected onto 2D plane. During the sphere- to-plane projection, distortions and stretching are inevitable, particularly near the poles. This spheri- cal distortion is analogous to the challenge in world map projection, where you never accurately express both the areas and shapes of each land. To mitigate the impact of spherical distortion, in- spired by the positional embeddings in Vision Transformers (ViTs), propose SphereViT- the main backbone of 2 Specifically, from the layout of ERP, we first compute the spherical angles (azimuth and polar) of each pixel in the camera-centric spherical coordinates. After that, we expand this two-channel angle field into the image feature dimension using sine-cosine basis embedding, forming the Spherical Embedding. Since all panoramas have the same full FoV, this spherical em- bedding can be fixed and reusable. Therefore, to inject spherical awareness, it's only necessary to let the image feature "attend" to the spherical embedding, but not vice versa tte spherical embedding doesn't need to be further refined. Consequently, rather than adding positional embeddings onto the image features before self-attention, as in standard ViTs (Vaswani et al.] 2017 Dosovitskiy et al.) 2020), SpT uses cross-attention: image features are regarded as queries and the spherical em- beddings as keys and values. This design lets the image feature explicitly attend to the panorama's 's spherical geometry, yielding distortion- aware representations and improved performance.
1 Pleass see Supp's Sec. for discussions on: metric, scale-invariant (biased), and affine-invariant (relative). We acknowledge the distinction between distance (d / + y² + 2²) and depth (d 2). We focus on scale-invariant distance prediction. Please allow us to use "depth" occasionally for readability and fluency. PRRP can represent full vertical FoV (i.e., 180%). If smaller than 1 180", cylindricaa projection can be used, such as the panoramic camera mode in mobile phones. Both can present full horizontal FoV (i.e., 360%).
2

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Published as a conference paper at ICLR 2026
Scaling Law of Model Errors vs Data Size
Scaling Law of Model Accuracies vs Data Size
0.
AbsRel RMSE
55.-
74-
24
15.0
512
14-
7.4
Panoramic depih seformance goined from perspective dota
IMSS
Panoramic death performace porned from perspoctive data
75-
22
233-
-774
410-
0g
100
Training [x100)
Training (x110))
Figure 2: Scaling-lav curves of model performance vs data size. Native, high-quality panoramic data is scarce, constraining the zero-shot generalization of panoramic depth estimators. With our data curation engine, DA achieves steadily and clearly higher performance as more perspective depth data are converted to panoramic form. Detailed numerical results are provided in Tab. 2
To validate DA we conduct a comprehensive benchmark scale-invariant distance combining multiple well-recognized evaluation datasets. However, due to the scarcity of panoramic data, ex- isting zero-shot approaches panoramic depth estimation are limited, whereas in perspective, there exist many powerful zero-shot methods. Therefore, to ensure more fair and comprehensive com- parison, following the panoramic depth estimation pipeline proposed by Wang et al. (2025c we
2024 Yin et al, also benchmark 2A against prior zero-shot perspective depth estimators (Hu et al.
2023 Piccinelli et al. 2024 2025b Wang al., 2025a d Bhat al. 2023 Yang al. 2024a by
He et al. 2024b). The results in Tab. I clearly demonstrate DA SoTA performance, with an aver-
age 38% improvement on AbsRel over the strongest zero-shot baseline. Notably, even surpasses prior in-domain methods, further underscoring its superior generalization ability. Beyond that, DA seamlessly supports various applications, such as panoramic multi-vier reconstruction, home deco- ration, and robotics simulation (please see our Supp's Sec. A}. Our key contributions are:
Panoramic data curation engine. We introduce data curation engine that generates high- quality panoramic depth data from perspective data, greatly scaling up the panoramic depth training data and substantially improving the zero-shot generalization ability of DA
SphereViT. We propose SphereViT the primary backbone of DA By directly leverag- ing the spherical coordinates of panoramas, ViT effectively mitigates the impact of spherical distortions and enhances the spherical geometry awareness of image features.
Comprehensive benchmark. Both zero-shot/ in-domain, panoramic / perspective meth- ods are compared to build a comprehensive benchmark for panoramic depth estimation.
So'T performance. Experimental results clearly demonstrate DA SoTA performance. DA even beats prior in-domain methods. It also enables many downstream applications.
2 RELATED WORKS
2.1 PERSPECTIVE DEPTHI ESTIMATION
Perspective depth estimation is being advanced very rapidly. Metric and scale-invariant depth mod- els. driven by large-scale training data, have achieved strong results, like UniDepth (Piccinelli al.
2024 2025b), Metric3D (Hu et al. 2024 YYn et al. 2023). DepthPro (Bochkovskiy et al.) (0225). and MoGi (Wang et al. 2025c d). Relative depth models also benefit greatly from scaling up the training data, like DepthAnything (Yang et al.] 2024a b). Another line of work fine-tunes massively
pre-trained generative models, e.g., Stable Diffusion (Rombach et al. [022 Ho et al. 2020 He et al.)
2024a Li et al. 2024b Liang et al. 2024: Gu et al. 2024), FLUX (BFL) 2024 Yang et al. 2025b),
with limited high-quality data, also yielding impressive results (Ke et al. 2024 He et al. 2024b
Wang et al.) 2025b; Li et al. 2024a). Despite these remarkable advances, perspective methods
main constrained by the limited FoV and cannot estimate depth in all directions simultaneously. In contrast, D targets full FoV depth estimation with strong zero-shot generalization.
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RGB
Partial Penorame
Pamorama
Far pains
P2E Prpp
FLUX-2P
Oulspainting
P9 Prp
laa Perspective Projection
Dapth
Equrestangular Projection
Equirectangular Projection (ERP)
Figure 3: Panoramic data curation engine. This module converts large-scale, high- high-quality perspec- tive RGB-depth pairs into full panoramas through P2E projection and panoramic out-painting using FLUX-I2P. dramatically scales up the panoramic depth training data, forming a solid training data foundation for DA?. The highlighted area on the spheres indicate the FoV coverage.
2.2 PANORAMIC DEPTH ESTIMATION
In-domain. Due the scarcity of panoramic data, most existing methods are constrained in- domain settings. Network designs have evolved from CNNs (Zioulis et al. 2018 Zhuang et al., 2022 ViTs (Shen et al., 2022, Yun al., 2023). Pipeline designs are mainly aimed miti- gate the spherical distortions inherent in panoramas. Many approaches fuse features from both the ERP panorama) and cubemap (6 perspectives) projections (Wang et al., 2020; Jiang et al. 2021 Wang et al. 2022 Li et al. 2022 Ai et al. 2023 Wang & |iuu 2024). For alternative solutions, SliceNet (Pintore et al., 202 1) and HoHoNet (Sun et al., (021) use RNNs or LSTMs along longi- tudes. SphereDepth Yan et al. 2022), Elite360D (Ai & Wang 2024), HUSH (Lee al. 20255 in- troduce spherical icosahedral meshes and spherical harmonics. While effective, these strategies still require additional modules, making them less streamlined and efficient. DA introduces SphereVit ViT to handle the spherical distortions in an end-to- andtto-end manner, without extra modules.
Zero-shot. With the rise of zero-shot perspective depth estimators, there has been trend toward developing zero-shot depth estimators for panoramas. Dephh (Rey et al. 2022) blends tan- gent perspective depths predicted by MiDaS (Ranfil et al. (020)) on icosahedral mesh but suffers from multi-view inconsistencies. PanDA (Cao et al. 2022)) leverages Möbius transformation-bart data augmentation for self-supervision. UniK3D Piccinelli al. (025a) separately predicts camera rays and distance maps, can generalize on various cameras. But their performance remains sub- optimal, due to limited panoramic data: ~00 labeled and 992K unlabeled in PanDA, ~29K in UniK3D. DepthAny
(Guo et al., 2025) projects perspective images with various horizon- tal FoVs (20" 124" <3600) into spherical space, can also generalize on various cameras. But its performance still remains constrained by the incomplete FoVs. In contrast, DA² introduces panoramic data curation engine, significantly boosting the quantity and diversity of panoramic data from available perspective data, yielding clearly enhanced zero-shot generalization performance.
3 METHODOLOGY
This section presents the methodology of 2 in detail, covering the panoramic data curation engine (Sec. 3.I and Sphere with its training loss functions (Sec. 3.2
3.1 PANORAMIC DATA CURATION ENGINE
"The quality of your data determines the ceiling of your ambitions." " (Surge Al 2020)
Due to the scarcity of high-quality panoramic data, existing panoramic depth estimators are often trained and evaluated within specific domains, greatly restricting their zero-shot generalization abil- ity and real-world applicability. Thus, the very first goal of this work is to scale up the panoramic data and build a strong data foundation for DA Motivated by this, we propose a perspective-to- panoramic data curation engine that generates high-quality panoramic data from perspective data.
As illustrated in Fig. the inputs of the panoramic data curation engine are a perspective image sized (W par Hper) and its FoVs. i.e., XFoV and YFoV. XFoV represents the coverage of this perspective image in the azimuth field Φ Φ,l and YFoV denotes the coverage in the polar angle field (,, - -Oa)- At first, P2E projection will be performed to map the perspective image onto the spherical space.
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Edis
Expor
(DINO)
w/ Eephers
SphareVVIT
Distance
Normal
Dissanes 3DPG
SDPC
Query
CrossActe
Key
Value
VIT Image Festure Z
Azr imuth
Poler
Sphare
Angle Field
Spherical Emb. Exptere
3D Paint Cloud (3DPC)
Figure 4: The architecture of SphereViT and training losses. By leveraging the spherical embedding Exphere. which is explicitly derived from the spherical coordinates of panoramas, Sphere ViT produces distortion-awann image features, yielding more accurate geometrical estimation for panoramas. The training supervision combines distance loss Ldss for globally accurate distance values and normal loss Loer for locally smooth and sharp surfaces. The effect of Lox ablated Fig. (b) and Tab.
Specifically, we start by obtaining the focal lengths from both FoVs:
W per
Hper
fz 2 x tan FoV
(1)
FoV
Lan
Then, the 3D vector d and its unit vector from the perspective camera each 2D pixel (æ, y) of the perspective image (x E [0, Wper
1]. E [0, Hper 1)) are given by:
W (y Hpp
11, d d
(2)
fm
fy
d
Then, in the spherical space, the azimuth (longitude) and polar 0 (colatitude) angles of d are: = atan2 (d,, de) + Φc:
arccos (d,,) + 0..
where (Øe: 0g) denote the spherical coordinates of the perspective image's optical center, used as offsets to obtain the absolute longitude and colatitude of each pixel. After that, the mapped pixel position (u, v) on the ERP image (i.e., panorama) sized (Wpano- Hpnno) ) is given by:
0
W panos
H panos
2x
where Φ [0, 2m), [0, T). After P2E projection, due to the limited FoV of perspective images, only a small portion of sphere can covered, as highlighted in Fig. 3 left sphere. This incompleteness leads to suboptimal performance: 1) the model lacks global context since never observes the full views of panoramic images, particularly near the poles; and spherical distortions vary significantly between the equator and poles, with severe stretching occurring at high latitudes.
Thus, following (Tencent 2025). the second step of data curation engine adopts LoRA (Hu 2022) fine-tuned FLUX model named FLUX-I2P for panoramic out-painting, generating panoramas from the "partial" panoramas. Earlier panoramic out out-painting methods (Gao al.,
et al.
"full"
2024
Feng et al.) (202)) often exhibited spatial inconsistencies, especially near the poles and the left-right seam. address this, FLUUX-12 concatenates image features with the spherical coordi- nates (azimuth & and polar f) along the channel dimension before feeding them into the Diffusion Transformer (DiT) (Peebles & Xic 2022), to improve the spatial coherence. For the GT depth asso- ciated with the perspective image, we apply only the P2E projection without panoramic out-patt aut-painting, because the absolute accuracy out-painted depth is hard to guarantee. As ablated in Tab, 3 al- the P2E projected GT depth is not performed, FLUX- 12P's panoramic out out-painting on the RGB images clearly improves the panoramic depth estimation per- formance by large margin, demonstrating its significance in panoramic data curation engine.
though the panoramic out- out-painting
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3.2 SPHEREVIT& & TRAINING LOSSES
This data curation engine creates ~543K panoramic samples, scales the total from ~~6 to ~607 (~10 times), significantly addressing the data scarcity issue that causes poor generalization. Here we focus on DA model structure and training, to effectively learn from the greatly scaled-up data.
Recently, ViT-based depth models have achieved great success (Wang et al. 2025c di Yang et al. 2024a b Piccinelli et al. 2025a), where positional embeddings (PE) are crucial for encoding spatial information. For perspectives, PE typically derived from the 2D (xx, y) pixel coordinates. How- ever, for panoramas, pixel coordinates (u, 2) correspond to spherical coordinates (longitude & and latitude 6). The spherical nature introduces -nn-uiiformiy:: high-latitude regions (near the poles) are stretched, while low-latitude regions (near the equator) are compressed. Conventional 2D PE cannot account for this spherical distortion, limiting the model's s spherical spatial understanding. To address this, many approaches fuse features from both the ERP (1 panorama) and cubemaa (6 per- spectives) projections or employ auxiliary modules, introducing inefficiencies and complexity. In contrast, DA aims to handle the distortions more simply and efficiently, without extra modules.
To this end, D proposes SphereViT, as illustrated in Fig. SphereViT leverages the spherical coordinates of panoramas efficiently and explicitly inject spherical-awarenee into the VIT image features, yielding distortion-awer aware representations and improved performance. Specifically, we first compute the azimuth and polar angles (Φ, of each pixel (u, v) in an ERP image sized (W, H):
u W
U
Φ = 2T X
A ==
x
(5)
H
Then, given the image feature E
where W P H' P and P patch size, we resize and flatten this two-channel angle field A E = x2 (Eq. 5 into A' E R x Moti- vated by the PE mechanism of ViT, sine-cosine embedding is utilized to expand A's channel from 2 to the image feature dimension D. Concretely, we first define series of coefficients (2 Jnmis where D' 2. dn, (x-1) 1og, Then, for each two-channel unit of A': Al,5 [ó,, 0,] E R D
where: i E [0, W" 11.j E 0, H' 1], we transpose and multiple it with the coefficients:
[2d Φb 2di0,
2d2 2d20,
20 db.
x 2d
2d
2d10 =
(6)
0g
Eq. 6 s result is shaped 2 D'. We then apply the sine- embedding on each unit of this matrix:
(sin(2 d:), cos 2.)) (2 f 8,), cos (2% 8,)] (sin(2"a,)), (2da
(2 (b)), c2s(24) db:]
[sin [sin di), cos d:)) [sin 8,), cos c2s(2 2d 8
(7)
Eq. 7 has a shape of 2 x D' x 2. Now, the flattened transformation of Eq. 7 with a dimension of 2 x D' x 2 D-is the unit j) of the Spherical Embedding Esphere E R (H' W'')D
As discussed in Sec. [ all panoramas share the same 360 x 180" FoV, so the spherical embedding is fixed, reusable, and docsn't 't need to be further refined. Thus, inject spherical awareness, it's only necessary to let image features "attend" to the embedding Exphere. but not vice versa. Accordingly, Sphere replaces the usual self-attention (after addition: Z + F.sphere) with cross-attention, where image features Z serve as queries and the spherical embeddings Exphere act as keys and values:
CrossAttn (Z, Esphere ) SoftMMa ZWQ (Eyphere Wk)
(Esphere Wv),
(8)
vDx
where Wo, WK Wy E Dx are learnable projection matrixs, and Z, Esphere E (H''x > This -ross-attention with spherical embedding Exphere allows the image features Z "learn" the underlying spherical structures of the panoramas, producing distortion-aware representations and leading to clearly enhanced geometrical fidelity as demonstrated in Fig. (a) and Tab. 3
Training Losses. 2s SphereViT is trained end-to-end to estimate dense, scale-invariant distance D I #x from panoramic RGB input 1 E # The supervision combines two terms: a distance loss Lais that enforces globally accurate distance values, and a normal loss Luer that promotes locally smooth, sharp geometrical surfaces, especially in regions where distance values similar but surface normals vary significantly. Concretely, let D and D be the predicted and GT

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison. For fair and comprehensive benchmark, we include both zero- shot in-domain, panoramic / perspective approaches. The best and second best performances highlighted (in zero-shot setting). In all settings (both zero-shot and in-domain), the best and second best performances are bolded and underlined. DA? outperforms all other methods no matter in zero-shot all settings, particularly showing large gains under the zero-shot setting. Median alignment (scale-invarian) is adopted by default. à: Affine-invariant alignment (scale and shift- invariant), for prior relative depth estimators: DepthAnything v1v2 (Yang et al. 2024a b), Lotus (He) et al. 2024b), and PanDA (Cao et al. 2025). We also report PanDA's results in median alignment for fairness. *: Implemented by ourselves (code will be released). The unit percentage (%)
Stanford2D3D
Matterport3D
PanoSUNCG
Rank Rank]
Categories
Method
AbsRel| RMSE,
62
AbsRell RMSE| 62
AbsRoll RMSE,
Zero-shot
OmniDepth
19.96
61.52
68.77 88.91
29.01
76.43
68.3087.90 94
11.43
37.10
87.0593.65
26.33
FCRN
18.37
57.74
72.3092.07
24.09
67.04
77.0391.74
9.79
39.73
92.2396.59
24.00
BiFuse
12.09
41.42
86.6095.80
20.48
62.59
84.5293.19
5.92
25.96 95.9098.23
16.83
EGFormer
15.28
49.74
81,8599.38
14.73
81.5893.90
15.50
SliceNet
12.49
43.70
83.7794.14
17.64
61.33
87.1694.83
14.17
SphereDepth
11.58
45.12
86.66.96.2
12.05
59.22
86.2095.19
12.42
BiFuse++
11.17
37.20
87.8396.49
87.9095.17
5.24
24.77
96.3098.35
12.17
UniFuse
11.14
36.91
87.1196.64
10.63
49.41
88.9796.23
5.28
27.04
98.25
11.25
In do-ommi
HoHoNet
10.14
38.34
90.5496.93
14.88
$1.38
97.8695.19
10.33
Elite360D
37.56
88.7296.84
11.15
85.1599
10.00
PanoFormer
11.31
35.57
88.08
9.04
44.70
88. 169..66
5.34
18.90
94.87
9,50
HRDFuse
9.35
31.06
91,4097.98
9.67
44.33
91.6296.69
6.90
27.44 92.1597.42
9.50
SphereFusion
8.99
31.94
92.5797.55
11.45
48.85
87.01
7.92
ACDNet
9.84
34.10
88.7297.04
10.10
46.29
90.0096.78
7.08
DepthAnywhere
91.0099..10
8.50
91.7097.66
OmniFussoo
9.50
34.74
89.8897.69
9.00
42.61
91.8999.99
5,00
HUSH
7.82
33.32
93.84
8.38
41.64
92.87 96.98
3.67
Lotus-D'4
45.88
48.86 86
37.67 68.39
32,39
85.86
48. 18815775 15.11578..2 78.23
37. .66
77.02
46.08 77.41
17,00
30.33
Lotus 4
45.08
47.90
38.38
31.82
84.51
49.11 78 92
38.02
76.82
46.1677.51
16.17
29.50
DepthAnything"
43.41
76.93
24.46
24.58
52.22
64.8690.39
14.58
27.42
Dopth AopthAnytinng22
36.79
43.39
47.66 76.96
25.85
70.67
58.42 19
23.90
50.74
66.86
14.25
27.25
ZoeDepth"
17.60
33.74
74.2692.86
18.43
53.46
12.1893.12
21.16
44.81
69.3494.45
11.75
22.58
Zero-shot (fusion)
3epMonoDepth
16.50
28.23
74.5692.98
20.83
79.09
65.5888.95
11.43
28.29
90.7598.12
10.83
21.67
VGGT"
18,70
33.50
74.08 83,90
10.78
38.80
88.7097.72
8.43
25.67
94.0498.19
8.42
15.08
Metric3D
12.93
20.80
84.7796.52
14.11
45.11
83.0996.59
11.42
26.95
90.4597.33
7.67
15.17
UniDepth"
15.06
20.48
70.9990.34
11.12
36.20
88.6697.94
10.40
27.29
92.5998.00
7.50
13.92
MoGe
15.81
25.76
89.0283.32
10.04
35.91
90.8098.45
8.60
25.80
93.85
6.33
12.08
UniDepthiv2"
20.46
82.12
88.7697.86
98.0698.30
Metric3Dy2*
11.59
21.78
86.07 97.36
17.78
62.55
72.3593.22
7,30
24.54
94.25
6.08
14.08
MoGev2
14.69
24.24
79.98 84,39
10.34
36.91
39.4898.22
8.26
24.67
94.1598.52
5.58
11.25
PanDA
48.44
53.06
33.9251.33
37.10
101.5
42,51
34.73
79.69
44.4971.44
17.50
30.83
Zero-shol
DepthAnyCamera
15.26
22.80
92.97
15.60
61.85
71.2795.6 2227996.62 62
12.78
27.88
97.6799.85
9.75
19.42
(cnd2end)
PanDA
16.48
23.64
73.26 85.42
8.88
33.25
92.0998. 26
6.71
21.85
42.8.25
5.33
10.33
UniKaD
11.31
19.72
95.9495.33
9.66
32.66
93.00
58
11.46
25.38
90.18 92.02
4.58
8.75
DA (Ours)
7.23
14.00
95.4598.38
6.67
28.82
95.61 98.60
5.96
19.07
96. 1298.55.55
1.00
1.67
distances. Then the surface normals can be obtained with a distance-to-normal operator D2N, giving Ñ D2N(D) and N D2N(D*) when GT normals are not directly available. Since we focuses on scale-invariant distance, Ò is median-aligned before loss computing: Amed = δ x Median(D) While training the Sphere ViT, minimize the per-pixel LI difference for both Cais and Lnori
Median(D")
Lais
Aimed
D2
Luor
Σ N₂ N PEN
[22
PER
where f2 is the set of valid pixels. For Losrs we prefer the LI norm over the commonly-used angular ) as the latter may introduce gradient collapse and destabilize training. The total loss is weighted sum: L Adhais >nfnor where > and X, are scalar weights.
discrepancy 1- (Np. N2)
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Training Datasets, 2 is trained using high-quality datasets. 6 perspective: Hypersim (Roberts et al. 2021), Virtual-KITTI 2 (Cabon et al. 2020), MVS-Synth (Huang et al. 2018). Unreal-

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Published as a conference paper at ICLR 2026
Table Ablation study on training data scaling. The results show clear, steady performance gains as the size of training data grows. Pano indicates perspective dataset converted into panoramic through our data curation engine. The average results across multiple datasets are reported (also in Tab. 33.
Please Supp's Sec.
B for more discussions about the data curation engine and the curated data.
S3D
HPS Pano
VK
VKPPno
Pano
MVSPamn
US4K Pano
3DKB
Pano
DR
Pinoo
Data Size
AbsRell
RMSFL
51 f
62
X
K
X
63,097
8.07
25.13
92.91
97.29
x
X
X
96,677
7.10
21.94
94.69
98.09
V
X
X
X
X
136,326
6,84
21.50
95.09
98.25
V
V
V
V
X
X
X
148,326
6.78
21.40
95.25 98.31
V
V
V
V
X
164,726
6.76
21.42
95.35 98.31
V
316,722
6.66
21.00
95.55
98.41
V
V
606,522
6.62
20.63
95.73 98.51
Stereo4K (Tosi et al. 2021). AD--en-Burmn (Niklaus et al. 2019). Dynamic Replica (Karaev al,) 2023), totaling 543, 425 samples; panoramic: Structured3D (Zheng al., 2020) (63,097 samples).
Evaluation Datasets & Metries. For a fair and reproducible comparison, DA evaluated on three widely-used, well-recognized benchmarks in panoramic depth estimation: Stanford2D3D-S (Ar- meni et al. 2017) (all splits), Matterport3D (Chang et al. (017) (test split), and PanoSUNCG (Wang al. 2018) (test split), using 2 error metrics (AbsRel, RMSE), and 2 accuracy metrics (d1, 02). Please see the implementation details (Sec. and metric formulations (Sec. C in our Supp.
4.2
QUANTITATIVE & QUALITATIVE COMPARISONS
Tab. presents comprehensive comparison of DA with previous SoTA approaches. Following (Wang) 2025c d). we also include prior perspective methods for more thor- ough comparison. As demonstrated in Tab. [ DA² consistently outper- forms all other methods across vari- ous settings. Particularly in the zero- shot setting, DA² shows significant gains over the second-best method by an average of 38% in AbsRel and 22% in RMSE, achieving re- markable average 82 of 95.73% and 52 of 98.51%. Notably, even a zero-shot model, DA² surpasses ear- lier in-domain methods as well, fur- ther underscoring its superior zero- shot generalization ability.
DAP (-0.1s)
Unik3D (-O.1e)
MoBer2 (-285)
Figure Qualitative comparisons. Compared with UniK3D and MoGev2, DA delivers more accurate geometric predic- tions and, as an end-to-end approach, achieves significantly higher inference efficiency than fusion-based methods.
In addition, for better access the DA performance, we also conduct qualitative comparisons with UniK3D (Piccinelli et al. 2025a the strongest prior zero-shot, end-to-end method, and Mo- Gev2 (Wang et al.] 2025d) the strongest prior zero-shot, fusion-base based method, as highlighted in Fig. 5 Thanks to data curation engine, DA² is trained with about 21 x panoramic data than Uni exhibiting clearly more accurate geometrical predictions. DA continuously yields better results over MoGev2, as its panoramic performance is restricted by the multi-view inconsistencies during fusion, e.g., irregular walls, fragmented buildings, etc. We also report the inference times: as an end-to-end method, DA achieves significantly higher efficiency than fusion-bast based approaches.
4.3 ABLATION STUDIES
Training Data. As reported Tab. D performance steadily improves as more perspective depth data converted into panoramic. thanks data curation engine. Fig, further shows rapid gains once the curated perspective data is introduced, with performance gradually converging as the data scales. Even near convergence, further improvements are still anticipated with additional data.
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Table Ablation studies on: 1) the panoramic out-painting in the data curation engine, spherical embedding Exphere in the Sphere ViT, and 3) the auxiliary normal loss Lax- The results below demon- strate that each design plays a vital role in achieving the final remarkable performance of DA
Pano. Out- But-painting Spherical Emb. Espbere
Normal Loss Lace
Data Size
AbsRel RMSE,
61
82
X
606.522
7.59
23.80
94.12
97.86
X
606,522
6.84
20.87
95.26
98.43
X
606,522
6.99
21.53
95.25
98.37
606,522
6.62
20.63
95.73
98.51
Spherical Eypers
Normal Loss
w/o Spherical Emo. Eiphere
w/o Normal Losk Foor
(6)
(b)
Figure 6: Ablation studies of DA?. (a) Removing the spherical embedding Esphere causes curved, distorted geometry. (b) Omitting the normal loss Laxr yields rougher surfaces and more artifacts.
Panoramic Out-painting. It is a crucial step in the panoramic data curation engine, generating full RGB panoramas from P2E-projected perspective images (Fig. 3.. Comparing Tab.2 row with Tab. 3 1st row, D performance can be improved only modestly via scaling up the perspective w/o panoramic out-painting, yielding a 0.48 gain AbsRel. In contrast, incorporating (w/) out- painting yields much larger boost than "w/o out tatt-panntnn" (~3 times), with a 1.45 gain in AbsRel (Tab. 2 S 1st row vs. Tab. 3 last row), clearly showing the importance of panoramic out-painting.
Spherical Embedding. We here ablate the impact of spherical embedding Exphere in the Sphere VTT. As shown in Tab. 3 (2nd last row), including Esphere noticeably boosts DA performance. Fig. (a) further illustrates that incorporating the spherical embedding produces more accurate geometric understandings on panoramas, while its absence often leads suboptimal performance (e.g., curved walls), highlighting its effectiveness in mitigating the spherical distortions.
Training Losses. We further ablate the auxiliary normal loss foor used for training the Sphere As shown in Tab. 3 (3rd vs. last row), adding Cnor boosts DA performance clearly. Also, as highlighted Fig. [6 (b), normal supervision yields flatter, smoother, and more coherent geome- try, reducing the artifacts that typically appear in ambiguous regions (e.g., corners, edges, and the upper or lower poles), where distance values may be similar but surface normals differ substantially.
5 LIMITATION & CONCLUSION
Limitation. Despite the strong performance en- abled by the large-scale training data thanks to our panoramic data curation engine and distortion-aweer awarr ViT, DA² still faces several constraints. As the training resolution (1024x512) is lower than higher-definition for- mats such as 2K or 4K, and the curated per- spective data provide only partially available GT depth in the spherical space, DA may oc- casionally miss fine details (Fig. 7 (a)) and produce visible seams along the panorama's 's left-right boundaries (which should idcally seamlessly aligned), illustrated in Fig. (b).
(b]
Figure 7: DA limitations. (a) The white lamp's predicted distance is mistakenly aligned with the desk surface. (b) Visible seams appear along the predictions at lower left-right boundarics.

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Published as a conference paper at ICLR 2026
Conclusion. We introduce DA², end-to-end, zero-shot generalizable, panoramic distance (scale- invariant) estimator that unites a panoramic data curation engine with the distortion-awand aware Sphere- VIT. Trained on over 600K samples (~543K curated from perspective and ~63K native panora- mas), DA delivers SoTT zero-shot performance, outperforming prior methods (both zero-shot and in-domain) by clear margin while remaining efficient and fully end-to-end. This work shows that scaling up panoramic data and explicitly modeling the spherical geometry enables high-quality and robust 360% x 180 geometrical estimation, paving the way for high-fidelity 3D scene applications, e.g. immersive 3D scene creation, AR/VR, robotics simulation, physical simulation, etc.
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Jia Zheng, Junfei Zhang, Jing Li, Rui Tang, Shenghua Gao, and Zihan Zhou. Structured3d: A large ahoto-realistic dataset for structured 3d modeling. In European Conference on Computer Vision, pp. 519-535. Springer, 2020.
Chuanqing Zhuang, Zhengda Lu, Yiqun Wang, Jun Xiao, and Ying Wang. Acdnet: Adaptively combined dilated convolution for monocular panorama depth estimation. In Proceedings of the AAAI conference artificial intelligence, volume 36, pp. 3653-3661, 2022.
Nikolaos Zioulis, Antonis Karakottas, Dimitrios Zarpalas, and Petros Daras. Omnidepth: Dense depth estimation for indoors spherical panoramas. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 448-465 2018.
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SUPPLEMENTARY MATERIALS OF DA DEPTH ANYTHING IN ANY DIRECTION
A APPLICATIONS OF DA²
Leveraging its remarkable capability in zero-shot generalizable panoramic depth estimation, DA effectively enables a wide range of 3D reconstruction-rlltte applications.
Figure 8: Pano3R: Panoramic Multi-view Reconstruction. Given panoramic images of different rooms from house / apartment, DA enables the reconstruction of globally aligned 3D point cloud, ensuring the spatial coherence across multiple panoramic views of different rooms.
A.1 PANO3R: PANORAMIC MULTI-VIEW RECONSTRUCTION
house / apartment typically consists of multiple distinct rooms, which may exhibit substantial geometric variations. Thanks to the strong zero-shot generalization and high geometric consistency in panoramic depth estimation, DA 2 able to reconstruct holistic 3D point cloud representation of the indoor layout, leveraging multiple panoramic images captured from different rooms. As shown in Fig. (8 the rooms can be consistently aligned via simple translation, without requiring any scaling or rotation operations. This characteristic highlights the robustness and superior geometric consis- tency of DA depth estimation, enabling seamless aligmment of shared structures such as walls and doors, facilitating applications such as VR-based indoor apartment tours and layout visualization.
Why is rotation not needed in our cases?
Of course, thanks to the robustness and high- nigh-quality of our panoramic depth estimator, scaling needed for alignment. For example, the height of the reconstructed rooms are nearly the same. And our cases, the rotations are not needed because:
1. The Z-directions of these 360 cameras are vertical to the ground.
2. The directions from the cameras the central pixel of the multi view panoramas facing the same direction the actual 3I) space where the multi view panoramas are captured. For example, panorama and have a wall and door overlapped, the door is located at the left behind A's camera, then it will also be at the right front of B's camera.
A.2 LAYERED HOME RENOVATION
As illustrated in Fig. g (a), given indoor panoramas with three distinct complexity levels-"empty",, "simple", and "full" the multiple sets of 3D point clouds reconstructed from DA panoramic dis- tance maps exhibit high consistency. They can be seamlessly aligned with fine details. As demon- strated in the zoom-in regions of Fig. (a), the fused point clouds are free of distortions: the text on the blackboard is sharp, and the wall boundaries consistently aligned.
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Empty Only
Empty + Simple
Empty + Simple + Full
(b)
Figure 9: More applications of DA (a) Layered Home Renovation. The three input panoramas correspond to different levels of foreground object complexity, denoted "empty", "simple", and "full". The zoom-in views show that the reconstructed 3D point clouds from these panoramas remain consistently aligned (primarily in backgrounds). point cloud can serve as practical 3D platform for evaluating robot manipulation performance.
(b) Robotics Simulation. The reconstructed 3D
A.3 ROBOTICS SIMULATION
Benefiting from Ds robust panoramic distance estimation, the reconstructed 3D point cloud can serve as a reliable 3D simulation environment for robot manipulation. As illustrated in Fig. (9) provides a practical 31) platform for simulating and demonstrating robotic tasks.
B PANORAMIC DATA CURATION ENGINE (MORE DETAILS)
As discussed in Sec. 4. 6 perspective datasets (Hypersim (Roberts et al. 2021 Fituul-KITTTY
(Cabon et al. (2020), MVS- WVSSSytt[Huunn et (2018). StreeoS (Tosi al. 202T), 3D-Ken- Burns (Niklaus et al. 2019). Dynamic Replica (Krrev al.) 2023)) are transformed into panoramic the proposed panoramic data curation engine. The curated datasets are summarized in Tab. 4 As shown, the sampling probabilities are normalized across datasets primarily considering data size to ensure balanced influence during D training process. Each dataset represents a domain, this balanced mixture ensures DA performance will not be over influenced by few strong datasets, achieving stable scaling behavior across datasets, and optimal cross-domain generalization. To this end, data curation engine generates ~543K high-quality panoramic image image-depth pairs from perspective data, expanding the total dataset to ~607K samples. This substantially enriches the quantity and diversity of panoramic data, constructs solid data foundation for DA and in turn significantly enhances the zero-shot performance of DA", as demonstrated in Fig. 2 and Tab. 2
On average, most perspective data samples used have an X FoV of 70 90 and the height usually equals the width. Thus, the fraction of GT depth labels in the curated panoramas is: 80/180# 80/360 9.9%. By latitude, the GT depth labels range from - -00 to 40 Moreover, though the fraction in area is only 10%. the fraction semantic much larger, because the ERP images contain much richer semantics near the equator than the poles.
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Table Perspective datasets processed by the panoramic data curation engine. For each dataset, the
vertical FoV (YFoV) is derived directly from the horizontal FoV (XFoV) as YFoV = XFoV x W where (W, H) denotes the input panorama's width and height.
H
Category
Dataset Name
Abbreviation (Tab.
Data Size In-or-outdoor
XFoV
Sam. Probability
Perspective
Hypersim
HPS
39,649
In
60'
16.59%
Perspective
Virtual-KrTTy
VK
33,580
Out
80
14.05%
Perspective
MiVt-yytth
MVS
12,000
Out
80
5,02%
Perspective
UnrealStereo4K
US4K
16,400
Various
90"
6,86%
Perspective
ID-Ken-Burns
3DKB
151,996
Various
60--90- -90
15.91%
Perspective Dynamic Replica
DR
289,800
In
85
15.16%
Panoramic
Structured3D
S3D
63,097
In
360
26.41%
C EVALUATION METRICS
Concretely, given the predicted panoramic depth ń and GT D* median alignment is performed on the predicted distance Á before computing the metries:
Median D*)
Amed
(10)
Median ())
following the evaluation protocols in prior works (Lee al., 2025 Wang Liu 2024 Yun al.
2023 Yan et al.
2025
2022;
Li et al. 2022 Shen et al. [0222] Zhuang et al. 2022 Wang et al.
2022
Sun et 2021
Pintore al. [0.1]] Jiang et al.J 2021; Wang al.) 2020 Rey et al. 2022
Piccinelli et al.
2025a
Cao et al. 2025), After that, the AbsRel and RMSE are given by:
Dmodd
D
AbsRel
RMSE
Σ PEN
(D D2)², p
(11)
[2
D
[22]
PEn
where Q is the set of valid pixels. 51 and 52 denotes the proportion of pixels satisfying
Max (Dz/ Damed Dised/ /D;) < 1.25 and < 1.252 respectively.
D DIFFERENCE AMONG: METRIC & SCALE-INVARIANT (BIASED) & ANFINE-INVARIANT (RELATIVE)
Metric and Scale-invariant Depth. In depth (or distance) estimation, metric depth Dmetric is the strictest setting, where the predicted values correspond to absolute physical distances and can be directly used reconstruct "real-scale" point cloud. Scale-invarian (or biased) depth D Diised is still strict but slightly more relaxed than metric: predictions include a global bias or shift, but not in the absolute global scale. Although the depths not metric, the underlying 3D structure is pre- served perfectly (Tab. (5), because the global bias or shift is preserved. During training & evaluation, for scale-invariant depth, median alignment occaleiivaariant is typically adopted re-scale the un- derlying 3D structure to real-world size (please see Sec. C). For metric depth, no alignment should ideally be required, but median alignment is still commonly applied because absolute scales can be ambiguous (cameras with different focal lengths can capture visually similar pictures but with substantially different absolute depths) (Hu et al. 2024 Yin al. 2023 Piccinelli al., 2025a).
2 focuses panoramic scale-invariant (or biased) distance estimation for two reasons: 1) like metric distance, scale-invariant distance also preserves the full underlying 3D geometry, and 2) D targets on the strong zero-shot generalization across diverse domains, enforcing absolute scales would introduce significant optimization challenges, as indoor and outdoor scenes differ drastically in scale, making the additional cost outweigh the benefits.
Affine-ivarrinn Depth. Affine-invariant (or relative) depth Dielauve the loosest definition, much more relaxed than either biased metric depth, preserving only the "ordering" of depths (which point is closer or farther). Since neither scale nor shift is preserved, affine-invarian depth D Delaiie cannot be used to reconstruct reasonable 3D point cloud (Tab. 5), but it's useful for tasks where
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only relative geometry matters. Affine-invariant alignment (scale and shift-invariant) is usually adopted during training & evaluation of infine-ivvariant depth estimators. Concretely, given the predicted Drelative and GT depth D*, least squares fitting is performed:
min scale, shirt
Σ
scale (Drelative,p + shift) - D 11
(12)
PES
where f2 is the set of valid pixels and the aligned predicted depth is: D = scale x (Drelative + shift).
The summarized difference is listed in Tab. [ Note that for the "Illustration with Dmeusic of scale- invariant and inveriiat depth, we only list the most widely adopted formats, passing over other scales for Doinsd and other specific transformations for Drelative like exp(-) and log(-).
Table 5: Summarized difference on depth maps among metric, scale-invariant (biased), and affine- invariant (relative). Both metric and acale-invariant depth fully preserve the 3D geometry. Due to the absence of bias or shift, affine-invariant depth is unable reconstruct an accurate 3D structure.
Depth Category
Metric Depth
Scale-invariant Depth
Affine-invariate Depth
Illustration with Dmettic
metric
Pacti
D Panic min( Duani) max Drvene mini D Derrt)
max Drente)
3D Point Cloud on:
E IMPLEMENTATION DETAILS
DA is implemented in PyTorch Paszke et al. 2019p. In ViTere the backbone of "ViT (DINO)" is initialized from DINOv2- ViT-L (Quab al. 2023) with 24 self-attention blocks, following (He et al.) 2024b Ke et al., 2024). to leverage the pre-trained visual priors. The "ViT w/ Exphere" is lightweight ViT contains only 4 cross-attention blocks. Training the Sphere takes ~5,000 optimization iterations on 32 NVIDIA H20 GPUs, with a batch size of 768. The distributed training is implemented with Accelerate (Gugger al. 2022). We set Adi = 1.0, Auoe = 2.0 for balanced loss values. Panoramas and GT depth maps are fed to VIT at a resolution of 1024x512. Please see the sampling probabilities of different data sources in Tab. 4 In the panoramic data curation engine, the FL.UX-12P is fine-tuned on FLUUX.1 [dev] (BFL. 2024), largely following Tencent (2025). The LoRA rank is set to 256 dur- ing the LoRA (Hu et al. (022) fine-tuning. The positive prompt is: clean, realistic, high- resolution, panoramic
image of a [*] scene, where [*] is either indoor outdoor. The negative prompt is: messy, low-quality, blur, noise, low-resolution
abnormal. The de, 8g are randomly selected from +30 and +15" respectively. The panoramic oai-ttnn of 543,425 perspective RGB images from various datasets is performed on 64 NVIDIA H20 GPUs and over nearly 9 days. The running time reported in Fig. is tested on NVIDIA H20 GPU resolution of 1024 x 512, excluding I/O operations.
F PRIOR SOTA METHODS FOR COMPARISONS
In-domain Baselines. 17 previous in-domain, panoramic depth estimation approaches are selected for the quantitative comparison Tab. [ HUSH (Lee et al.] 2025), AyythAnywhere (Wang &
Liu 2024), Elite360D (Ai & Wang 2024), EGFormer ( (Yun et | 2023). SphercFusion (Yan et al.)
2025). SphereDepth (Yan et al. 2022). OmniFusion (Li ct al. 2022). HRDFuse (Ai ct al. 2023). PanoFormer (Shen al. 2022). ACDNet (Zhuang al. 2002). BiFuse++ (Wang 2002 Ho-
HoNet (Sun et al.] 2021). SliceNet (Pintore et al. [022). ). UniFuse (Jiang et al. 2021). BiFuse (Wang
al. 2020), FCRN (Laina et al., 2016), and OmniDepth (Zioulis al. 20118)
Zero-shot, fusion-based baselines. 13 zero-shot, dusion-based panoramic depth estimators are se- lected implemented. is originally panoramie: 360MonoDepth (Rey et al. 2022). The other
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16 are prior SoTA perspective depth estimators: Metric3D & Metric3Dv2 Yin al. 2023 Hu
jet al., ).24), VGGT (Wang et al., 2025a), MoGe & MoGev2 (Wang et al. 2025c d). Uni & 2 (Piccinelli et al., 2024 2025b). ZoeDepth (Bhat et al. (023). DepthAnything & DeythAnythingg2 (Yang et al. 22024a]] by and Lotus-D Lotus-G (He et al. al. (024b). These methods
implemented for panoramic scenarios via multi-view splitting and fusion.
Zero-shot, end-to-end baselines. Prior zero-shot, end-to-end methods are rare, and their perfor- mance are limited by the scarcity of high-quality panoramic depth data. Only methods are com- pared: UniK3D (Piccinelli et al., 2025a), PanDA (Cao et al. 2025), and DepthAnyCarm
(Guo (relative) depth, while other methods including 2 predict at least the icale-invariant (biased) depth.
al., 2025). As evident in Tab. I PanDA predicts
G DISCUSSIONS ABOUT THE POTENTIAL DISTRIBUTION MISMATCH BETWEEN THE OUT-PAINTER AND TESTING SCENARIOS
The distribution mismatch between training and testing (i.e., applications, experiments, etc.) is critical issue that may affect the final zero-shot performance. And making the training data more diverse and comprehensive is an effective way to mitigate the distribution mismatch. Thus, we comprehensively select multiple high-quality perspective datasets in different domains (i.e., captured in different scenes with different styles, etc.) as the sources of our panoramic data curation engine. Some examples of the perspective data sources and the curated data are shown in Fig. [1 Also, the training data of the out out-painter is also diversely combined from commercially purchased data and self rendered data, in order to fit the diverse domains of multiple perspective data sources. Some examples of the out-painter's training data are shown in Fig. 10
clean, rmalixtis, high-quality, high-resolutton, pancrante Image
clean, realivtis, high-quality, Migh-resolution, panorante Image
clean, realixtis, high-quality, high-resalation, pancrante tmage
clean, realistis, high-quality, Migh-resolution, panorande Image
clean, realistic, high-quality. res-reeslutton, panorande Image
clean, realistic, high-quality, high resolution, panorards Image
"a clean, realistic, High-quality, high- resolution, panorande Irage
clean, realistic, high-quality, high- resolution, panorande trage
Figure 10: Training data examples of our panoramic our-painter.
H DISCUSSIONS ABOUT REAL-WORLD PERFORMANCE AS THE TRAINING DATA ARE MOSTLY SYNTHETIC
Real-world performance surely an important evaluation asppct. We basically use synthetic training data because synthetic data typically exhibit higher quality and sharper edges compared to real- world data, the quality of which may be affected by the many potential limitations of the hardware sensors, i.e., the accuracy issue, resolution issue, and for panoramas, many real-world captures have empty depth values near the poles (please see Fig. 15). Also, it's worthwhile to note that in the adopted three well-recognized benchmarks in panorama depth estimation (please see Tab. T.. two of them (Stanford2D3D (Armeni et al. 2017). Matterport3D (Chang et al. 2017)) are woaldworld captured, which demonstrate superior zero-shot generalization ability in world scenarios. Additionally, we believe making the training data more diverse and comprehensive is an effective way to mitigate the distribution mismatch, please Sec. G for more details.
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The original out-gainted panorama
The left 50%
Figure 11: The curated data examples, which come from different resources with diverse domains. Note that the left 50% of the original panorama is appended on the right, in order to illustrate the horizontal circular consistency.
I DISCUSSIONS ABOUT IN-COMPLETE DEPTH SUPERVISION
The performance of DA would surely be better if the depth label can also be accurately out-painted, and the missing depth label may decrease the performance especially near the poles. Therefore, we also incorporated a high-quality and native panoramic depth dataset (i.e., Structured3D) with slightly higher sampling probability than each panoramic transformed perspective dataset shown in Tab, ii Supp's 's Sec. [B. And while training on this dataset, large errors and subsequently, large gradients near poles can be consistently observed, as illustrated in Fig. 12 which forces the model to predict good distances on these areas.
Input RGB
Error Map
Input RGB
Error Map
Figure 12: Training supervision visualization (i.e., dense loss maps). Large (light color) be observed particular near the poles, forcing the model to predict good distances on these areas.
order to better understand missing annotations at borders (where the depth labels are missing) and centers (where the depth labels are available), we conducted experiments using the native panoramic dataset, Structured3D (Zheng et al. 2020):
Using the entire dataset, with all depth labels available, noted as "AIl".
2. Using the entire dataset, with all depth labels available in the first half and only "centers" depth labels are available in the second half, noted as "Half-center".
3. Using the entire dataset, with all depth labels available in the first half and only "border" depth labels available the second half, noted as "Half-horder"
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4. Using only the first half of the dataset, with all depth labels available, noted "Half".
We originally expect that "All" is the best and "Half" is the worst due to the difference in scale. However, surprisingly, these 4 groups experiments have basically the same results as the second row of Tab. (only Structured3DD, or S3D, is v, which corresponds to "All"). This phenomenon mo- tivates us more seriously consider the importance of the "diversity" of "domains" in our training data. When the "quantity" is not the "the shortest plank in the barrel", the importance of "diversity" higher than our expectation. We thus re-analyze the scaling-law curves illustrated in Fig. 2 and Tab. At the beginning, when the number of datasets used is small and the existing diversity is lim- ited, performance grows very rapidly as additional datasets are introduced, since each new dataset contributes significantly the diversity (e.g., different scene semantics, layout, lighting conditions, etc.). As illustrated, the introduction of Hypersim (Roberts et al. 2021 yields an improvement of 0.97 in AbsRel with only ~33K's improvement in quantity, the ratio is 0.03 AbsRel( % /K. The in- troduction of VKITTI (Cabon et al. (2200) further brings clear improvement of 0.26 in AbsRel with 440K's s improvement quantity, but the ratio is only 0.007 AbsRel(% VK. As more datasets curated from different sources are introduced, the performance gained from both the diversity and quantity gradually becomes marginal. When the Dynamic Replica (Karaev et 2023) was introduced, the gain in AbsRel is only 0.04, though it brings an improvement of ~~9K in quantity.
J THE VISUAL QUALITY OF OUT-PAINTED REGIONS
Perspective RGB
Out-painting Mask
Perspective RGB
Out-painting Mask
The original out painted panorama
The left 50%
MVDitfusion
MVDiflusion
Diftusion600
Diffusion500
FLUX-12P (Ours)
FLUX-12P (Ours)
Figure 13: Qualitative comparisons of FLUX-I2P with SoTA baselines. Following Fig. 11 50% is appended on the right of the figure show the circular consistency.
the left
As essential part of our panoramic data curation engine, the visual quality of the panoramic out-painter, i.e., the FLUXXI2P better to evaluated, which can help understanding the qual- ity of our curated data. Currently, there exist two well-recognized panoramic out-painters, Diffu- sion360 (Feng et al. 2023) and MVDiffusion (Tang et al. 2023).
MVDiffusion is capable of generating 7 more perspective images based on given input perspective image. Combined with the input, these 8 images (each has a horizontal FoV of 90%) constitute a "partial" panorama, with each image separated by horizontal gap of 45%. However, MVDiffusion assumes the input perspective image has horizontal FoV 90" and can not generate the upper and lower poles of the panorama, which is different from requirement.
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Diffusion360's setting aligns with closer. The inputs of both Diffusion360 and FLUX-I2P a perspective image and its horizontal FoV (which corresponds to the out- out-painting mask as shown in Fig. E And the output is the out out-painted image. In our setting, the input perspective image wewpertiie---euuureetaannnuee
will firstly be quently out-painted. During the out- out-painting, the P2E projected images, which perfectly correspond to the out-painting masks, are completely preserved. However, in Diffusion360's setting, the input perspective image may not be strictly preserved, as evident in Fig. 13
(P2E) projected into the spherical space, and be conse-
The qualitative comparisons of FLUX-I2P with Diffusion360 and MVDiffusion is shown in Fig. 13 As illustrated, FLUX-I2P able to generate panoramas with superior visual quality, high realism, and strong semantic coherence with the input image. While Diffusion360 and MVDiffusion may exhibit clear inconsistencies between their out-painted content and the input image (e.g., Diffu- sion360's ' result on the left case, MVDiffusion' result on the right case), exhibit oversaturated style (e.g., Diffusion360's result on the right case).
In order to quantitatively evaluate our FLUX-I2P with SoTA baselines, we conduct a user study across a wide range of evaluation aspects, as shown in Tab. and Fig. [4] This because there rarely exists well-recognized benchmark for panoramic image out-painting, where we can cal- culate metrics like SSIM, LPIPS, PSNR; and it's not straightforward to evaluate the "generation ability" using reconstruction metries. Also, considering the in-complete panoramic out-painting of MVDiffusion, which largely differs from the settings FL.UX-12P and Diffusion360, we kindly re- move MVDiffusion for the comparison. Four metrics are considered in this quantitative evaluation:
1. The overall visual quality of the panorama (an ERP image) (VQ-ERP).
2. The semantic consistency of the out-painted content and the input image (IN-OUT-CON).
3. The circular seams (SEAM).
4. The visual quality of the panorama under immersive perspective projection (VQ-PER).
Table 6: Winning rate of FLUX-I2P over Diffusion360 measured on 30 cases by human preferences (the higher, the better). A total of 5 people participate (no authors) and 150 comparisons are made. FLUX-12P consistently out-performs Diffusion360 in the above evaluation perspectives. Please see
Fig. 14 for the interface of the user study of one example case.
Competition
VQ-ERP
IN-OUT-CON
SEAM
VQ-PER
FLUX-I2P (Ours) vs. Diffusion360
72.7%
92.7%
55.3%
79.3%
K DISCUSSIONS ABOUT SITEREVIT: PANOFORMER AND SPHEREDEPTH
PanoFormer (Shen et al. 2022 proposed a STLM to initialize the related token positions (replacing conventional ViT positional embedding). using the spatial transformations among ERP domain, spherical domain, and tangent domain. Then, PanoFormer proposed a token flow to "learn" bias of STLM, to refine based on image features. SphereDepth (Yan et al.) (022) leverages ISM to represent the spherical structure and use interpolation to transfer the ERP image pixels into (and back from) each sample point on the ISM (controlled by both MR and TR).
Both Sphere and PanoFormer propose a "sphere-aware" embedding to replace the conventional one. But compared with PanoFormer, SiT directly and explicitly inject the entire 360 180 spherical structure into the network, while PanoFormer's STLM focuses on the relative positions within a tangent projection patch, which is like "fine-grained" version of cubemaps.
Both SphhreDepth and Sphere VIT directly and explicitly inject spherical structure into the network. SppereDepth does this even more explicitly as it directly designs sphere network blocks and both the input and prediction are spherical representations sampled on ISM. However, as stated in their original paper: "representing a high-resolution panorama image needs a spherical mesh with high resolution (i.e., high MR) and more triangles.", and "The topological complexity of the spherical mesh exponentially grows with the number of triangles (i.e. MR) increasing, which degrades the computing efficiency and increases the memory footprint.". Also, interpolation is needed to project
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User Study of the Out- nut-painting's is Visual Quality
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Figure 14: The user study interface of one example case, designed for Tab.
dense pixels into and back from sampled points on ISM. Thus, compared to SphereDepth, Sphere ViT more streamlined and may have higher efficiency.
L DISCUSSIONS ABOUT SPHEREVIT: SE
Also, in order to mitigate the spherical distortions of panoramas, Se (Li et al. 2023) utilizes HEALPix (Gorski et al. 2005) as the spherical sampling space. And similar to Spherelepth, Net also uses interpolation to map ERP features onto and back from Sphere, formularized as f and fs- Net also utilizes cross attention fuse the spherical features from different fine-grained levels. Compared to Nett SpT may exhibit higher efficiency because:
1. Net fuse features from 4 coarse-to-fin spherical levels via cross-attentions, which may increase the computation burden.
2. Though the HEALPix structure used by Se is already optimized for efficiency, it still much slower than rectangular image features, because its pixels are curved sphere instead of a flat plane, and their arrangement is irregular.
3. Feature projections performed during inference, making it less streamlined.
M DISCUSSIONS ABOUT SPHEREVIT: HUSH
M.1 THE SPHERICAL EMBEDDING AND CROSS-ATTENTION
HUSH (Lee al. 2025) uses spherical harmonic (SH) basis functions as spherical priors aiming for better feature learning spherical structures. The core modules are: SH-based hierarchical attention module, and SH basis index module. In the first module, SH basis functions serve geometric priors guide hierarchical cross-attention between spherical geometry and image features. The second module selects important indexes of SH bases based on the similarity of the task specific feature fr and embedded SH basis B,, to adaptively emphasize relevant SH bases.
Both HUSH and SphereVit ViT use Pross-attention, but the ways how they are using them are different. uses spherical embeddings Esphere keys and values, and the image feature as queries.
Sphere ViT ViT
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While HUSH, in its SH-based hierarchical attention module, initially uses SH feature query and image feature f1 as key and value. Then, HUSH sequentially and alternatielyy uses image feature (e.g., f2, fa) SH feature query, the output of last cross-attention acts key and value. HUSH only uses the initial image feature fi as the key and value, and SH feature as query.
The underlying reason for such differences in injecting spherical awareness is that the structural prior in HUSH (SH coefficients) is learnable but in SphereViT the prior is fixed. SH bases are base functions defined on spherical space. They don't directly represent the spherical structure itself, but are more likely a defined feature space which should also be refined (or learned) with the image features. HUSH also selects different SH bases to accommodate different tasks. While in Sphere ViT, the spherical embeddings Esphere directly represent the spherical structure, and thus can be fixed as a global context and be shared across different panoramas. This is similar to the way diffusion models (Rombach et al. 2022) inject users' prompts as global context during image generation.
M.2 THE IMPROVEMENT FROM SPHERICAL EMBEDDING
The baseline VIT model does use standard 2D sine-cosine positional embeddings based on the rect- angular pixel coordinates (u, v), consistent with conventional ViT'. In contrast, our Sphere ViT proposes "sphere-aware" awwrre"" spherical embeddings (ó. 0) derived from the true azimuth and polar an- gles of each pixel. This change explicitly encodes the non-uniform sampling of the ERP images and the spherical structure of the 360 domain; these are key properties that (u, v) embeddings cannot model. While the overall AbsRel gain may appear moderate, we emphasize that this because:
The introduction of spherical embeddings brings reduced curvature distortion (e.g., more straight walls in Fig- (a)) and improved geometric stability primarily near the poles where the spherical distortions are significant, which may not be fully captured by globally aver- aged metrics such as AbsRel or RMSE.
2. The panoramas in real-world benchmarks are usually in-complete, missing both the RGB and depth labels near the poles. Some examples are shown Fig. 15
RGB
Depth
Stanford2D3D
RGB
Depth
Matterport3D
Figure 15:
Some RGB and depth examples from the real-world captured benchmarks. Notably, the upper and lower regions of the panoramas are missing, perhaps due to the limitations of hardwares.
M.3 THE NORMAL LOSSES
HUSH states in its abstract: "Finally, by combining the scene features with task-relevant features in the mask-specific heads, we perform various scene understanding tasks, including depth, surface normal and room layout estimation.". While focus zero-shot panoramic depth estimation,
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because depth estimation can be considered feed-forward 3D point cloud reconstruction, which enables a wide range of applications compared to only the 2D visual content.
Both HUSH and SphereViT normal loss, but the motivations are different. HUSH "performs various scene understanding tasks" (i.e., depth, normal, layout) and its predictions of different tasks come from different prediction heads and different SH bases. Thus, the normal loss (also the depth loss, gradient loss) are the main losses that should be included during training because they directly correspond to the claimed tasks. While Sphere focuses on depth only, and we consider depth as an important foundational 3D task. The motivation why we use normal loss is aiming to mitigate the artifacts of the reconstructed 3D point cloud (please see Fig. (6) especially near the poles. These artifacted areas generally share similar depth values with GT, yet their normal values differ from GT markedly. Thus, the normal loss of Sphere ViT' an important auxiliary loss to improve the quality of the predicted depth, and the reconstructed 3D point cloud.
N MORE ABLATIONS ABOUT SPHEREVIT'S ARCHITECTURAL DESIGN
In cross-attention layer, the computation is defined as:
QWQ (KWk) (VWv), VDz
CrossAttn (Q, K, V) = SoftMaxx
(13)
Firstly, the output has the same spatial and temporal dimension as query (Q). that is, the attention's output corresponds strictly with query, where each token incorporates information from key (K) and value (V). This indicates K and V provides "reference information sources" and Q is the "target feature" being updated. Secondly, considering the information flow, the Q determines "where to look", its features generate attention weights with K that decide which parts of V are relevant. The attended information is then aggregated back to Q's positions. Consequently, the data path updates Q while K and V act conditioning sources that guide the update. Then, during backpropagation the dominant gradient flows toward Q's parameters, since the loss is computed on the attention's 's output that shares Q's shape. The model thus learns to adjust Q's representations that they can better extract and integrate information from K.V.
Thus, the defines the representation to be enhanced, while the K. V provide context prior knowledge. This principle underlies SphereViT's design, where image features (as Q) are refined through attention the spherical embedding (as K, V), enabling the model absorb geometric priors without modifying the fixed spherical coordinates themselves. Similar design has been well recognized in Stable Diffusion (Rombach et al. 2022). where cross-attention is used to inject users' prompts as global context. During image generation, the image latent features (as Q) are gradually refined through attention to the "fixed" prompt embedding (as K, V), enabling the model generate images that accurately respond to users' preferences.
Table 7: Ablation studies about Sphere architectural design. The best and second best per- formances are bolded and underlined. Following Tab. 6 and the averaged results across multi benchmark datasets reported. Unit is percentage (%)
Operations for ablation
AbsRel. RMSE| 81 1 82
Swap and K V in cross-attention
(Model doesn't 't converge)
Replace cross-attenionn by concatenation and self-attention Replace cross-attention with self-attention
7.42
23.04 94.31 97.98
7.29
22.45
94.60 98.13
Nothing changed
6.62
20.63
95.73 98.51
N.1 CROSS- VS. SELF-ATTENTION
When the cross-attention module is replaced by self-attention after the addition of the spherical em- bedding and image features, the model performance also drops notably, as shown in Tab. 7 This is because self-attention treats the spherical embedding "part of the feature channels, without ex- plicitly establishing any directional interaction between the image features and the geometric prior.
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Consequently, the model fails to effectively incorporate spherical structural information, leading to weaker distortion awareness and reduced overall accuracy.
N.2 CROSS-ATTENTION VS, CONCATENATION AND SELF-ATTENTION
When replacing the cross-attention module with simple concatenation followed by self-attention, the spherical embedding is merely treated as additional feature channels, without explicitly estab- lishing any directional interaction between the image features and the geometric prior. Similar to Sec. N.1 this operation finally degrades the model's performance, as shown in Tab.
N.3 SWAPPING QUERY AND KEY VALUE IN PROSS-ATTENTION
This modification fundamentally changes the direction of "attention flow". As analyzed earlier, in a cross-attention layer, the tokens in Q are the ones being refined, while the K / V tokens act as reference sources. When the roles are swapped, the spherical embedding becomes tion tries to update the spherical geometry considering the image features. This disrupts semantic alignment between image features and spherical geometry, preventing the model from absorbing the spherical priors. As result, the model fails converge and produces severely degraded perfor- mance, confirming that the original attending direction essential for effective spherical condition- ing.
Q, the atten-
N.4 MORE ABLATIONS TO SHOW THE COMPREHENSIVE EFFECT OF ESPHERE AND LNOR
Table 8: More ablation studies. We here report the quantitative results w/ pano. out-painting, w/o spherical emb. Esphere, w/o normal loss Leors to better understand the comprehensive effect of Esphere and Lnor. The last three rows are directly borrowed from Tab,
Pano. Out-painting Spherical Emb.
Exnar
Normal Loss Live
AbsRel
RMSE,
61 1
82 t
7.27
22.48
94.77
18.19
X
V
6.84
20.87
95.26
98.43
X
6.99
21.53
95.25
98.37
6.62
20.63
95.73
98.51
When both the spherical embedding Exphere and the normal loss Lnx are removed, shown in Tab. [ performance drops significantly. Without Esphere, the model's awareness of spherical geom- etry will decrease, leading to larger distortions; and without Case, local surface smoothness will be less preserved. The results in Tab. further confirm that both components are essential for achieving distortion-awen aware and high geometrical fidelity predictions.
O METRIC DEPTH VERSION OF DA
Table 9: Quantitative comparisons on metric depth estimation, compared with UniK3D.
Stanford2D3D
Matterport31
PanoSUNCG
Method
AbsRelj RMSE,
1 82 f AbsRell, RMSE, 1 f AbsRel] RMSE, 61
82
UniK3D
17.95
48.50
78.23
86.63
22.24
66.80
66.34 92.81
12.60
27.20
88.90
98.47
16.56
45.73
80.04 90.32
17.53
60.85
68.21 96.13
11.23
21.03
89.20
98.53
We sincerely agree that metric depth estimation can expand the downstream applications. The cur- rent version of this work only supports "scale-invarias or "biased" because the authors consider the "metric-scale" little ill-posed because sometimes the metric-scale can't be reflected in the panora- mas. For example, if the entire scene is scaled proporolopor and linearly, the 360 pictures captured will still remain the same. However, these situations are rare. Thus, we also train a metric version of DA? by directly supervising the model with metric GT depth, to explore its metric depth performance. As illustrated in Tab. [9] thanks to the data curation engine and the Sphere ViT which is "sphere-aware" 2 consistently achieves higher performance compared with UniK3D.
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P DISCUSSIONS ABOUT THE LEFT-RIGHT SEAMS AND POTENTIAL SOLUTIONS
We sincerely acknowledge that our model may exhibit left-right seams in the predicted distance maps and the reconstructed 3D. The seam artifacts mainly arise from two factors:
1. The input ERP images introduce artificial left-right boundaries. Commonly-used neural network architectures can not easily enforce this horizontal consistency. Because there is a lack of adjacent correlation between the left-right boundaries which should be adjacent but are separated in ERP images.
2. The out-painted RGB may include slight color or structure discontinuities at the left-right boundaries, which may also influence the horizontal consistency of Sphere ViT.
For potential solutions, performing horizontal circular rotating augmentation may be an effective way to enforce the left-right boundary consistency. Specifically, for if the panoramas the training data have perfectly aligned left-right boundaries and the depth maps are complete and also perfectly aligned at left-right boundaries. Then, during training, we can augment each data sample into mul- horizontally rotate the
tiple ones (1 - N), and for each augmented sample (the 7th sample), panorama and the depth map with 360 & N. We believe adding such a horizontal circular rotating augmentation will mitigate the left-right seams better than adding simple smoothing interpolation near the left-right seams on the predicted depth maps, which we have tried but it didn't work well.
Q MORE QUALITATIVE RESULTS COMPARED WITH UNIK3D AND MOGEV2
We here provided more qualitative results compared with SoTA baselines: UniK3D (Piccinelli et al. (025a) and MoGev2 (Wang et al. 2025d). Consistent with Fig. [ compared with UniK3D and MoGev2, DA delivers more accurate geometric predictions. MoGie22 divides the panorama into several perspective patches, and performs perspective depth estimation on each patch, then these perspective depth patches are fused into a panorama. It takes much longer time during inference and its quality is limited by the geometrical consistency of the estimated depth at each patch. Such inconsistencies usually take place near the poles in our practice, perhaps due to the RGB inputs of MoGev2's training data rarely "look up" or "down". As illustrated in Fig. [6] MoGev2 may exhibit irregularly deformed or distorted 3D geometry.
UniK3D aims to achieve robust generalization across different cameras through training on large- scale datasets. However, due to the limited amount of panoramic depth data in its training data (only ~29K), UniK3D's performance on panoramic images, particularly near the poles where spherical distortion becomes significant, remains clearly constrained. Its performance near the equator is rel- atively strong, benefiting from the smaller domain gap to conventional perspective images. Also, in its official demos (the equirect angular ipg and venice. jpq under this folder). the top and bottom 100% of the panoramas are cropped to ensure reliable results. As shown in Fig. [6 UniK3D exhibits noticeably inferior performance near the poles, producing clearly distorted geom- etry in regions such as ceilings, floors, and the upper and lower regions of side walls.
We here primarily show the qualitative comparisons on indoor scenes because indoor scenes can more clearly demonstrate our superior geometrical fidelity. Due to the much larger space represented in outdoor panoramas, the lower regions of panoramas usually represent very small area of ground, which only covers a tiny portion in the whole 3D scene. And the upper regions are usually part of sky. As discussed above, the inconsistencies between patches in MoGev2 usually happen near panoramic images also typically fail short near the poles. Thus, outdoor scenes may make more difficult in very clearly demonstrating the superiority of D geometrical estimation, compared with indoor scenes.
the poles, and the UniK3D's performance
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Input RGB
MoGev2
UniK3D
D (Ours)
Figure 16: More qualitative results compared with UniK3D and MoGev2. Consistent with Fig.
DA 2 still delivers more accurate geometric predictions. Please zoom-in for more details.
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Reviews:
[{"review_id": "Du7tY5zbOs", "reviewer": "Reviewer_t7tq", "summary": "DA2 trains an end-to-end panoramic depth (scale-invariant distance) model by first,  curating a large dataset containing panoramas from perspective RGB-D via P2E projection plus generative panoramic out-painting (FLUX-I2P), and develop a sphereViT backbone that cross-attends from image features to a fixed spherical angle embedding. On benchmarks, DA2 reports the best zero-shot results, beating strong zero-shot baselines like UniK3D.  Authors highlight better or comparable speed to end-to-end competitors while much faster than fusion pipelines.", "questions": "1. What fraction of pixels per training panorama actually have GT depth supervision, on average and by latitude?\n\n2. Can we consider add a simple scale-head (or post-hoc scale regressor) and evaluating metric errors where ground truth exists. Compare to UniK3D on the same splits.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Strong empirical gains. The proposed method demonstrates strong performance compared to both zero-shot fusion, zero-shot end-to-end and in-domain methods.\n2. The proposed method scales well. The paper shows clear scaling-law-like improvements as more perspective data are \"panoramaized\"\n3.  The proposed architecture is effective. The SphereVIT's fixed spherical embedding plus cross attention is a simple but effective way to inject spherical awareness, which shows strong results.", "weaknesses": "1. It seems there is a supervision mismatch introduced by the generative out-painting. The curation engine out paints RGB to a full panorama but supervised only the P2E covered part of depth (no out-painted depth due to acc concern). Hence, many pixels in the training panoramas lack ground-truth depth, yet their RGB content is model-generated. This opens risks of learned correlation with out-painter priors and biased geometry in unsupervised regions (especailly near poles and seams).  The paper shows gains with out-painting but it doesn't quantify how much of the test-time improvement stems from distribution mathcin got FLUX-I2P artifacts compared with real geometric learning. \n\n2. Injecting coordinates/angles and using attention to a fixed positional bank is not conceptually new. [1] mitigates ERP distortion via spherical tangent tokens and transformer design. [2] operates directly on spherical meshes, and several recent spherical ViTs or positional-encoding approaches aim at similar goals. \n\n3. Metric depth is not addressed when comparing to Unik3D. DA2 predicts scale-invariant distance, not metric depth. With Unik3D showing metric 3d across cameras (including panoramic), it is important to discuss scale recovery or to provide an optional scale head, since it is important for downstream applications like AR or robotics.\n\n\n[1] Shen, Zhijie, et al. \"PanoFormer: panorama transformer for indoor 360∘ depth estimation.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022.\n[2] Yan, Qingsong, et al. \"Spheredepth: Panorama depth estimation from spherical domain.\" 2022 International Conference on 3D Vision (3DV). IEEE, 2022.", "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": "LPga340ChT", "reviewer": "Reviewer_GN9K", "summary": "This paper aims to build a strong zero-shot depth estimation model for panoramic images. The main contributions are:\n\n- A data curation pipeline that maps perspective images into spherical coordinates followed by image out-painting, substantially expanding the training data scale.\n- A customized transformer with angle-based positional embeddings and a cross-attention design.\n- A comprehensive evaluation of zero-shot and in-domain depth estimation across multiple benchmarks.", "questions": "1. Center vs. border performance: Can you provide ablations comparing performance in central regions versus border regions on panoramic images, to clarify benefits and limitations of the proposed dataset (related to Weakness 1-1)?\n2. Out-painting quality: Can you include visual quality comparisons (e.g., SSIM, LPIPS, PSNR) between the out-painted images and corresponding ground-truth images (Weakness 1-2)?\n3. Attention ablations: Please provide ablations for self-attention as well as query/key-value choices in cross-attention (Weakness 2).\n4. Ablation dataset: Which evaluation dataset is used for the ablation results? The reported scores do not seem to align with any dataset in Table 2.\n\n---\n\nReference  \n[1] HUSH: Holistic Panoramic 3D Scene Understanding using Spherical Harmonics, CVPR 2025.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "1. The paper clearly identifies limitations in current panoramic depth estimation methods and significantly expands the data scale, which is crucial for the field’s progress.\n2. Benefiting from the enlarged dataset, the model outperforms existing methods on standard benchmarks.\n3. It provides a comprehensive benchmark, including perspective-based models, which clarifies the current landscape.\n4. The paper is clearly written and contains rich technical details.", "weaknesses": "1. Limited ablations on the dataset curation process.\n   - Because ground-truth depth is only available in the central regions of the curated data (due to the limited FOV of perspective images; Fig. 3), it is unclear how missing annotations at the borders affect final performance.\n   - The model uses image out-painting to compensate for limited FOV, but the visual quality of the out-painted regions is not clearly evaluated.\n\n2. Limited novelty in some architectural components.  \n   The paper emphasizes angle-based positional embeddings (L269–299), cross-attention (L300–311), and a normal loss (Table 3, listed as a separate ablation item), but:\n   - Positional embeddings: Improvements appear modest. In Table 3, removing $E_{\\text{sphere}}$ yields only a small degradation (AbsRel 6.62 → 6.84) compared to gains from out-painting and the normal loss. It is also unclear whether the baseline here uses standard \\(uv\\) positional embeddings or no PE. The utility of the proposed PE seems smaller than claimed.\n   - Cross-attention: The idea is not new and is under-ablated. Prior work (HUSH [1]) uses cross-attention to attend image features and SH features. Here, the method uses \\(uv\\) PE instead of SH and fixes image+feature as queries with PE as keys/values. Ablations should compare:  \n     (a) Cross-attention vs. self-attention (with image+PE as inputs, matching L303), and  \n     (b) Swapping queries/keys (PE vs. image features), following HUSH.  \n   - Normal loss: It contributes notably (Table 3), but normal losses have been used previously (e.g., [1]).", "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": "LbecnggYhn", "reviewer": "Reviewer_c7zZ", "summary": "This paper aims to create a universal, end-to-end depth estimation model that generalizes across domains and any directions for panoramic application.\nMain contribution:\n1. provide a synthetic panoramic data engine/pipeline mainly powered by utilizing FLUX-I2P out painting. And evaluated interest scaling law on that curated data.\n2. Design a SphereViT which incorporates spherical embedding, can yield accurate geometrical estimation by distortion-aware image features.\n3. Evaluation on zero-shot/in-domain benchmark shows strong quality.", "questions": "1. What if we collected more real panoramic data (not limited in some domains) and use them to evaluate the scaling law? It should also have improvement for depth estimation task.\n2. Can we also use video-generation model to generate some temporal panoramic data for temporal consistency? Single frame is easier to handle.\n3. For the ablation study, spherical embedding didn’t show too much improvement (less than 1 point), is there any other better design for the specific utilization of spherical information?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1. Good work which represents a step toward universal depth perception across all directions.\n\n2. It combines synthetic panoramic data and sphere-aware architecture could inspire like Geometry-consistent generative modeling.\n\n3. Solid results on comprehensive panoramic benchmarks show good zero-shot performance, outperform in-domain models proved good generalizability.", "weaknesses": "1. The large-scale training set is created by FLUX I2P. This work tries to find a good way to utilize the strong FLUX base model capacity. However, the generation pipeline might have distribution gaps like lighting environment & geometry consistency may still have some gaps from real panoramic scenes.\n2. Efficiency: this paper’s E2E pipeline is faster than fusion based method but remains at same level with other E2E methods like UniK3D. Both are 0.3s. The E2E pipeline seems not novel.\n3. The data and ViT arch design are mainly focused on depth estimation quality improvement. Other methods like PanDa, DepthAnyCamera or UniK3D could also cover the tasks.", "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": "Tprrmi0MgY", "reviewer": "Reviewer_xeLR", "summary": "This paper introduces DA2, a method for zero-shot, monocular panoramic depth estimation. The work tackles two fundamental challenges in this domain: the scarcity of large-scale panoramic training data and the geometric distortions inherent in the equirectangular projection.\n\nTo address data scarcity, the paper proposes a panoramic data curation engine. This engine leverages abundant perspective RGB-D datasets by first projecting a perspective image into a partial panorama and then using a image generative model (i.e. FLUX-I2P) to out-paint the partial panorama into a full one. This process increased their training set size by nearly tenfold, from ~63K to ~607K pairs.\n\nTo handle spherical distortions, the paper proposes SphereViT, a ViT-based architecture. Instead of using standard 2D positional embeddings, SphereViT creates a fixed Spherical Embedding derived from the spherical coordinates (azimuth and polar angles) of each image patch. It then uses cross-attention, with the image features as queries and the spherical embedding as keys and values, to explicitly inject distortion-awareness into the feature representations.\n\nThrough extensive experiments on standard benchmarks (Stanford2D3D, Matterport3D, PanoSUNCG), DA2 is shown to achieve state-of-the-art performance, significantly outperforming prior zero-shot methods and many specialized in-domain models with high inference efficiency.", "questions": "Thank you for this good work. Besides the points raised in the discussion of weakness, I have a few further questions to better understand the nuances of your contributions.\n\n1) Current image generative methods are capable of out-painting depth information. If the perspective depth maps are also out-painted, would this lead to performance improvements?\n\n2) As mentioned in the paper, the depth estimation model and data will be open-sourced. Will the FLUX-I2P data and model also be open-sourced? The paper does not provide sufficient details about the data used to train FLUX-I2P.\n\n3) Please discuss the relevance to the method proposed in \"S2Net: Accurate Panorama Depth Estimation on Spherical Surface\". If relevant, please cite this work.\n\n4) In Table 3, what would the results be if only Pano. out-painting is used without SphereViT and Normal loss?\n\n5) In Section A.1, the authors mention that only translation is needed to align the 3D points from panoramas taken at different locations. Please explain why rotation is not needed?\n\n6) Please proofread the manuscript again to identify and correct spelling errors—for example, \"date\" in Line 380.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper's primary strength is its two-fold innovation.\n\n1) The data curation engine creatively uses a generative model (i.e. FLUX-I2P) to synthesize panoramic training data from abundant perspective data, the authors have effectively broken through the data barrier in the academic community that restricts a open source panoramic depth estimation comparable to commercial models. The resulting \"scaling law\" (Fig. 2) is a powerful demonstration of the data-centric approach.\n\n2) The SphereViT architecture introduces a neat and elegant solution to the problem of spherical distortion. Its use of cross-attention with a fixed spherical embedding is a more principled and efficient approach than the fusion strategies common in prior work.\n\nThe resulting model, DA2, is not only accurate but also efficient. As a fully end-to-end method, it is significantly faster than fusion-based alternatives, making it practical for real-world applications, as mentioned in the appendix. The planned release of the code and the large-scale curated panoramic dataset is a big contribution in itself and will be a substantial asset to the research community.\n\nThe paper is easy to follow. The experimental validation is thorough, featuring a comprehensive benchmark against a large number of competing methods.", "weaknesses": "The paper is novel and effective, the following points are intended as constructive suggestions for further improvement.\n\n1) About Out-painter\n\nThe quality of the curated data, and thus the performance of DA2, is intrinsically linked to the performance of the FLUX-I2P out-painter. Any biases or artifacts of the generative model could be implicitly learned by DA2. A brief discussion on the limitations of the out-painter, and how different out-painters affect depth estimation would add valuable context.\n\n2) About SphereViT\n\nThe cross-attention mechanism in SphereViT is a key architectural novelty. The ablation study shows that including the spherical embedding is crucial. However, it does not compare the proposed cross-attention design against other plausible alternatives for incorporating this embedding. For instance, the standard ViT approach (adding the embedding to image features before self-attention) or simply concatenating the spherical coordinates as extra channels would provide stronger evidence that the specific cross-attention is optimal.\n\n3) About Seam Artifact \n\nThe paper acknowledges visible seams at the panorama's left-right boundary as a limitation. Given that SphereViT is designed with explicit knowledge of spherical coordinates, it is somewhat surprising that this issue persists. A brief discussion on why the current architecture does not fully resolve this and potential solutions would strengthen the paper.", "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": "VOHJ7T8OG9", "reviewer": "Reviewer_RcRx", "summary": "This paper proposes DA$^2$, an end-to-end, panoramic depth estimation model that achieves strong zero-shot generalization.\n\nPrevious methods face challenges in zero-shot panoramic depth estimation because:\n1. [Data scarcity] Existing panoramic depth datasets are rare\n2. [Spherical distortions] Common equirectangular projection of panoramas clearly distorts near poles.\n\nCorrespondingly, the authors addess this problems with two main contributions:\n1. [Panoramic data curation engine] Considering the rich data in perspective depth estimation, this paper proposes a panoramic data curation engine. Firstly, the perspective RGB / depth images are transformed into spherical space via Perspective-to-Equirectangular (P2E) projection. Then, for the transformed RGB is panoramic out-painted to obtain the \"full\" panorama. This engine curates additional 540K data samples.\n2. [SphereViT] A ViT network that explicitly incorporates spherical coordinates into latent feature via spherical embeddings, designed to mitigates the effect of spherical distortions.\n\nTo validate DA$^2$, the authors conduct a comprehensive comparison, including both panoramic depth estimation methods and perspective methods. The results clearly show the SOTA performance of DA$^2$.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Reasonable analysis of existing methods, and straightforward solutions targeting the existing limitations. The existing issues (Data scarcity, Spherical distortions) is critical in panoramic depth estimation. And the solutions (Panoramic data curation engine, SphereViT) effectively address the limitations, making this paper valuable in panoramic depth estimation.\n\n2. In the experiments, the author built a comprehensive benchmark among various datasets, and compared DA$^2$ with both zero-shot / in-domain, panoramic / perspective approaches. The quantitative and qualitative results clearly show DA$^2$'s SOTA performance. The ablation studies clearly show the performance gained via scaling-up with perspective data, also the spherical embeddings in SphereViT.\n\n3. The authors claimed that the code and the curated data will be open-sourced. This will be a valuable contribution to the research community.\n\n4. The writing is clear and also the figures. The paper is easy to follow.", "weaknesses": "1. Performance concerns on real-world panoramas: The reviewer noticed that the curated panoramic data are basically come from synthetic perspective data. The domain gap between synthetic data and real-world data may decrease the model's performance on real-world panoramas.\n\n2. While the data curation engine is effective, the depth label is largely missing, which is better obtained. The author should add more discussions on this matter.\n\n3. More qualitative results: There are only 2 cases in this paper's qualitative comparison.\n\n4. The reviewer also noticed left-right seams in the reconstructed 3D point clouds. The author should add more discussions about this problem, which can be critical in real-world applications.\n\n5. Minors:\n\n    (1). The arrow on the right figure of figure 2 is in opposite direction.\n\n    (2). The citation of MVS-Synth in line 371, use \\citep instead.", "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)