Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image
Authors: Ming Qian
Zimin Xia
Changkun Liu
Shuailei Ma
Wen Wang
Zeran Ke
Bin Tan
Hang Zhang
Gui-Song Xia
Venue: ICLR 2026 Poster (2026)
Keywords: 3D generation
novel view synthesis
satellite to street-view generation
feed-forward image to 3D
outdoor scene generation
Primary area: generative models
TL;DR: Given a single satellite image, Sat3DGen generates a street-view-renderable NeRF-based 3D scene with strong geometry, enabling large-area meshing, multi-camera surround-view video, semantic-map-to-3D, and single-image DSM estimation.

Abstract: Generating a street-level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade-off: geometry-colorization models achieve high geometric fidelity but are typically building-focused and lack semantic diversity. In contrast, proxy-based models use feed-forward image-to-3D frameworks to generate holistic scenes by jointly learning geometry and texture, a process that yields rich content but coarse and unstable geometry. 
We attribute these geometric failures to the extreme viewpoint gap and sparse, inconsistent supervision inherent in satellite-to-street data.
We introduce Sat3DGen to address these fundamental challenges, which embodies a geometry-first methodology. This methodology enhances the feed-forward paradigm by integrating novel geometric constraints with a perspective-view training strategy, explicitly countering the primary sources of geometric error.
This geometry-centric strategy yields a dramatic leap in both 3D accuracy and photorealism. {\revisioncolor For validation, we first constructed a new benchmark by pairing the VIGOR-OOD test set with high-resolution DSM data. On this benchmark, our method improves geometric RMSE from 6.76m to 5.20m.} Crucially, this geometric leap also boosts photorealism, reducing the Fr\'echet Inception Distance (FID) from $\sim$40 to 19 against the leading method, Sat2Density++, despite using no extra tailored image-quality modules. We demonstrate the versatility of our high-quality 3D assets through diverse downstream applications, including semantic-map-to-3D synthesis, multi-camera video generation, large-scale meshing, and unsupervised single-image Digital Surface Model (DSM) estimation. The code will be released on https://github.com/qianmingduowan/Sat3DGen.

Published as a conference paper at ICLR 2026
SAT3DGEN: COMPREHENSIVE STREET-LEVEL 3D SCENE GENERATION FROM SINGLE SATELLITE IMAGE
Ming Qian',
Zimin Xia',
Changkun Liu',
Shuailei Ma²,
Wen Wang", Zeran Ke', Bin Tan', Hang Zhang',
Gui-Song Xia
LIESMARS & School of Artificial Intelligence, Wuhan University EPFL
3 HKUST Northeastern University 5 'Zejjiag University 6 Ant Group" 7 Amap, Alibaba Group
ABSTRACT
Generating street-level 3D) scene from single satellite image is crucial yet challenging task. Current methods present a stark trade-off: geometry-colorization models achieve high geometric fidelity but are typically building- (occusg and lack semantic diversity. In contrast, proxy-based models use feed-forward image-to- - frameworks to generate holistic scenes by jointly learning geometry and texture, process that yields rich content but coarse and unstable geometry. We attribute these geometric failures the extreme viewpoint gap and sparse, inconsistent supervision inherent in satellite-to-street data. We introduce Sat3DGen to address these funda- mental challenges. which embodies a geometry-firee methodology. This methodol- ogy enhances the feed-forward paradigm by integrating novel geometric constraints with a perspective- view training strategy, explicitly countering the primary sources of geometric error. This geometry-centre strategy yields dramatic leap in both 3D accuracy and photorealism. For validation, we first constructed a new benchmark by pairing the VIGOR-OOD test set with high-resolution DSM data. On this bench- mark, our method improves geometric RMSE from 6.76m to 5.20m. Crucially, this geometric leap also boosts photorealism, reducing the Fréchet Inception Distance (FID) from 400 to 19 against the leading method, Sat2Density++, despite using no extra tailored image-quality modules. We demonstrate the versatility of high- quality 3D assets through diverse downstream applications, including semantic- map-to-3D synthesis, multi-camera video generation, large-scale meshing, and unsupervised single-image Digital Surface Model (DSM) estimation. The code will be released on ntttps://githus com ccmm://aam.rgguuuver/aaaeeeeee
INTRODUCTION
Street-level 3D scenes are useful for mapping, robotics, simulation, and media creation
(Wokkman
al. 2017 Toker et al. 2021 Xie al.) 2024 Zhou al. 2020 Shi al., 2022
Li al.
2024a). Ground-level capture is costly and uneven across regions (Anguelov et al. 2010). whereas
satellite imagery offers wide coverage, low cost, and frequent updates (Campbell & Wynne 201T).
These characteristics motivate the generation of street-level 3D from overhead satellite images for large-scale, long-term applications. Our goal to generate 3D scene that faithfully preserves the semantics and appearance of an input satellite image and that can be rendered for strect-view images and videos.
Existing methods for generating 3D from single satellite image fall into two categories: 3D geometry colorization (Hua et al. 2025 Li et al. 2024b) and 3D proxy for image rendering (Qian et al. 2023 2026). 3D geometry colorization follows two-stage pipeline predict and then texture 3D building geometry. While producing clean building models, these methods fail to capture non-building elements (e.g., zebra crossings, trees), resulting in outputs weakly consistent with the input satellite image (Fig. I (a.b)). Extending them beyond buildings would require fine-grained geometry labels for many classes, which are scarce. 3D proxy for image rendering uses tailored feed-forward image-
to-3D frameworks (Hong et al. 2024 Xiang et al. 2024 Yu et al. 2021
Zhang et al. (025) to learn
Corresponding author
As of the submission deadline, the official implementations of Sar2Scene and Sat2City had not heen fully released. We therefore report the 3D results shown in their papers and project pages.

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Published as a conference paper at ICLR 2026
Image Renderable
Satellite Faithful
More than Buildings
Supervision Signal
Synthetic or Real
Geometry Quality
Sat2City Sat2Seene Sat2Dnnity
X
3D Imgac
Synthetic
X
Real
V
Imgac Imgae
Real
x
Ours
/
Real
(a)
SatCCity
Input
(c) SarDensity++
(d) Ouns
Comparisan of single satellite input to 3D scene methods
Surround-view multi-cameni and panorama vidoo gen.
Satellite DSM
Semantic Map 3D
Large
High-quality Renderable3D generation from from satellite image support multiple applications...
visual results). Given an input satellite image, (a) Sat2Scene and (b) Sat2City generate only shells of buildings and roads and miss non-buile moniuilddin semantics; (c) Sat2Density+* and (d) Ours are faithful to satellite semantics and appearance, but Sat2Density++ is heavily distorted, whereas our Sat3DGen yields more structured, higher-quality 3D representation.
Figure 1: Comparison of 3D scene generation methods (top: attribute table; bottom:
coarse, differentiable 3D proxy via joint optimization under 2D supervision. These methods semantically faithful but yield poor geometry: boundaries are degraded, roofs are unrealistic, and floating artifacts are common (Fig. (c)).
Our goal requires preserving the rich semantics of the input satellite image, making the proxy-based paradigm more suitable than geometry colorization. Encouragingly. recent object-level feed feed-forward image-to-3D works (e.g., InstantMesh (Xu et al. 2024),, L.R (Hong et al. (2244) have demonstrated that high-quality 3D can learned from 2D supervision alone. This suggests that the poor geometry of existing scene-level proxy methods is not fundamental flaw of the paradigm. Instead, we hypothesize it stems from insufficient geometric constraints to handle the unique challenges of outdoor scenes. Specifically, the supervision from only one satellite patch and few ground-level panoramas is extremely sparse. This sparsity, coupled with the extreme viewpoint gap, leaves rooftop geometry underconstrained and induces artifacts like holes and floaters vertical facades. Additionally, footprint mismatch between the satellite and street views often destabilizes the geometry scene boundaries.
To solve these specific problems, we propose Sat3DGen, which embodies a holistic, geometry-fisg methodology. Our strategy is invent new feedforward image-to-3D architecture from scratch, but to elevate a general framework by demonstrating how to effectively solve its core geometric failures. To enforce plausible vertical structures and suppress floating artifacts, introduce Graviry-based Density Variation Loss. To address boundary errors stemming from the footprint mismatch, a Spatial Token regularizes peripheral layouts. To resolve rooftop ambiguity. Monocular Relative-Depih Prior constrains satellite-view depth. Furthermore, mitigate the issue of sparse supervision, we employ Perspective View Training, jointly training on panoramas and their projected views to increase effective viewpoint coverage and photometric consistency.
evaluation, this emphasis on geometry translates directly to substantial quantitative and perceptual gains. We first validate our geometric improvements against the leading method, Sat2Density on a new benchmark we constructed by pairing the VIGOR- test set with 1-meter resolution DSM data. Sat3DGen achieves geometric RMSE of 5.20m, significant reduction from Sat2Density++'s 6.76m. Crucially, this leap in 3D accuracy directly fuels dramatic improvement in photorealism. Even though it includes no components tailored to image quality, our framework reduces the Fréchet
2

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Published as a conference paper at ICLR 2026
Inception Distance (FID) on the VIGOR- unseen-city split from Sat2Density++'s 440 to 19. The resulting assets support diverse downstream applications such as monaatic-map-to-33 synthesis, surround-view video from satellite imagery, large-area mesh generation, and single-image Digital Surface Model (DSM) generation without ground-truth depth supervision.
RELATED WORKS
Feed-Forward Image to 3D Works has gained popularity for producing high-quality 3D assets. Recently, large reconstruction models (Hong et al., [2024] Tang al. 2024 Xu et al. [024 Xiang et al. 2024) have focused on generating object-level 3DD assets, leveraging larger datasets, more refined annotations, and more substantial models to improve the quality of the generated assets, achieving impressive results. However, existing works primarily focus on object-level generation, presenting additional challenges when applying these models in outdoor scenes. our work, focus on generating high high-quality, comprehensive street-level 3D from single input satellite image, thereby naturally enhancing the quality of generated videos and supporting various applications.
Single Satellite to Street-view Synthesis. Early studies generate individual street-view images from single satellite patch (Regmi Borji 2018: 2019, Toker et al. 2021, Shi al. 2022 Lu al. 2020 Tang et al., 2019), but they do not produce usable 3D or multi-view consistency. Later works
synthesize street-view videos by learning a colored 3D asset from the satellite input (Li al.) 2021
2024b Qian et al. 2026). colooitry-olorization methods (Li al. 202 2024b) often rely on height maps and vertical-facade assumptions, yielding building-centric scenes and missing non-buildn non-building semantics such roads, crosswalks, and trees. Our work builds on the proxy-basee line and focuses on improving 3D quality under the same single-satellite input setting.
METHOD
As shown in Fig. 2 given a single overhead satellite image Lat and an optional global illumination feature input ful used solely control illumination when rendering street views, model can synthesize a renderable 3D scene that (i) preserves the semantics and appearance of sat (ii) supports high-fidelity satellite, perspective street-view, and panoramic rendering under controllable lighting, and (iii) can be exported as a mesh with Marching Cubes.
Add extra
spatial
Sat. Input
Decoder
ONIG
Sat. View
Image Tokens
MLP
3D Generation
Pers, Sir.
IlIa. feature
Illu. Input
Sky Feature
Illmmiaatoon Adaptation
Rendering
Figure Diagram of the proposed Sat3DGen framework.
We adopt feed-forward image-to-3D framework instantiated with a tri-plane NeRF (Chan et al. 2022) as baseline. A frozen DINO-v3 encoder (Siméoni et al. 2025) maps /sat to compact token grid, which is optionally padded with learnable spatial capacity at the periphery and then decoded into a high-resolution tri- vi-plane feature field. A lightweight MLP predicts density and color features from tri-plane features for volumetric rendering. Besides, we follow the illumination adaaptive design in Qian et al. (2026) to mitigate the sky/illumination mismatch issue.

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Published as a conference paper at ICLR 2026
Beyond this backbone, we introduce three novel geometry-orienterr components that substantially enhance performance and depart from prior work (Qian et al. 2026): a gravity- based density variation loss to favor gravity-aligned structures, monocular depth prior in satellite view to resolve rooftop ambiguity, and peporaami--t--psppecete supervision to densify viewpoints. The remainder of this section details the backbone; the losses and supervision strategy presented in Section 3.4
3.1 SATELLITE TO 3D GENERATION
Given a satellite image, our pipeline constructs radiance field by encoding it into a 2D token grid with a frozen backbone, padding with spatial tokens to expand the effective scene extent, and decoding the tokens into tri-plane features.
Satellite Encoder and Tokenization. Following exsiting object-level feedforward image to 3D works, use frozen pretrained VIT model image encoder (Xu et al. 2024 Xiang et al. 2024). In practise, a frozen DINO-v3 VIT encoder (Siméoni et al. 2025) Egat processes /at into 2D token grid:
F Fioken
(Taa) E BR",xiixC,
with H, = We = 16 and C = 1024 in all experiments. This token grid is the minimal scene-level latent that will be lifted into 3D feature field.
Spatial Tokens. Street-view supervision often observes buildings and roads extending beyond the satellite crop, which induces boundary artifacts if the 3D field is constrained the crop footprint. We therefore pad F token with a border of N zero-valued spatial tokens on each side:
F tooeon-pd PAD (F (ooken) E IR (2
= 2.
(2)
With H; W, 16, padding yields F token_pad
R 20x000 1024 Suppose the original scene cube spans L meters per side (c.g., L 50 m). In that case, padding enlarges the effective cube to
L [+ 2N (c.g.. 62.5 m), providing degrees of freedom accommodate peripheral content while stabilizing interior geometry.
Tokens - Tri-Plane Features. A lightweight VAE-style decoder (Esser et al. 2021) upsamples tokens into a high-resolution tri- tri-plane feature map with an upsampling factor = 16:
F us D(Fwoee pad) E peesu
(3)
where respi 320 when padding is used and 256 otherwise. Channels are reshaped into three orthogonal planes XXY
Tri-Plane Sampling. 3D query point x E R within the normalized scene cube is orthographically projected onto each plane and bilinearly sampled to obtain features @x (x), 6xz (xx(xx) Φv 2 (x). The three plane features are aggregated by elementwise summation form the fused feature:
hxx)
(x)(x)
(x) + (xz(x) + (x)..).
(4)
Then, a shallow MLP predicts density and color:
(x), c(x, w) = MLP(h(x), w):
where (x) denotes the volume density, c(x, w) is the radiance color conditioned on an illumination code w, and h(x) is the fused tri- plane feature at location x; the MLP uses shared trunk with two output heads for density and color.
3.2 COMIINATIOO-ADAPTINN RENDERING AND SKY GENERATION
Global Illumination Code. Following Sa12Density++, we extract global illumination feature fu from real street-view image IuI in a statistical way (Qian et al. 2026), and then project to style code Win with a light mlp:
Will En((Tn
During training. fn extracted from the groundtruth strect-view panorama image to mitigate sky/illumination mismatch, and at test time, enables lighting-controllable rendering.
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Sky Region Generation with Spherical Feature Maps. To natively support perspective view rendering, the sky module must provide consistent appearances for arbitrary viewpoints. We achieve this by modeling the sky as a feature map the sphere. A lightweight 2D convolutional decoder produces this sky feature map from win-
Fuy Gay(wnn) E R212X512xG,
where matches the renderer's feature channels. For any given ray with normalized direction d E S?, we convert its Cartesian coordinates to spherical angles (0, ø) and bilinearly sample Fay to obtain the sky color feature Cuky (d). This design elegantly provides consistent sky features for both panoramic and perspective-view rendering.
3.3 VOLUMETRIC RENDERING AND OUTPUTS
Ray Marching and Compositing. For camera ray r(t) td, t [t,, tyl, sample points {x+} with step 0g and compute transmittance T₁ exp (- (xx) 8,). The rendered color is
C(r) (1-e c(xX,, wa) + Tout Csky (d),
Th
(8)
where Yow is the remaining transmittance upon exiting the volume. The same renderer supports perspective and spherical cameras; the latter yields full panoramas.
Renderable Views and Mesh Export. Our model can render (i) satellite views, (ii) perspective street-view images at arbitrary camera poses, and (iii) panoramic street views. For asset export, we evaluate a a dense grid and run marching cubes with fixed isovalue to obtain watertight mesh. The sky branch is excluded from meshing.
3.4 Loss FUNCTIONS.
Gravity-based Density Variation Loss. Outdoor scenes reconstructed from sparse views often exhibit geometric artifacts like floating debris and hollow grounds. To mitigate these issues, we introduce regularizer based on simple design principle: volumetric density should generally be non-increasing with altitude. The design this regularizer motivated by the physical effect of gravity. To translate this concept into the NeRF framework, we leverage the volume density o. In NeRF, a measures light obstruction, making it a natural proxy for physical matter, given that outdoor scenes predominantly composed of opaque surfaces like terrain, rocks, and tree trunks. Following the intuition that gravity causes matter to accumulate at lower elevations, we establish our principle: should generally be non-increasing with altitude. This is consistent with real-world observations; for instance, solid ground and tree trunks are typically found at lower altitudes, while higher altitudes often contain sparser structures like leafy canopies or simply open air. Grounding our regularizer in this physical intuition helps the model learn more plausible geometry.
Specifically, we sample 3D point x R& and a corresponding point x' x ôz at slightly higher altitude, where 8z is small displacement vector purely in the upward (anti-gra sentitgravit) direction. We then penalize cases where the density at the higher point x' is significantly greater than the density at the lower point x. This is enforced by minimizing the following loss:
Lyaav óz [ReLU(a + 8z) (xx) c)],
where the slack variable (set to in our experiments) provides soft constraint, allowing for legitimate hollow or overhanging structures such as tree canopies, arched roofs, and bridges. This loss effectively suppresses floating artifacts and fills baseless cavities while preserving realistic sparsity under overhangs.
Satellite- View Depth Regularization. Each scene provides one bird's seed' satellite image and only a few street-view observations; rooftops lack multi-view photometric supervision and tend to be irregular. We therefore impose relative depth prior in the satellite view using pseudo labels from Depth Anything v2 (Yang et al. 2024). Let D* be the pseudo relative depth for the satellite camera and the rendered depth from our field. We adopt a scale-and-shift invariant MiDaS-style loss (Ranful al. 2022):
Edepth
Σ N
(t) +t00+t-''00 Da(o)) -
A Σ ((s((p) +t) vD' (10)
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where (s, t) are optimal scale and shift estimated per image by least squares, N is the number of valid pixels, and denotes spatial gradients. This encourages consistent depth ordering and smooth rooftops without requiring metric depth.
Photometric Reconstruction and Adversarial Loss. We supervise three rendered view types: satellite views, panoramic street views, and perspective crops projected from panoramas. Let I, be a rendered image and I the corresponding ground truth. The photometric objective combines per-pixel reconstruction with perceptual similarity, and add adversarial term mitigate blur from pure regression in complex outdoor scenes:
LRGB Σ
18t
Alpips Ei (lr.1f) + AGAN Σ CCAN
(11)
where LLPIPS is the perceptual loss and LGAN follows the StyleGAN2 hinge objective (Karras et al., 2020) for realism. In practice, the index ranges over satellite, panorama, and perspective supervision views rendered during training.
Sky Losses: Opacity BCE and Masked Sky L1. To disentangle the sky from the 3D scene and improve sky quality,
two complementary losses panoramic street views.
Let Maky E {0, 1) be the pseudo binary sky mask a panorama (1 for sky), which is generated by the off-the-shelf model (Zhang et al. 2022),, and let Tou E [0, 1] be the residual transmittance per pixel from volumetric rendering (interpreted the fraction attributed to the sky background after alpha compositing). We apply a binary cross-entropy:
op LaCr Maky)-
(12)
Denote the rendered panorama 4paoo and the ground-trun panorama -a We enforce color fidelity on sky pixels only:
Lay-L11
Σ
Masy (p) | (p) - - Ipam (p)
(13)
Msy
Overall Objective. The full training objective is weighted sum of the above terms:
Liveal Xegh.RGB + Agnv + oa + M.ky.-.Ifoayyl + Adephh Edepth
(14)
where weights X. are hyperparameters.
EXPERIMENTS
Datasets and Splits. We train on GPS-matched snelliteeground image pairs. Training uses three cities (Chicago, New York, San Francisco) in the VIGOR dataset (Zhu et al. 2021). and off-oo-dommin (OOD) testing uses the held-out city Seattle (VIGOR-OOD), VIGOR provides multiple street-view panoramas per satellite tile together with relative camera poses; the satellite zoom level is fixed at 20, yielding near-constant ground sampling distance per pixel. In total, we use 78,185 pairs for training and 1,875 pairs for quantitative evaluation on VIGOR. More details, data preparation, and statistics are provided in Appendix F
Implementation Details. We resize satellite images to 256 x 256 input, and the generated triplane features have dimensions of 320 x 320 x 32 x 3. For fair comparison, the generated panorama images are shaped 512 128, and the perspective images are 256 256. The training process is conducted on 8 NVIDIA H20 GPUs with a batch size of 32, comprising 600,000 iterations for the training phase. More implementation details can be seen in the supplementary materials.
3D Comparision. We compare our 3D results with Sar2Scene (Li al. 2024b), Sat2City (Hua et al.) 2025) and Sat2Density++ (Qian et al. 2026). The colored meshes are generated by the Marching Cubes algorithm for Sat2Demsity++ and ours. Since there are ground truth 3D assets available to evaluate the reconstruction quality, we can only perform qualitative comparisons, as shown on Fig. (b). and Fig. 6 We observe consistent improvements in geometric plausibility
Fig.
Fig.

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Published as a conference paper at ICLR 2026
Input Satellite
Sa12Desity++
Ours
Figure 3:
The comparison of generation 3D between Sat2Density++ QQian et al. 2026) and our model on the VIGOR-OOD test set (Zhu et al. 2021).
Input
(b) generated by Sar2Density=* (lef) (right)
(c) Generated panorama frames by Sar2Density + (Top) and Ours (Botto)
(d) Generated multi-veww perspective frames by Ours
Figure 4: Visual results of generated mesh (b), panorama videos (c), and multi-view perspective video (d) from single satellite image input and camera trajectories (rainbow line) (a). The full video can be seen in the supplemental materials.
and semantic faithfulness across diverse urban layouts. Compared with Sat2Scene and Sat2City, which mainly texture simplified building blocks and leave non-building regions weakly modeled, our reconstructions better preserve road markings, crosswalks, medians, tree belts, and sidewalks that are visible in the satellite input(Fig. Relative to Sar2Density++ although both adopt a ford-d
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Table I: Quantitative results of street-view comparison the test set of VIGOR-OOD. Bold indicates the best results, while underlined text represents the second-bes -est results.
Realism Evaluation
Semantic DINO
Structure SSIM
Pixel PSNR1
Perceptual Similarity
Method
FID
KIDJ
Palex +
Paquese
ControlNet
23,6
/
/
0.34
12.02
0.46
0.34
ControlS2S
28.0
0,42
13.80
0.38
0.27
Sat2Density
85.6
0.079
0.451
0.32
12.48
0.45
0.37
Sat2Density++
40.8
0.035
0.465
0.34
12.51
0.44
0.34
Canonical Image-to-3D
35.6
0.030
0.479
0.35
12.63
0.42
0.32
Ours
19.2
0.014
0.525
0.37
12.83
0.38
0.30
image-to-3D framework, our method jointly integrates several lightweight components to improve geometry learning at street level under sparse, cross-view supervision. Taken together. these design choices strengthen scene layout near the satellite patch boundary, bias the volumetric field toward gravity-aligned structures, and inject rooftop depth cues from the overhead view, while increasing effective viewpoint coverage via panorama-to-peran noopeesppectiv supervision. The resulting reconstructions exhibit more coherent ground planes and periphery geometry, with fewer torn edges and warped borders across the tile extent. Rooftops and building bases become geometrically plausible: roofs avoid bubbling or sagging, flat roofs remain planar, pitched roofs retain credible tilt, and facades connect cleanly to the ground (Fig.
Fig. and Fig. 6).
Image and Video Comparison. We provide quantitative and qualitative comparisons. The qualita- tive comparison can be seen on Fig. 4 and more video comparisons are provided in the supplementary ZIP archive. The quantitative comparison is shown [
Quantitative comparison. We follow prior work (Qian et al. We report Fréchet Inception Distance (FID) (Heusel et al.
2026 Ze et al. 2025) for evaluation. 2017) and Kernel Inception Distance
(KID) (Bińkowski et al.] (018)) to quantify distributional simil Iaritayyy between generated and real image sets, reflecting realism and coverage. Semantic alignment is assessed using a DINO-based feature similarity following (Qian et al. 2026). Pixel-level fidelity and structural similarity are evaluated with PSNR and SSIM, and perceptual similarity is measured with LPIPS (Zhang et al. 2018) using AlexNet and SqueezeNet backbones, denoted Palex (Krizhevsky et al. 2012) and Psqueeze (landola al. 2016). Given that our task is an input-view conditioned novel view generation problem with a very large viewpoint gap, pixel-level correspondence to any single real image is inherently brittle due to occlusions, parallax, and minor pose or scene changes. The primary desiderata are photorealism and semantic faithfulness rather than exact pixel matching. We therefore treat FID, KID, and DINO-based semantic similarity as primary metrics, and report PSNR, SSIM, and LPIPS for completeness.
We compare our model with Sat2Density (Qian et al. 2023). Sar2Density+++ (Qian et al. 2026), the diffusion-based image generation model ControlNet (Zhang al.] (023), and SS2S (Ze et al. 2025). We also provide results from a feed-forward image-to-3D model (denoted as "Canonical Image-to-3D" in Table which removes the proposed spatial token module, gravity-based density variation loss, perspective training strategy, Lgrav. and Leepth proposed in our method. For Sat2Density, Sa12Density++, and our model, we pair each satellite image with a randomly selected global illu- mination feature input from the training set for a fair comparison. For ControlNet and ControlS2S, we use the results reported in the ControlS2S paper. is worth noting that the ControlS2S work, they divided the training and testing sets within each city. In contrast, our approach trains on three cities and conducts out-of-domain testing on an additional city, Seattle. This out-of-domain testing is significantly more challenging.
As shown in Table [] our method leads in FID, KID, and DINO. Compared with the Canonical Image-to-3D, we only add our 3D optimization modules, yet the rendered images improve a lot. When compared with diffusion image generation models, our model also achieves lower FID and KID. This is because we learn a high-quality, fiew-consistent 3D representation that handles the large aerial-to- tooooondd viewpoint change and produces more realistic and semantically correct images.
Qualitative Comparison. We provide qualitative comparison in Fig. 4 (c), and more video compar- isons can be seen in the zip supplemental materials. We can render videos from a given satellite image and any street-view camera trajectory, as illustrated in Fig. (c) We compared the panorama video
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Table 2: Ablation results the VIGOR-OOD test set. The first removes all proposed key components: Ldep. Lgav. Spatial Tokens, and perspective training. The next three rows ablate each component individually under the same setting (without perspective training). "Base (Full model w/o perspective training)" enables Ldep. fegaa, and Spatial Tokens but omits perspective training. "Full model" enables all components, including perspective training.
DINOv3 Ldep Lgrav Sp. Tok. Per. Train FID
KID x100
RMSE
Canonical Image-to-3D
35.6
30.1
6.21
Base w/o Ldep
23.7
18.4
5.75
Base w/o Lgaa
25.9
19.0
5.21
Base w/o Spatial Tokens
24.8
18.1
5.64
Base (Full w/o per. training)
21.6
16.2
5.23
Full model
19.2
13.6
5.20
Left: Spatial Tokens, Right: w. Spatial Tokens
Left: Gravity-based Density Varistion Loss, Right Gravity-based Density Variation
Left: wio Satellitovview Depth Rogularization, Right: w. Satellite-view Depth Regularization
Figure 5: Qualitative ablation on key modules.
results generated by Sat2Density++ and our model. Consistent with the conclusions drawn from the generated 3D assets, model enhances the generated videos primarily by reducing artifacts and producing smoother edges around buildings and scene boundaries through the generation of higher-quality 3D representations.
Geometric Comparison To quantitatively evaluate the geometric accuracy of the generated 3D models, we compare the predicted satellite-view depth against the ground truth. The detailed procedure for preparing the ground truth DSM pairs for VIGOR-OOD test is provided in the Appendix Section D As shown in Table we adopt the standard evaluation metrics from satellite stereo literature (Gao al. 202T). including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the percentage of pixels with errors below 2.5m and 7.5m.
The results clearly demonstrate the effectiveness of our proposed methodology. The state-of-the-a method, Sar2Density++, achieves an RMSE of 6.76m. Our controlled baseline, Canonical Image-to- 3D, which benefits from stronger backbone, already improves upon this with an RMSE of 6.2 Our full model, however, significantly outperforms both, establishing new state-of-the-art with an RMSE of 5.20m and a MAE of 3.47m. Notably, our model reconstructs 62.69% of the surface with an error of less than 2.5m, substantial improvement over Sat2Density++ (49.69%). This underscores the superior capability of our method in generating geometrically precise urban scenes. In addition to these quantitative results, provide extensive qualitative comparisons of the rendered satellite-view

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Table 3: Quantitative comparison for predicted DSM.
MAED
RMSE
2.5m 1
7.5m
S
4.72
6.76
49.69
83.65
Canonical Image-to-3D
4.23
6.21
52.73
84.54
Base w/o Lgaa
3.53
5.21
61.17
88.94
Base w/o Ldep
3,82
5.75
59.88
87.04
Base w/o Spatial Tokens
3.87
5.64
57.10
86.46
Base (Full w/o per. training)
3.52
5.23
61.97
88.52
Ours (Full model)
3.47
5.20
62.69
88.68
depth and the groundtruth DSM in the Appendix Fig. which visually corroborate our model's superior geometric fidelity.
We conduct a comprehensive ablation study to validate our design choices, with quantitative results in Table 2 Table 3 and qualitative visualizations in Fig. 5 Starting from our Canonical Image-to- -o baseline (FID 35.6, RMSE 6.21m), integrating our core geometric priors (Leep. Lynv, Spatial Tokens) synergistically boosts performance an FID of 21.6. Among them, gravity-based loss (Lgra) is most critical for photorealism, as removal causes the largest FID degradation (to 25.9). Conversely, removing Ldep Spatial Tokens leads to more significant geometric errors (RMSE increases to 5.75m and 5.64m, respectively). Finally, adding perspective training achieves our best results across both photorcalism (FID 19.2) and geometric accuracy (RMSE 5.20m).
Ablation Study.
These quantitative gains visually corroborated in Fig. As intended, Spatial Tokens regularize boundaries, Lgrav yields straighter facades and reduces floaters, and Ldep corrects rooftop geometry. The consistent improvements across metrics and visuals confirm that each component is essential, targeting distinct aspects of the final high-fidelity reconstruction.
Applications. We show a few application examples Fig. Additional downstream results and implementation details are provided in Appendix Section B including satellite-to-DSM (metric depth) conversion without ground truth depth data supervision (Fig. 99. semantic map to 3D recon- struction (Fig. [II, large-are area 3D mesh generation from single large satellite patch (Fig. 10). and surround view multi-camera video synthesis from satellite imagery (Section B.2).
5 CONCLUSION
In this work, we introduce Sat3DGen, novel algorithm for generating 3D outdoor scene from satellite image. Our algorithm is trained exclusively on GPS- satellite and panorama street view image data. By incorporating the novel approaches such as gravity- based Density Variation Loss and spatial tokens proposed in this paper, experiments demonstrate that Sat3DGen effectively improves the quality of the generated 3D assets and enhances the quality of the generated videos. We hope that our approach inspires future research in 3D outdoor scene generation and that model can effectively support downstream tasks such as digital Earth and scene simulation, thereby enhancing the efficiency and performance of outdoor 3D-related applications.
ACKNOWLEDGEMENTS
This work was supported by NSFC under Grant 523251
ETHICS STATEMENT
Our work builds on a publicly available dataset (VIGOR Zhu et al. 2021), which consists of satellite images and street-view panoramas collected from widely accessible map platforms. We do not gather or release any personally identifiable information (PII). All data sources follow the original dataset licenses and terms of use, and no effort is made to identify individuals or private
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properties beyond what is already visible in the released benchmarks. Potential misuse of our approach should be considered: while our method advances urban-scale 3D scene reconstruction for beneficial applications such autonomous driving simulation, AR/VR urban planning, and geographic visualization, it could also be applied to large-scale surveillance if deployed irresponsibly. To mitigate such risks, emphasize that our framework is intended solety for academic research and positive societal use cases, and we release neither additional sensitive data nor pre-trained models tied private regions.
REPRODUCIBILITY STATEMENT
We aim ensure the reproducibility of our results. All implementation is based on standard deep learning frameworks (PyTorch (Paszke et al. 2019)). and our model architecture, training strategies, and evaluation protocols are fully described in Sec. 4 and Appendix [ We specify dataset splits, preprocessing steps, and evaluation metrics in detail, and we adopt widely used benchmarks (VIGOR) to facilitate fair comparisons. Hyperparameters, batch sizes, number of iterations, and hardware configurations are reported in the Implementation Details paragraph. We will release the training scripts, configuration files, and inference code, together with instructions for data preparation and evaluation, upon publication. This enables other researchers to reproduce our quantitative scores and qualitative visualizations, and extend our method new geographic regions or related cross-view generation tasks.
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This appendix provides more details and results of Sat3
LLM USAGE
In this section, we clarify the role large language models (LLMs) in preparing this work. The model was used exclusively for language polishing, such refining grammar, style, and readability, without contributing to the research design, analysis, or conclusions.
A MORE 3D AND VIDEO RESULTS
A.1
MESH RESULTS COMPARED TO SAT2DENSITY++
We provide additional mesh results in Fig. o with all satellite images sourced from the VIGOR-OOD
test set.
A.2 MESI RESULTS COMPARED TO CANONICAL IMAGE-TO-3D
As shown in Fig. 7 the Canonical Image-to-3D baseline produces significantly inferior geometry compared to our full model. Specifically, its ground surfaces are noisy and uneven, and fails to capture distinct shapes for trees or produce flat building rooftops. The mesh boundaries also exhibit irregular, spiky extrusions. These geometric flaws result in a much lower rate watertight meshes. In contrast, model consistently generates smoother surfaces, more plausible object structures, and cleaner boundaries. This visual evidence strongly corroborates the quantitative results Table 2 where the baseline's poor geometric fidelity is reflected in its high RMSE score.
A.3 PANORAMA VIDEO RESULTS.
In the supplementary ZIP archive, the 'video result' folder contains results from 16 sets of VIGOR- OOD test data. Each video is named according to its latitude and longitude, allowing you view the latest satellite images by entering these coordinates into Google Maps]] In each video, the top-left corner displays the satellite image and camera trajectory, the top-right section presents the results of the Sat2Density++ (Qian et al.) 2026), and the adjacent section showcases our results. From the presented videos, it is clear that our method, by producing enhanced 3D assets, achieves superior panorama video generation yielding more regular building shapes, fewer floating artifacts, and improved generation of ground vehicles, road signs, and trees.
A.4 SATELLITE- VIEW DEPTH COMPARISON
We present qualitative comparison of the generated depth maps in Fig. It is important to note the inherent challenge of temporal misalignment between the satellite images and the ground truth DSM, which was collected over year (Section D). This leads discrepancies from transient objects (e.g.. vehicles) and noise within the DSM itself, making perfect reconstruction unattainable.
Despite these challenges, clear difference emerges. Sat2Density++ (b) yields werly-smoother results with indistinct building edges and noisy ground planes. In contrast, our model (c) generates significantly sharper and more geometrically plausible structures, featuring flat rooftops and clean ground surfaces. These visual improvements directly corroborate our superior quantitative metrics reported the main paper, demonstrating better capability reconstruct high-fidelity geometry from a single image.
B MORE APPLICATIONS RESULTS.
B. SINGLE SATELLITE IMAGE TO DSM (METRIC DEPTH)
AS shown in Fig. our model can render satellite-view metric depth from the learned NeRF-based 3D representation, even though no metric-depth annotations are used during training.
https::/ htttps://www... http:////wwwgg///////////// e. com/ hommm/pp//////
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Satellite input
Sat2Density++
Ours
Figure 6: Comparison of generation 3D assets between Sat2Density++ Qian et al. 2026 and Ours.
B.2 SURROUND- VIEW MULTI-CAMERA VIDEO GENERATION FROM A SINGLE SATELLITE IMAGE.
We provide some surround-view multi-camera video results Fig. and the supplementary ZIP archive, with four fixed 120-degree FOV perspectives shown the bottom of each video. Our algorithm leverages NeRF-based 3D representations, allowing the generation of perspective im- ages/videos with varying FOV and image sizes. The videos demonstrate that the quality of
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Canonical Image-to-3D
Ours
Figure 7: Comparison of generation 3D assets between the baseline Canonical image-to-3D model and Ours.
generated perspective images is nearly on par with panorama images, underscoring the superiority of model design.
To the best our knowledge, our approach is the first to generate diverse content in multi-view perspective videos from a single satellite image without requiring video data or 3D geometry as training input. The most relevant existing method for generating perspective videos from a single satellite image is Sat2Scene (Li et al., 2024b). However, this method is limited synthesizing buildings and ground surfaces, primarily due to its strong reliance on building height maps converted into point clouds. Moreover, their code and dataset are fully open-sourced, direct comparison with our results is not feasible. In summary, we demonstrate the capability of our algorithm to generate multi-view perspective videos, underscoring its potential upplications in vehicle driving simulation.
B.3 MARGE-SCALLE MESH GENERATION
The process for generating big mesh from large satellite image is as follows: We first download an extensive satellite image from online platforms and then resize it to ensure the per-pixel resolution remains consistent with the pixel resolution of our training data. We then perform inference using a sliding window approach, processing each patch. Each patch resolution is 256x256 with step set to 128. For the density at the edges, we simply average the values to obtain the final result. In our demo, shown in Fig. 10 the input for the large-scale satellite image is at zoom level 19, covering spatial
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(a) Satellite Img.
(b) Sen2ititty
(c) Ours
(d) Real DSM
Figure 8: Comparison of predicted sutellite-view height map.
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Figure 9: Visual results of model generated DSM (metric depth) from the monocular satellite image, which rendered from the satellite view, with no metric depth data for training.
area of approximately 150 m 150 In theory, download larger remote sensing images to generate results over even broader areas. However, at a fixed zoom level, the Google Maps Static API returns most 640x640 pixels per request.
Our demo results clearly demonstrate that the proposed algorithm produces smooth and coherent ground surfaces, seamlessly integrating multiple patches. The transitions between buildings show minimal discontinuities, highlighting the effectiveness of our method and its strong potential for large-scale 3D content generation.
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imput Image
Generated Mesh
Zoom
Figure 10: Given large satellite image, model can generate mesh with sliding window inference mode.
B.4 SEMANTIC MAPS TO 3D ASSETS.
Generating 3D scenes from 2D semantic maps is highly effective application. We can utilize open street map data to obtain ground semantic maps or directly create semantic maps through drawing. In our work, collected multiple semantic haap-ateelite image pairs using OpenStreetMaqj and Google Satellite Maps"
[ to train an additional model for transforming color semantic maps into satellite images. This model is based diffusion <Song al. 2021). composed of ControlNet (Zhang et al. 2023 and SDXL (Podell et al. 2023).
As shown in Fig. 11 given colored semantic map, the diffusion model first generates satellite map, followed by Sat3DGen generating 3D assets based on the satellite image input. The results indicate that the generated 3D assets maintain spatial consistency with the semantic positions in the input semantic map. This application of generating 3D assets from semantic maps can be ad advantageous in spatial planning and game modeling, underscoring the significance of our work.
C IMPLEMENTATION DETAILS
Perspective sampling. During training, we randomly obtain perspective images from panorama street view images. We set the pitch range to [-30, 30] and fix the roll at O. The yaw is selected within the range [-79, 179], and the field of view (FOV) randomly chosen from [90, 105, 120]; the render size is 256 x 256.
Volume Rendering.
The triplane dimension is set to 32, and there are 96 samples per ray.
Other Hyperparameters. In reconstruction of the panorama and satellite view perspectives, as well as the sections of GAN loss and opacity loss, we maintain the same weight settings as Sat2Denstty++ For the newly introduced Gravity-based Density Variation Loss, we assign a weight of 3.5. As for the Satellite View Depth Regularization based on Midas loss, we set the weight to 0.1, acknowledging that the pseudo-labels for relative depth predicted by existing models may not be entirely accurate, hence opting for a smaller weight to mitigate potential adverse effects on the model.
Regarding the perspective view image reconstruction loss, we assign weight that half of the panorama view reconstruction loss. Conversely, the weight for the perspective view GAN loss is kept consistent with the panorama view GAN loss weight. This approach is due to the significant detail present in perspective images and the substantial difference between input satellite views and output perspective views. Given that the satellite input offers limited effective information for such
https://mapspaatfrmm..m ccm/
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Input semantic map
Synthesized sat.
Generated 3D by ours
Figure 11:
Given a colored semantic map, our model can generate 3D mesh through a pipeline that first converts the semantic map satellite map and then transforms the satellite map into 3D assets.
detailed perspective views, aim to prioritize realism and image quality in generating perspective images (as constrained by GAN loss) rather than achieving visual consistency with the ground truth images (which is the target of the reconstruction loss constraint). The full code will be released after acceptance.
D GROUND TRUTH DSM PREPARATION FOR VIGOR-OOD
Quantitative evaluation of 3D geometry requires accurate ground truth Digital Surface Models (DSMs). However, high-quality, publicly available LiDAR-derived DSM data is scarce and typically limited to a few cities. We were fortunate that our out-of-distribution (OOD) test set, VIGOR-OOD. based on the city of Seattle, for which were able to obtain corresponding high-precision DSM dataset. This section details the entire pipeline for processing and aligning this raw data to create the ground truth for our geometric evaluation.
D.1 DATA SOURCE AND JUSTIFICATION
The ground truth DSMs were derived from the King County West 2021 dataset, which is publicly available through the Washington State Department Natural Resources (DNR) Lidar Portal As shown in the data's is official report, this consists of Quality Level (QL1) LiDAR data collected in the spring and summer of 2021. We specifically chose this data as its acquisition timeline closely matches the period when the VIGOR dataset (Zhu et al. 2021 was being created.
selected six large GeoTIFF tiles from this collection that collectively cover the geographical extent of the VIGOR-OOD test images in Seattle, as visualized in Fig. 12 The official metadata
https://1idarpprrtt .drr gov/
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confirms the high quality of this source data, reporting positional emrors of less than 5.6 with 95% confidence, which provides a reliable basis for our geometric evaluation.
D.2 METHODOLOGY FOR SATELLITE-DE DSM ALIGNMENT
A significant technical challenge lies in aligning the raw DSM GeF files with the individual satellite images from the VIGOR-OOS test set. The datasets use different Coordinate Reference Systems (CRS), resolutions, and are not spatially aligned. We developed robust processing pipeline to address this, which is outlined Algorithm
The key to our approach is leveraging the metadata provided by the VIGOR dataset, which, fortunately, includes the precise WGS84 latitude/logittude coordinates and the Google Maps zoom level for each satellite image. This allows to first estimate the geographic bounding box of each image. We then reproject the corresponding section of the high-resolution DSM onto the exact pixel grid of the satellite image, ensuring perfect alignment. The main steps detailed below.
Algorithm DSM Ground Truth Preparation Pipeline
1: Input: Set of (GGOR-OOL satellite images I, set of raw DSM GeoTIFF tiles T.
2: Output: A ground truth DSM array D; for each valid input image I,.
3: Initialize an in- in-memory spatial index dim_inddex for all tiles in T for fast lookups.
4: for each satellite image I, do
5: (Step I: Estimate satellite image's geographic footprint
6: Parse center latitude/longitude and zoom level from I;'s filename.
7: Estimate the WGS84 bounding box target bounds for It
8: (Step 2: Find and Reproject Overlapping DSM Data)
9:
Find all candidate tiles candi date_ti ee from dam_i ndex that spatially overlap with target .buund..
if candidate.tile is empty then continue (No DSM coverage for this image] end if
11:
13:
Initialize best_dss _ss.cccsmeee to 'null' and max coverage to 0. for each tile T, in candidate t lises do
15:
Create an empty destination array temp_dam with the same dimensions I, Reproject the data from T, onto temp-dam using bilinear resampling. if coverage of temp_dam max.coverage then
16:
17:
18: best +- temp max max.coverage + coverage.
19: end if
20:
end for
21:
(Step Post-processing and Quality Control) if brses_dsmm not 'null' then
22:
Convert elevation values in best_dsm from feet meters.
24:
Calculate the percentage of NaN pixels nan percent in best dam. if nan percent S 5.0% then
26:
Save best_dsm as the final ground truth D.. else
28:
Discard this pair due to insufficient DSM coverage.
29: end if
30:
end if
31:
end for
This automated pipeline ensures that for every test image, we generate an aligned, unit-corrected. and quality-controlly DSM that can be used for direct, pixel-wise comparison in our geometric accuracy evaluation. Fig. shows visualizations of several paired satellite images and their corresponding DSM data.
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Figure 12: The collected DSM data in Seattle City.
Table 4: Ablation on variation regularization. "No variation loss" means starting from the base (Full model w/o perspective training) and removing Lyrae- "w. TV loss" replaces Ggry with total variation (TV) loss Chan et al. (2022). The remaining rows use Egrvv with different our choice c=1.0 (Ours) performs overall. Lower better.
Method
FID
KIDx 1004
No variation loss
25.90
19.0
W. TV loss
24.83
20.1
grav (e=0)
24.52
18.7
Lggav (e=0.01)
22.61
17.2
grrav (c=0.1)
22.63
16.2
Egrav (e=0.5)
21.94
17.9
grvv (e=1.0)
21.60
16.2
Lgrav (c=5.0)
21.74
18.5
gra 10.0)
21.66
17.5
E MORE ABLATIONS
E. VARIATION REGULARIZATION ABLATION.
As shown Table removing variation regularization gives the worst results (FID 25.90, KID 19.0). Replacing it with TV loss slightly lowers FID (24.83) but hurts KID (20.1). indicating oversmoothing and weak structural guidance. Our Cgrav outperforms both, with e 1.0 yielding the best metrics (FID 21.60 60, KID 16.2). Setting 0 degrades performance because it fails to accommodate genuine voids (c.g., gaps beneath tree canopies outside the trunk), leading to over- wer-penalizzaton and smoothing.
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F DATASET PREPARATION AND DETAILS
VIGOR. VIGOR (Zhu et al. 2021 contains four cities and, for each satellite image, multiple associated street-view panoramas with known relative poses. The satellite zoom level is 20. We train in Chicago, New York, and San Francisco, and use Seattle as an unseen-city OOD test set. We use 78, 188 satellite- panorama pairs for training and 11,875 pairs for metric evaluation. In principle, additional cities could be incorporated to further scale training.
Discussion on other Datasets. CVACT (Shi al. 2022 and CVUSA (Zhai et al. 2017) are widely used for satellite-to-stree image synthesis and localization. However, they are unsuitable for our 31) generation task due to two main reasons.
First, their data structure provides insufficient geometric supervision. Each satellite tile in these datasets is paired with single ground-level image at the tile center, without multi-view coverage. This data design is incompatible with our method, which relies on panoramas captured at spatially distinct positions to learn consistent street-level 3D. With only a centered street view, the supervision too sparse to learn reliable geometry, rendering our optimization components ineffective. Consequently, we do not use CVACT or CVUSA for training. In practice, VIGOR- datasets can be constructed for any geographic region where street-level panoramic imagery is available (e.g.. via Google Maps) by pairing a single satellite image with N panoramic views.
Second, these datasets unsuitable for a fair images in CVACT and CVUSA were captured by different satellites and at different zoom levels than those in VIGOR. This results in significant appearance shifts and differences in pixel-level spatial resolution. Evaluating our VIGOR-trained model on these datasets would conflate the test of geometric generalization with a test of resilience data source domain shift, which is beyond the scope of this work. We test on an unseen city from the same data source.
(OOD) evaluation. The satellite
Our Strategy for Generalization Evaluation. We explicitly test generalization using the VIGOR- OOD split, by training on three cities and testing on the unseen city of Seattle. This setup introduces significant domain gap in terms of urban layouts and architectural styles, providing strong signal of our model's robustness. Furthermore, the notion that VIGOR is purely "urban" is misconception. Like other datasets derived from vehicle-based captures, it covers a wide range of environments, including many less-dense, suburban areas, shown in qualitative results (c.g.. Figure 7). The primary generalization challenge is not urban vs. suburban density, but rather the architectural and environmental shifts between disparate geographic regions.
G LIMITATIONS
Our work faces challenges rooted in both data availability and model assumptions.
Pose Inaccuracy. A primary limitation is the lack of precise pose data. We treat satellite images as ideal orthogonal projections, which is not always the case. Moreover, the panoramas lack authentic intrinsic/extrinsic parameters; we only have GPS data. Our model assumes panoramas are captured perpendicular to the ground, ignoring roll angles from terrain road banking. Accessing predicting accurate poses is a valuable direction for future work.
Geometric and Terrain Assumptions. Our model's performance is also constrained by its underlying assumptions. By its generative nature, it struggles with atypical architectures that are rare in the training data, as we lack explicit 3D ground-truth shapes for supervision. Furthermore, our framework assumes locally flat ground plane and does not model significant terrain variations like hills, which are difficult to infer from sparse imagery alone. Future work could address this by incorporating multi-modal data, such as terrain maps.
Evaluation Metrics. Finally, while metrics like multi-view photometric consistency or temporal flicker are powerful, they are not applicable to the VIGOR dataset. VIGOR's sparse, non-sequential collection of still images does not support the evaluation of temporal stability or dense view consis- tency.
23


Reviews:
[{"review_id": "dYVCodlM9H", "reviewer": "Reviewer_zCTK", "summary": "Sat3DGen proposes a feed-forward framework to generate street-level 3D scenes from a single satellite image, addressing the trade-off between semantics and geometry in existing methods. Built on a tri-plane NeRF backbone, it introduces three key components: a gravity-based density variation loss to suppress floating artifacts and voids, spatial tokens to stabilize boundary geometry, and satellite-view depth regularization to resolve rooftop ambiguity. Additionally, it strengthens supervision by jointly training on panoramas and their projected perspective views. Experiments on VIGOR-OOD demonstrate superior performance and support downstream applications like DSM estimation and large-area mesh generation. The work’s core strength lies in targeted geometric optimizations, though it relies on combinations of existing techniques.", "questions": "- What was the motivation for selecting DINOv3 as the satellite encoder? Have you conducted ablation studies comparing it with other encoders in terms of performance, computational cost, or token quality? If DINOv3 is replaced with a simpler encoder, how much performance degradation would occur? \n- Could you supplement quantitative 3D metrics to objectively validate geometric improvements?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Clear framework design**: The pipeline (satellite encoding → tri-plane lifting → illumination-adaptive rendering) is logically coherent, with sufficient details for reproducibility.\n- **effective geometric optimizations**: The gravity-based loss directly addresses volumetric field voids and floaters, which is a critical problem in scene-level NeRF-based generation, with clear qualitative and quantitative improvements.\n- **Strong practical value**: Supports multiple downstream tasks (e.g. DSM estimation, multi-camera video generation, semantic-map-to-3D) without extra supervision, enhancing real-world applicability.\n- **Comprehensive experimental validation**: Includes ablation studies for key components, cross-method comparisons, and qualitative/quantitative evaluations, ensuring result credibility.", "weaknesses": "- **relying on existing method combinations**: The core framework (tri-plane NeRF + 2D supervision) is borrowed from prior works (e.g., Sat2Density++). No breakthrough in methodology or framework design is presented.\n- **Unclear motivation and lack of ablation for DINOv3 encoder**: The paper uses a frozen DINOv3 ViT encoder for satellite tokenization but provides no justification for choosing DINOv3 over other encoders. There is no ablation to verify whether DINOv3 contributes to performance gains, or if simpler encoders could achieve similar results, or if the model can generalize to out-of-distribution scenarios.\n- **Lack of quantitative 3D geometric evaluation**: All geometric assessments are qualitative (mesh visualizations), with no quantitative metrics for 3D quality. This makes it hard to rigorously validate the claimed \"superior geometric quality\" compared to baselines like Sat2Density++.", "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": "OC7AOlusrN", "reviewer": "Reviewer_udS7", "summary": "This paper proposes a method for efficiently generating high-quality street-level 3D scenes from a single satellite image. Specifically, this method designs an end-to-end framework from satellite image to 3D. This framework first uses DINO v3 to encode the features of the input satellite image, and then decodes the encoded features into a Triplane feature field. Subsequently, volume rendering of this feature field is performed using an MLP to reconstruct the 3D scene. To effectively address potential issues such as edge artifacts, geometric distortions, and roof errors during scene reconstruction, this paper introduces various optimization strategies, including physical constraints and depth constraints. Experimental results show that, compared to existing methods such as Sat2Density++, Sat3DGen can generate street-level 3D scenes with more accurate geometric information and more detailed rendering results.", "questions": "1. Regarding the comparison of Figure 4(c), currently only Sat2Density is compared, excluding Sat2Density++. Given that Sat2Density++ clearly demonstrates a quality improvement over Sat2Density in its original paper, could you explain why this baseline was not included? Furthermore, since this paper achieves higher quantitative metrics, it is recommended to supplement the comparison with Sat2Density++ through parallel rendering, providing qualitative results under the same settings, to more comprehensively evaluate the differences between the two in terms of geometric consistency and texture fidelity.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Well-written: The paper has a clear organizational structure, is well-written, and has a logical flow.\n2. Targeted improvements to geometric stability: Gravity-based Density Variation Loss is proposed, which modulates the volume density along the direction of gravity, significantly alleviating the common \"floating layers/holes\" problem, making the reconstruction more coherent and more renderable.\n3. Simultaneous improvement in rendering quality and cross-view consistency: Combining depth prior with multi-view supervision of panorama/perspective, covering a wider field of view and strengthening geometric constraints, resulting in more stable reconstruction and higher rendering fidelity, especially more reliable in details such as boundaries and roofs.", "weaknesses": "1. The contribution is unclear: Based on my understanding of this article, its basic framework is quite similar to Sat2Density++, with the core contribution being the introduction of depth conditions as training constraints. The authors need to better clarify the differences between this and the Sat2Density++ framework.\n\n2. The experimental validation is insufficient. The authors only conducted experiments on VIGOR-OOD. To my knowledge, VIGOR-OOD is mainly designed for urban scene acquisition. For scenes that are more suburban or rural (e.g.,CVACT[1]), will the authors' method still have significant robustness?\n\n3. Lack of evaluation baseline: As I understand it, based on the contribution of the paper, the focus is on optimizing the 3D geometry generated by the baseline method. Therefore, this paper should add a comparison with general Image-3D methods [2, 3].\n\n[1] Liu, Liu, and Hongdong Li. \"Lending orientation to neural networks for cross-view geo-localization.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.\n[2] Xiang, Jianfeng, et al. \"Structured 3d latents for scalable and versatile 3d generation.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n[3] Hunyuan3D, Team, et al. \"Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material.\" arXiv preprint arXiv:2506.15442 (2025).", "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": "S9cUpoWbjV", "reviewer": "Reviewer_Quvg", "summary": "This paper proposes Sat3DGen, a novel framework for generating high-fidelity, street-level 3D reconstructions from a single satellite image. The method introduces three geometry-focused contributions: (1) Gravity-based density variation loss, (2) Spatial token padding, (3) Monocular satellite-view depth regularization. It demonstrates substantial improvements over prior state-of-the-art approaches like Sat2Density++ and Sat2Scene, particularly in scene-level 3D geometry consistency, semantic fidelity, and rendered view realism, across both qualitative and quantitative benchmarks (e.g., FID, DINO similarity).", "questions": "1. Are the generated meshes watertight and suitable for downstream simulation tasks (such as physics simulation or driving simulation)?\n2. How robust is the model to varying lighting conditions or the absence of panorama-derived illumination codes, and does it offer controllable rendering for different times of day?\n3. Does the method generalize well to images outside the VIGOR dataset, including rural or non-urban areas?\n4. It would be nice to see more analysis on the failure cases and the robustness of the method (e.g., non-planar surfaces, complex geometry, occlusion, etc.).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clearly motivated and well-structured paper with substantial methodological contributions.\n- The paper achieves strong improvements in empirical results (e.g., FID drops from 40.8 to 19.2) and demonstrates practical utility in applications such as DSM estimation and multi-view video synthesis.\n- Effective ablation studies and transparent discussion of limitations are provided.", "weaknesses": "- The paper lacks evaluation against metric 3D ground truth (e.g., DSM or city-scale LiDAR) and could be strengthened with controlled experiments on public datasets and analysis of DSM or mesh error.\n- There is insufficient discussion of major failure modes (such as occlusions or challenging geometry), and additional metrics beyond FID/LPIPS—like multi-view photometric consistency or temporal flicker—would offer a fuller assessment of 3D and video realism.\n- Robustness and generalization should be further explored, including evaluation on non-VIGOR data, handling of noisy or missing illumination inputs, and clarifications on methodological details and reproducibility (e.g., pretrained model release, citation updates, and clear diagram separation).", "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": "dHG4s488fu", "reviewer": "Reviewer_tZHZ", "summary": "The paper introduces Sat3DGen, a framework designed to generate comprehensive street-level 3D scenes from a single satellite image. The method incorporates several techniques — a gravity-based density variation loss, spatial token padding, and a monocular relative-depth prior — to enhance the performance of Sat2Density++. However, the overall architecture remains almost identical to Sat2Density, leading to concerns about the lack of novelty.", "questions": "The problem corresponds one-to-one with the content of the weakness:\n1. How does the use of DINO-v3 affect inference speed and memory consumption? Are the improvements mainly due to the stronger feature extractor?\n2. Could the authors provide clearer mathematical formulations and theoretical justification for the Gravity-based Density Variation Loss?\n3. How accurate is Depth Anything v2 on satellite imagery, and how sensitive is the model to potential depth estimation errors? Why not integrate depth directly into the model rather than using it as a loss? Does the spatial gradient supervision risk over-smoothing sharp depth transitions?\n4. Why is Sat2Density used as the baseline in Figure 4(c) instead of Sat2Density++? Why are quantitative comparisons with ControlNet, ControlS2S, or Canonical Image-to-3D missing?\n5. How do the authors justify the novelty of Sat3DGen given its high similarity to Sat2Density++?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. The authors tackle a meaningful and challenging task: generating realistic ground-level 3D scenes from a single satellite image.\n2. The proposed gravity-based density variation loss, spatial token padding, and monocular relative-depth prior improve upon the previous Sat2Density++ framework.", "weaknesses": "**1. The use of DINO-v3**\n\nThe model employs DINO-v3, which is computationally expensive. It is unclear how the inference speed and GPU memory consumption are affected. Moreover, it remains uncertain whether the performance gains primarily come from DINO-v3, rather than the proposed method itself.\n\n**2. Unclear explanation of the Gravity-based Density Variation Loss (Lines 241–254)**\n\n* The mathematical formulation is ambiguous. Is x defined in Line 197 as a 3D point?\n* Why does δx (a scaled 3D point) represent “along gravity”?\n* The statement “lower-altitude points usually have density that is no smaller than higher-altitude points” seems empirical — is there any theoretical justification?\n\n**3. Depth estimation and supervision issues (Line 258–265)**\n* How accurate is the depth estimated by Depth Anything v2 when applied to satellite imagery?\n* What happens if the estimated depth is inaccurate?\n* Why is depth only used as a loss term instead of being fused into the network representation?\n* The supervision on spatial gradients (Line 265) might oversmooth regions that should exhibit sharp depth changes (e.g., building-ground boundaries).\n\n**4. Inconsistent baseline comparison**\n\n* In Figure 4(c), the comparison is made against Sat2Density instead of Sat2Density++. Why not compare with the most recent and stronger baseline?\n* The paper lacks quantitative comparisons with related methods such as ControlNet, ControlS2S, or Canonical Image-to-3D.\n\n**5. Limited novelty**\n\nThe overall architecture is nearly identical to Sat2Density++, with improvements mainly stemming from new loss terms and a stronger backbone.", "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)