Pith. sign in

REVIEW 3 major objections 6 minor 1 cited by

Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Performing multi-view matching directly in the spherical domain is the key to generalizable 3D Gaussian splatting for wide-baseline panoramic images.

desk verdict The spherical cost volume is a real contribution with strong internal ablations, but the headline lead over MVSplat is confounded by asymmetric depth supervision and needs an ablation. read the letter →

arxiv 2412.06250 v1 pith:262K2NAY submitted 2024-12-09 cs.CV cs.GR

Zheng Chen , Chenming Wu , Zhelun Shen , Chen Zhao , Weicai Ye , Haocheng Feng , Errui Ding , Song-Hai Zhang This is my paper · ORCID
classification cs.CVcs.GR
keywords generalizable3DGaussiansplattingpanoramicnovelviewsynthesiswide-baseline360°imagessphericalcostvolumesweepalgorithmbi-projectionencodercross-viewattentionequirectangularandcubemapprojection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Splatter-360 is a feed-forward 3D Gaussian splatting model that takes two (or three) wide-baseline 360° panoramas and renders novel panoramic views in real time. Its central claim is that multi-view matching should be done directly on the spherical image domain: a spherical sweep algorithm samples depth candidates along rays and builds a spherical cost volume, instead of flattening each panorama into cubemaps and running plane-sweep matching, which can sample points behind the source camera. Combined with a bi-projection encoder that fuses equirectangular and cubemap features under cross-view attention, and with monocular depth features injected as geometry priors, the network predicts pixel-aligned Gaussian primitives on the equirectangular grid. On HM3D and Replica, the paper reports PSNR of 28.29 and 29.89 dB, beating the strongest perspective baseline (MVSplat trained on panoramas) by 1.11 and 1.49 dB and the panoramic NeRF baseline PanoGRF by 2.66 and 1.97 dB, with better depth estimates. The reason this matters is that wide-baseline panoramic captures are cheap to acquire but hard to render into; if the claim holds, real-time high-quality view synthesis from sparse 360° images is within reach for VR and simulation pipelines.

What carries the argument

The spherical cost volume is the central object. For each reference pixel, depth candidates $r_m$ are sampled logarithmically between $r_{\text{near}}$ and $r_{\text{far}}$, converted from equirectangular coordinates $(\theta,\phi)$ to Cartesian camera coordinates, back-projected into each source view with the relative pose, and compared by feature dot product; softmax over the $D=128$ candidates yields a spherical depth estimate that directly determines Gaussian centers. This is what lets the network avoid the behind-camera ($z<0$) sampling problem of cubemap plane sweeps. Around it sit the bi-projection encoder, which fuses equirectangular and cube-map features with cross-view attention and monocular depth features, and a refinement U-Net that corrects the cost volume.

What would settle it

Retrain MVSplat (or remove depth supervision from Splatter-360) with the supervision held fixed: give MVSplat the same depth loss and the same monocular depth features while keeping its cubemap plane-sweep cost volume, and compare PSNR and depth metrics on HM3D. If the margin over MVSplat collapses, the paper's evidence does not establish that spherical-domain matching is the cause.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that a generalizable 3D Gaussian splatting pipeline can work end-to-end on wide-baseline panoramas if it matches features in spherical coordinates. The central comparison is that Splatter-360 outperforms the state-of-the-art perspective generalizable splatting methods MVSplat, DepthSplat, and HiSplat, as well as the panoramic radiance-field method PanoGRF, on HM3D and Replica across PSNR, SSIM, LPIPS, and estimated-depth metrics. The authors attribute the gain to three design choices: the spherical cost volume from the spherical sweep, the 3D-aware bi-projection encoder with cross-view attention, and the injection of monocular depth features. Ablations show that removing the spherical cost volume costs 5.27 dB on Replica and 2.26 dB on HM3D, while removing cross-view attention costs about 0.90 dB on Replica and 0.57 dB on HM3D, which supports the claim that spherical-domain matching carries most of the improvement.

Load-bearing premise

The load-bearing premise is that the comparison with MVSplat is fair enough to attribute the gains to the spherical cost volume, even though Splatter-360 is trained with ground-truth depth supervision (loss weight 0.1) and pretrained monocular depth features that MVSplat does not receive.

Editorial extensions

If this is right

  • If the central claim is right, feed-forward panoramic novel view synthesis from two wide-baseline inputs becomes a real-time rasterization problem rather than a slow NeRF-style optimization.
  • The reported depth gains (Abs Rel 0.063 vs 0.088 on Replica and 0.076 vs 0.094 on HM3D, with $\delta<1.25$ above 94%) imply that spherical-domain matching improves geometry estimation from sparse 360° views.
  • The method generalizes across datasets: after training on HM3D it keeps a clear margin on held-out Replica scenes with no per-scene optimization.
  • Ablation numbers imply the spherical cost volume, not the decoder, is the main source of the quality jump, so future panoramic splatting designs should focus on matching strategy.
  • The stated limitations mean the approach still assumes known poses, cannot generate unseen content, and is demonstrated only on indoor scenes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not isolate the supervision confound; a fair test would retrain MVSplat with the same depth loss and monocular features to see whether the spherical cost volume is the real cause.
  • Because matching runs at 1/8 resolution with 128 depth bins, the same spherical sweep should extend to more input views or higher resolutions; the paper only reports two-view and three-view results.
  • The behind-camera problem it identifies is not limited to panoramas, so the spherical sweep idea could transfer to fisheye or wide field-of-view perspective rigs.
  • The pose requirement is the main practical gap; a pose-refinement head trained jointly with the spherical cost volume would be the natural step toward pose-free 360° reconstruction.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes Splatter-360, an end-to-end generalizable 3D Gaussian splatting framework for novel view synthesis from wide-baseline 360-degree panoramic images. The method constructs a spherical cost volume via a spherical sweep algorithm, uses a bi-projection encoder that combines equirectangular and cubemap features with pretrained monocular depth features, incorporates cross-view attention, and predicts pixel-aligned Gaussians. The authors report evaluations on HM3D and Replica against PanoGRF, MVSplat, DepthSplat, and HiSplat, claiming consistent improvements in synthesis quality and depth estimation. The supplementary material contains additional ablations, including an ablation of a depth refinement U-Net that is not described in the main text.

Significance. If the central comparisons hold, this is a strong empirical contribution. The spherical cost volume is a natural adaptation of plane-sweep matching to panoramic imagery and avoids behind-camera sampling artifacts, and the prospect of real-time rendering from sparse panoramic captures is practically relevant. The paper releases code and trained models, and the ablations (e.g., Supplementary Table 6) show that the refinement components matter. However, the current evidence for the central causal claim, namely that spherical-domain matching drives the gains, is weakened by a supervision confound in the comparison with MVSplat and by an internal inconsistency between the main results and the ablation baseline.

major comments (3)
  1. [Table 1 vs Table 3 and Supplementary Table 6] The full model in the ablations does not reproduce the headline model. Table 1 reports Splatter-360 at 28.293 dB PSNR on HM3D and 29.888 dB on Replica, while the 'Full' rows of Table 3 and Supplementary Table 6 report 27.487 dB and 29.121 dB on the same datasets, differences of 0.806 dB and 0.767 dB. Because all ablation deltas are computed against this different baseline, the module-contribution claims in Section 4.5 are not quantitatively supported as stated. Please explain the discrepancy (e.g., different number of GPUs, batch size, or training budget) or retrain the ablations to match the model used in Table 1.
  2. [Sec. 4.1, Sec. 3.1 (Eq. 1), Table 2] The depth and geometry comparison with MVSplat is confounded by asymmetric training signals. Splatter-360 is trained with an explicit depth loss of weight 0.1 (Sec. 4.1) and consumes monocular depth features from a pretrained DepthAnythingV2 (Sec. 3.1, Eq. 1), while MVSplat is trained with an RGB loss only. The depth-metric lead in Table 2 and the qualitative depth advantage in Fig. 4 could therefore be caused by the additional supervision or monocular prior rather than by the proposed spherical cost volume or bi-projection encoder. The '× Mono Feat.' ablation in Table 3 removes the monocular features but retains the depth loss, so it does not isolate the cause. Please ablate the depth loss, or train MVSplat with the same depth supervision, and report the resulting PSNR and depth metrics.
  3. [Sec. 3.2 and Supplementary Sec. 7.3/Table 6] The main-text method description omits the depth refinement U-Net. Section 3.2 describes only the spherical cost volume and its refinement U-Net (Eq. 7), yet Fig. 1 and Supplementary Sec. 7.3 state that a separate depth refinement U-Net exists, and Supplementary Table 6 shows that removing it costs about 0.7 dB PSNR on both datasets. The architecture evaluated in Tables 1-4 is therefore not fully described in the main text. Please include this component in Section 3.2 and in the ablation study, or explicitly refer the reader to the supplementary description at the point where the refinement U-Net is introduced.
minor comments (6)
  1. [Sec. 4.2] The sentence 'To proveide a fair comparisions' contains typos; it should read 'To provide a fair comparison'.
  2. [Sec. 4.5] 'Corss-view attention' should be 'Cross-view attention'.
  3. [Fig. 4] The caption says the depth comparison is with PanoGRF, but Section 4.4 and the surrounding text describe a comparison with MVSplat; please align the caption with the method actually shown.
  4. [Supplementary Sec. 7.2] The near/far settings are described confusingly: the text first says near=0.5 and far=10 for the perspective baselines, then says near=0.1 was also tried and gave worse results; please clarify which configuration produced the numbers in Table 1.
  5. [Abstract and Conclusion] The paper claims real-time rendering but reports no runtime or throughput measurement anywhere; please add a quantitative runtime comparison (e.g., rendering fps on a specified GPU) to substantiate this claim.
  6. [Sec. 4.3/Supplementary] For the models trained by the authors (MVSplat† and Splatter-360†), please report the number of training iterations, batch size, learning rate, and the exact train/validation split so that the comparisons are reproducible.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the core derivation is self-contained and evaluated against held-out ground truth; only minor non-load-bearing self-citations are present.

full rationale

Splatter-360's load-bearing derivation is self-contained. The spherical cost volume (Eqs. 3-8), bi-projection encoder (Eqs. 1-2), and pixel-aligned Gaussian decoding are standard feed-forward modules trained end-to-end on HM3D and tested on held-out HM3D and Replica targets, so the headline PSNR/SSIM/LPIPS results do not reduce to the training inputs. The spherical projection formula is cited to DiffPano [48], whose author list overlaps with this paper, but Eqs. 3-4 are standard spherical-to-Cartesian geometry and are not load-bearing in the sense of importing an unverified uniqueness claim or ansatz. PanoGRF [8] is also a self-citation but appears only as a baseline, not as a premise of the method. The depth supervision (weight 0.1) and pretrained DepthAnythingV2 monocular features are additional training signals; this makes the comparison with MVSplat (trained with RGB loss only) a fairness or confound concern, but it is not circularity because the depth and color predictions are evaluated against ground-truth test targets rather than being fitted to those targets. The limitations section discloses pose dependence, lack of generative capability, and indoor-scene restriction, none of which indicates a circular step. No equation is defined in terms of the quantity it is claimed to predict, no fitted parameter is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method does not introduce new physical entities or theoretical constructs. Its central claim rests on standard geometry, a hand-picked depth sampling range, loss weights, and a domain assumption that rendered indoor datasets are representative. The main dependence beyond prior literature is the spherical cost volume and the bi-projection encoder, whose efficacy is empirically claimed but not fully isolated from the depth supervision and monocular depth prior.

free parameters (4)
  • Depth sampling range [r_near, r_far] = [0.1, 10] m
    Chosen by hand to match indoor scenes (Sec. 7.2); the central claim depends on this range covering all relevant scene depths, but no sensitivity analysis is provided.
  • Number of depth candidates D = 128
    Set consistently with MVSplat (Sec. 7.3); affects the discretization of the spherical cost volume and the accuracy of depth estimates.
  • Loss weights = RGB=1.0, LPIPS=0.05, depth=0.1
    Chosen in Sec. 4.1; the balance between depth and RGB losses directly affects the learned geometry and rendering, and no sensitivity is reported.
  • Feature matching resolution = 1/8 H x 1/8 W
    Set in Sec. 7.2 because of GPU memory limits; downsampling may lose high-frequency geometric detail, yet the paper does not analyze this effect.
assumptions (4)
  • standard math Spherical projection formulas (Eq. 3 and Eq. 4) and 3DGS rendering equations (Supplementary Sec. 8) are correct and standard.
    The method relies on these geometric relationships without proof; they are standard in the field.
  • domain assumption HM3D and Replica rendered with AI-Habitat provide realistic and representative wide-baseline panoramic testbeds.
    The evaluation in Sec. 4.2 and dataset details in Sec. 7.1 assume rendered indoor meshes approximate real captures; the paper notes the method is limited to indoor scenes.
  • domain assumption Pre-trained models on Re10K (HiSplat, DepthSplat, and MVSplat without retraining) are valid zero-shot baselines for evaluating panoramic wide-baseline performance.
    The paper evaluates these models on cube-map inputs with near=0.5 and far=10 (Sec. 7.2); this cross-domain comparison may disadvantage them, and the paper does not justify its fairness.
  • ad hoc to paper Depth candidates are sampled in logarithmic space between 0.1 and 10 meters, which assumes all relevant scene geometry lies within this range.
    The range is chosen for the indoor HM3D dataset (Sec. 7.2); if scenes contain geometry outside this range, the estimated depth and rendered views will be incorrect, but the paper does not test this boundary.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images." pith.science (2026). https://pith.science/paper/262K2NAY

@misc{pith2026241206250,
  author       = {Pith},
  title        = {Pith review of: Splatter-360: Generalizable 360$^\circ$ Gaussian Splatting for Wide-baseline Panoramic Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/262K2NAY}},
  note         = {Machine review of arXiv:2412.06250}
}
abstract

Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs. However, synthesizing novel views from these panoramic images in real time remains a significant challenge, especially due to panoramic imagery's high resolution and inherent distortions. Although existing 3D Gaussian splatting (3DGS) methods can produce photo-realistic views under narrow baselines, they often overfit the training views when dealing with wide-baseline panoramic images due to the difficulty in learning precise geometry from sparse 360$^{\circ}$ views. This paper presents \textit{Splatter-360}, a novel end-to-end generalizable 3DGS framework designed to handle wide-baseline panoramic images. Unlike previous approaches, \textit{Splatter-360} performs multi-view matching directly in the spherical domain by constructing a spherical cost volume through a spherical sweep algorithm, enhancing the network's depth perception and geometry estimation. Additionally, we introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images and integrate cross-view attention to improve feature interactions across multiple viewpoints. This enables robust 3D-aware feature representations and real-time rendering capabilities. Experimental results on the HM3D~\cite{hm3d} and Replica~\cite{replica} demonstrate that \textit{Splatter-360} significantly outperforms state-of-the-art NeRF and 3DGS methods (e.g., PanoGRF, MVSplat, DepthSplat, and HiSplat) in both synthesis quality and generalization performance for wide-baseline panoramic images. Code and trained models are available at \url{https://3d-aigc.github.io/Splatter-360/}.

Figures

Figures reproduced from arXiv: 2412.06250 by the authors.

Figure 1
Figure 1. Our Splatter-360 processes 360° panoramic images using a bi-projection encoder that extracts features from both equirectangular [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Qualitative comparison between our Splatter-360 and PanoGRF, MVSplat on the Replica dataset. Regions with notable differ [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison between our Splatter-360 and PanoGRF, MVSplat on the HM3D dataset. Regions with notable differences [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Novel view depth comparison between Splatter-360 and PanoGRF on the Replica dataset. “Pano.” denotes panoramic view and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A feed-forward Gaussian splatting system that synthesizes novel 4K panoramic views from two wide-baseline inputs, using Fibonacci-lattice Gaussians and memory-efficient training.

Reference graph

Works this paper leans on

57 extracted references · 24 canonical work pages · cited by 1 Pith paper

  1. [1]

    Matryodshka: Real-time 6dof video view synthesis using multi-sphere images

    Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, and James Tompkin. Matryodshka: Real-time 6dof video view synthesis using multi-sphere images. In European Conference on Computer Vision, pages 441–459. Springer, 2020. 2

    2020

  2. [2]

    360-gs: Layout-guided panoramic gaussian splatting for indoor roaming

    Jiayang Bai, Letian Huang, Jie Guo, Wen Gong, Yuanqi Li, and Yanwen Guo. 360-gs: Layout-guided panoramic gaussian splatting for indoor roaming. arXiv preprint arXiv:2402.00763, 2024. 2

    arXiv 2024

  3. [3]

    Depth esti- mation from indoor panoramas with neural scene representa- tion

    Wenjie Chang, Yueyi Zhang, and Zhiwei Xiong. Depth esti- mation from indoor panoramas with neural scene representa- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 899–908, 2023. 2

    2023

  4. [4]

    pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction

    David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19457–19467, 2024. 2, 5

    work page 2024

  5. [5]

    Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images

    Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images. CoRR, abs/2403.14627, 2024. 2, 3, 4, 5, 8

    arXiv 2024

  6. [6]

    Mvsplat360: Feed-forward 360 scene synthesis from sparse views

    Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. Mvsplat360: Feed-forward 360 scene synthesis from sparse views. In Ad- vances in Neural Information Processing Systems (NeurIPS),

    work page

  7. [7]

    Text2light: Zero-shot text-driven hdr panorama generation

    Zhaoxi Chen, Guangcong Wang, and Ziwei Liu. Text2light: Zero-shot text-driven hdr panorama generation. ACM Trans- actions on Graphics (TOG), 41(6):1–16, 2022. 3

    2022

  8. [8]

    Panogrf: generalizable spherical radiance fields for wide-baseline panoramas

    Zheng Chen, Yan-Pei Cao, Yuan-Chen Guo, Chen Wang, Ying Shan, and Song-Hai Zhang. Panogrf: generalizable spherical radiance fields for wide-baseline panoramas. Ad- vances in Neural Information Processing Systems, 36:6961– 6985, 2023. 1, 3, 5, 8

    work page 2023

Show all 57 references
  1. [9]

    Depth-supervised nerf: Fewer views and faster train- ing for free

    Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ra- manan. Depth-supervised nerf: Fewer views and faster train- ing for free. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12882– 12891, 2022. 2

    2022

  2. [10]

    Learning to render novel views from wide-baseline stereo pairs

    Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitz- mann. Learning to render novel views from wide-baseline stereo pairs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4970– 4980, 2023. 2

    2023

  3. [11]

    Omni-nerf: neural radiance field from 360 image captures

    Kai Gu, Thomas Maugey, Sebastian Knorr, and Christine Guillemot. Omni-nerf: neural radiance field from 360 image captures. In 2022 IEEE International Conference on Multi- media and Expo (ICME), pages 1–6. IEEE, 2022. 2

    2022

  4. [12]

    Somsi: Spherical novel view synthesis with soft occlusion multi-sphere images

    Tewodros Habtegebrial, Christiano Gava, Marcel Rogge, Di- dier Stricker, and Varun Jampani. Somsi: Spherical novel view synthesis with soft occlusion multi-sphere images. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 15725–15734, 2022. 2

    2022

  5. [13]

    Lrm: Large reconstruction model for single image to 3d

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 2

    2023 arXiv

  6. [14]

    Squeeze-and-excitation net- works

    Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation net- works. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018. 4

    2018

  7. [15]

    360Roam: Real-Time Indoor Roaming Us- ing Geometry-Aware 360◦ Radiance Fields

    Huajian Huang, Yingshu Chen, Tianjian Zhang, and Sai- Kit Yeung. 360Roam: Real-Time Indoor Roaming Us- ing Geometry-Aware 360◦ Radiance Fields. arXiv preprint arXiv:2208.02705, 2022. 2

    2022 arXiv

  8. [16]

    Unifuse: Unidirectional fusion for 360 panorama depth estimation

    Hualie Jiang, Zhe Sheng, Siyu Zhu, Zilong Dong, and Rui Huang. Unifuse: Unidirectional fusion for 360 panorama depth estimation. IEEE Robotics and Automation Letters, 6 (2):1519–1526, 2021. 3, 4

    2021

  9. [17]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  10. [18]

    360fu- sionnerf: Panoramic neural radiance fields with joint guid- ance

    Shreyas Kulkarni, Peng Yin, and Sebastian Scherer. 360fu- sionnerf: Panoramic neural radiance fields with joint guid- ance. In 2023 IEEE/RSJ International Conference on Intel- ligent Robots and Systems (IROS), pages 7202–7209. IEEE,

    2023

  11. [19]

    Ggrt: Towards generalizable 3d gaussians without pose priors in real-time

    Hao Li, Yuanyuan Gao, Dingwen Zhang, Chenming Wu, Yalun Dai, Chen Zhao, Haocheng Feng, Errui Ding, Jing- dong Wang, and Junwei Han. Ggrt: Towards generalizable 3d gaussians without pose priors in real-time. arXiv preprint arXiv:2403.10147, 2024. 2

    2024 arXiv

  12. [20]

    Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model

    Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214, 2023. 2

    2023 arXiv

  13. [21]

    Scenedreamer360: Text-driven 3d-consistent scene generation with panoramic gaussian splatting

    Wenrui Li, Yapeng Mi, Fucheng Cai, Zhe Yang, Wangmeng Zuo, Xingtao Wang, and Xiaopeng Fan. Scenedreamer360: Text-driven 3d-consistent scene generation with panoramic gaussian splatting. arXiv preprint arXiv:2408.13711, 2024. 3

    2024 arXiv

  14. [22]

    Re- conx: Reconstruct any scene from sparse views with video diffusion model

    Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Re- conx: Reconstruct any scene from sparse views with video diffusion model. arXiv preprint arXiv:2408.16767, 2024. 3

    2024 arXiv

  15. [23]

    3dgs- enhancer: Enhancing unbounded 3d gaussian splatting with view-consistent 2d diffusion priors

    Xi Liu, Chaoyi Zhou, and Siyu Huang. 3dgs- enhancer: Enhancing unbounded 3d gaussian splatting with view-consistent 2d diffusion priors. arXiv preprint arXiv:2410.16266, 2024. 3

    2024 arXiv

  16. [24]

    Neural rays for occlusion-aware image-based render- ing

    Yuan Liu, Sida Peng, Lingjie Liu, Qianqian Wang, Peng Wang, Christian Theobalt, Xiaowei Zhou, and Wenping Wang. Neural rays for occlusion-aware image-based render- ing. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 7824–7833,

  17. [25]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021. 2 10

    2021

  18. [26]

    Fastscene: Text-driven fast 3d indoor scene generation via panoramic gaussian splat- ting

    Yikun Ma, Dandan Zhan, and Zhi Jin. Fastscene: Text-driven fast 3d indoor scene generation via panoramic gaussian splat- ting. arXiv preprint arXiv:2405.05768, 2024. 3

    2024 arXiv

  19. [27]

    Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai

    Santhosh K Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alex Clegg, John Turner, Eric Un- dersander, Wojciech Galuba, Andrew Westbury, Angel X Chang, et al. Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai. arXiv prepri...

    2021 arXiv

  20. [28]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2

    2022

  21. [29]

    Habitat: A Platform for Embodied AI Research

    Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. Habitat: A Platform for Embodied AI Research. In Proceedings of the IEEE/CVF International Conferen...

    2019

  22. [30]

    Simplere- con: 3d reconstruction without 3d convolutions

    Mohamed Sayed, John Gibson, Jamie Watson, Victor Prisacariu, Michael Firman, and Cl´ement Godard. Simplere- con: 3d reconstruction without 3d convolutions. InEuropean Conference on Computer Vision, pages 1–19. Springer, 2022. 1

    2022

  23. [31]

    Mvdream: Multi-view diffusion for 3d gen- eration

    Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d gen- eration. arXiv preprint arXiv:2308.16512, 2023. 2

    2023 arXiv

  24. [32]

    The replica dataset: A digital replica of indoor spaces

    Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, et al. The replica dataset: A digital replica of indoor spaces. arXiv preprint arXiv:1906.05797,

    1906 arXiv

  25. [33]

    Flash3d: Feed-forward gener- alisable 3d scene reconstruction from a single image

    Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, Jo ˜ao F Henriques, Christian Rup- precht, and Andrea Vedaldi. Flash3d: Feed-forward gener- alisable 3d scene reconstruction from a single image. arXiv preprint arXiv:2406.04343, 2024. 2

    2024 arXiv

  26. [34]

    Splatter image: Ultra-fast single-view 3d recon- struction

    Stanislaw Szymanowicz, Chrisitian Rupprecht, and Andrea Vedaldi. Splatter image: Ultra-fast single-view 3d recon- struction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10208– 10217, 2024. 2

    2024

  27. [35]

    Hisplat: Hierarchical 3d gaus- sian splatting for generalizable sparse-view reconstruction

    Shengji Tang, Weicai Ye, Peng Ye, Weihao Lin, Yang Zhou, Tao Chen, and Wanli Ouyang. Hisplat: Hierarchical 3d gaus- sian splatting for generalizable sparse-view reconstruction. arXiv preprint arXiv:2410.06245, 2024. 2, 5, 8

    2024 arXiv

  28. [36]

    Grf: Learning a general radi- ance field for 3d representation and rendering

    Alex Trevithick and Bo Yang. Grf: Learning a general radi- ance field for 3d representation and rendering. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 15182–15192, 2021. 1, 2

    2021

  29. [37]

    Sparf: Neural radiance fields from sparse and noisy poses

    Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, and Federico Tombari. Sparf: Neural radiance fields from sparse and noisy poses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4190–4200, 2023. 2

    2023

  30. [38]

    Bifuse: Monocular 360 depth estimation via bi-projection fusion

    Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, and Yi-Hsuan Tsai. Bifuse: Monocular 360 depth estimation via bi-projection fusion. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 462–471, 2020. 3

    2020

  31. [39]

    Perf: Panoramic neural radiance field from a single panorama.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 46(10): 6905–6918, 2024

    Guangcong Wang, Peng Wang, Zhaoxi Chen, Wenping Wang, Chen Change Loy, and Ziwei Liu. Perf: Panoramic neural radiance field from a single panorama.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 46(10): 6905–6918, 2024. 2

    2024

  32. [40]

    Is attention all that nerf needs? arXiv preprint arXiv:2207.13298, 2022

    Peihao Wang, Xuxi Chen, Tianlong Chen, Subhashini Venu- gopalan, Zhangyang Wang, et al. Is attention all that nerf needs? arXiv preprint arXiv:2207.13298, 2022. 2

    2022 arXiv

  33. [41]

    Ibr- net: Learning multi-view image-based rendering

    Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibr- net: Learning multi-view image-based rendering. In Pro- ceedings of the IEEE/CVF conference on computer vision and ...

    2021

  34. [42]

    Freevs: Generative view synthesis on free driv- ing trajectory

    Qitai Wang, Lue Fan, Yuqi Wang, Yuntao Chen, and Zhaox- iang Zhang. Freevs: Generative view synthesis on free driv- ing trajectory. arXiv preprint arXiv:2410.18079, 2024. 3

    2024 arXiv

  35. [43]

    Reconfusion: 3d reconstruction with diffusion priors

    Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P Srinivasan, Dor Verbin, Jonathan T Barron, Ben Poole, et al. Reconfusion: 3d reconstruction with diffusion priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patt...

    2024

  36. [44]

    Unifying flow, stereo and depth estimation

    Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 3, 2

    2023

  37. [45]

    Murf: Multi-baseline radiance fields

    Haofei Xu, Anpei Chen, Yuedong Chen, Christos Sakaridis, Yulun Zhang, Marc Pollefeys, Andreas Geiger, and Fisher Yu. Murf: Multi-baseline radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20041–20050, 2024. 2

    2024

  38. [46]

    Depthsplat: Connecting gaussian splatting and depth

    Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. Depthsplat: Connecting gaussian splatting and depth. arXiv preprint arXiv:2410.13862, 2024. 2, 3, 4, 5, 8

    2024 arXiv

  39. [47]

    Depth any- thing v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiao- gang Xu, Jiashi Feng, and Hengshuang Zhao. Depth any- thing v2. arXiv preprint arXiv:2406.09414, 2024. 3

    2024 arXiv

  40. [48]

    Diffpano: Scalable and con- sistent text to panorama generation with spherical epipolar- aware diffusion

    Weicai Ye, Chenhao Ji, Zheng Chen, Junyao Gao, Xiaoshui Huang, Song-Hai Zhang, Wanli Ouyang, Tong He, Cairong Zhao, and Guofeng Zhang. Diffpano: Scalable and con- sistent text to panorama generation with spherical epipolar- aware diffusion. arXiv preprint arXiv:2410.24203, 2024. 3, 4

    arXiv

  41. [49]

    pixelnerf: Neural radiance fields from one or few images

    Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 4578–4587, 2021. 1, 2

    2021

  42. [50]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024

    Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024. 3 11

    2024 arXiv

  43. [51]

    Taming stable diffusion for text to 360{\deg} panorama im- age generation

    Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xi- aoshui Huang, Dinh Phung, Wanli Ouyang, and Jianfei Cai. Taming stable diffusion for text to 360{\deg} panorama im- age generation. arXiv preprint arXiv:2404.07949, 2024. 3

    2024 arXiv

  44. [52]

    Gs-lrm: Large recon- struction model for 3d gaussian splatting

    Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. Gs-lrm: Large recon- struction model for 3d gaussian splatting. In European Con- ference on Computer Vision, pages 1–19. Springer, 2025. 2

    2025

  45. [53]

    Dreamscene360: Uncon- strained text-to-3d scene generation with panoramic gaus- sian splatting

    Shijie Zhou, Zhiwen Fan, Dejia Xu, Haoran Chang, Pradyumna Chari, Tejas Bharadwaj, Suya You, Zhangyang Wang, and Achuta Kadambi. Dreamscene360: Uncon- strained text-to-3d scene generation with panoramic gaus- sian splatting. In European Conference on Computer Vision, pages 324...

    2025

  46. [54]

    Stereo magnification: learning view syn- thesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: learning view syn- thesis using multiplane images. ACM Trans. Graph., 37(4): 65, 2018. 5, 2 12 Splatter-360: Generalizable 360 ◦ Gaussian Splatting for Wide-baseline Panoramic Image...

    2018

  47. [55]

    Comparisons with More Input Views Table 4 presents a quantitative comparison of MVSplat and Splatter-360 using three-view inputs

    Additional Quantitative Results 6.1. Comparisons with More Input Views Table 4 presents a quantitative comparison of MVSplat and Splatter-360 using three-view inputs. Splatter-360 demon- strates superior performance to MVSplat in SSIM and LPIPS, while exhibiting comparable PSN...

  48. [56]

    Dataset Details The datasets are built based on Replica [32] and HM3D [27] textured mesh dataset

    More Implementation Details 7.1. Dataset Details The datasets are built based on Replica [32] and HM3D [27] textured mesh dataset. In particular, we sample camera trajectories to render videos with AI-Habitat simulation tool [29]. Since AI-habitat only provides the API for cap...

  49. [57]

    Preliminary of 3DGS The 3D Gaussian ellipsoid is formally defined as: G(x | µ, Σ) = e− 1 2 (x−µ)T Σ−1(x−µ) (9) where µ ∈ R3 represents the spatial mean, and Σ ∈ R3×3 denotes the covariance matrix. To ensure numerical stabil- ity during optimization, the covariance matrix Σ is ...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.