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REVIEW 2 major objections 5 minor 43 references

SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read SSEditor generates large-scale 3D outdoor semantic scenes directly from user-drawn masks in one diffusion pass, outperforming prior unconditional generators on SemanticKITTI and CarlaSC.

desk verdict Useful mask-conditioned triplane diffusion for outdoor scenes, but the semantic token definition in Eq. 5 is unresolved at inference and may leak the target triplane. read the letter →

arxiv 2411.12290 v1 pith:IWCKINXN submitted 2024-11-19 cs.CV cs.AI

Haowen Zheng , Yanyan Liang This is my paper · ORCID
classification cs.CVcs.AI
keywords 3Dsemanticscenegenerationmask-to-scenetriplanerepresentationlatentdiffusionmodelgeometric-semanticfusioncontrollableeditinginpaintingKITTI
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

SSEditor claims that large-scale 3D outdoor semantic scenes can be generated or edited directly from mask assets in a single diffusion pass, without the multi-step resampling that earlier triplane-based generators needed for inpainting. The method is a two-stage latent diffusion framework: a 3D autoencoder first compresses voxel scenes into triplane latents, then a mask-conditional diffusion model denoises those latents under a geometric-semantic fusion module. If the claim holds, a user could add or remove cars, widen roads, erase LiDAR trailing artifacts, or stitch masks from different scenes to compose larger urban scenes, while cutting inference time roughly fourfold at 100 sampling steps. The paper reports FID 47.93 versus 61.20 for SemCity on SemanticKITTI and 50.98 versus 137.94 on CarlaSC, and shows qualitative novel-scene generation on the unseen Occ-3D Waymo dataset.

What carries the argument

The load-bearing object is the trimask, a compression of a 3D voxel mask into three orthogonal 2D planes $\mathcal{M} = [\mathcal{M}_{xy}, \mathcal{M}_{xz}, \mathcal{M}_{yz}]$, stored per category in an asset library. The argument is carried by the Geometric-Semantic Fusion Module (GSFM): its geometric branch uses an MLP to turn concatenated trimask planes into mask embeddings and applies self-attention across categories, and its semantic branch adds coarse label embeddings plus fine-grained semantic tokens $T_i^{sem} = \text{SpatialPooling}(M_i \cdot T)$ obtained from category-masked triplane features. The fused embeddings serve as the query in cross-attention, with geometric and semantic embeddings as key and value, while the raw trimask is concatenated with the noised triplane. Training also uses classifier-free guidance by randomly zeroing the trimask, which lets the denoiser run in a single DDPM sampling pass without returning to earlier timesteps.

What would settle it

Compute the semantic tokens in $T_i^{sem} = \text{SpatialPooling}(M_i \cdot T)$ with $T$ set to a placeholder triplane (all zeros, or taken from an unrelated scene) and run the full mask-to-scene pipeline on Occ-3D Waymo masks. If category fidelity and FID stay near the paper's reported values, the method is genuinely mask-driven; if they collapse, the conditioning is using the target scene's own latent content and the 'novel scene' claim is not supported by the current experiments.

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Extended reading notes

Core claim

At its center, SSEditor claims that a 3D outdoor semantic scene can be generated from a user-provided trimask—three orthogonal 2D planes encoding where each semantic category should appear—by training a diffusion model directly on triplane latents. The paper's central mechanism is the Geometric-Semantic Fusion Module: a geometric branch encodes trimasks into mask embeddings and runs self-attention across categories, while a semantic branch combines per-class label embeddings with finer-grained semantic tokens $T_i^{sem} = \text{SpatialPooling}(M_i \cdot T)$ pooled from category-masked triplane features. Fused embeddings enter the denoising U-Net through cross-attention, and the raw trimask is also concatenated with the noised triplane, so the model sees both where objects are and what they are. With this conditioning, scene inpainting, outpainting, and editing use ordinary DDPM sampling instead of RePaint-style resampling. The paper reports that this improves FID from 61.20 to 47.93 on SemanticKITTI and from 137.94 to 50.98 on CarlaSC relative to SemCity, with higher precision and recall, and that the same model transfers to unseen Occ-3D Waymo masks for novel urban scene generation.

Load-bearing premise

The load-bearing assumption is that the fine-grained semantic condition can be computed from the user's mask input alone; in the paper's Equation (5), however, the semantic tokens are pooled from the triplane of the very scene being generated, and the paper does not say what triplane is used when generating a brand-new scene that has no ground-truth latent.

Editorial extensions

If this is right

  • Scene editing that previously required resampling-based repainting—adding or removing objects, removing trailing artifacts, widening roads—can be done with one DDPM pass, reducing inference time from 56.44 seconds to 13.40 seconds at 100 sampling steps in the paper's comparison.
  • A user can compose larger scenes by concatenating trimasks drawn from different assets or scenes, enabling controllable outpainting and scene stitching without retraining the model.
  • Because trimasks are interpolated to a common resolution, a model trained on SemanticKITTI can generate novel urban scenes from Occ-3D Waymo masks, pointing toward rapid construction of 3D occupancy data for new cities.
  • Mask conditioning yields measurable controllability: on SemanticKITTI validation, semantic scene completion reaches IoU 57.85 and mIoU 43.09, above the reported RGB-based (41.92/14.89) and point-cloud-based (50.24/37.55) comparison methods.

Reading between the lines

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

  • Extension: The paper compares against unconditional generators, and its own ablations remove one module at a time; a mask-conditional baseline trained on the same triplane space would isolate how much of the FID gain comes from the conditioning signal itself rather than from the fusion design.
  • Extension: Equation (5) computes semantic tokens from $M_i \cdot T$ with $T$ the triplane being generated, so the paper does not specify what $T$ is used for a truly novel scene; an independent test should compute tokens from a mask-only or placeholder source to confirm the 'novel scene' results do not depend on the target's own latent content.
  • Extension: The asset-library workflow implies an interactive editor—drawing, copying, rotating trimasks to widen roads or add congestion—but the paper only demonstrates offline, pre-built mask edits, not real-time user interaction or a user study.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper proposes SSEditor, a two-stage framework for mask-to-scene generation of 3D outdoor semantic scenes. Stage one trains a 3D scene autoencoder to obtain latent triplane features; stage two trains a mask-conditional latent diffusion model on these features, using a Geometric-Semantic Fusion Module (GSFM) that injects geometric mask embeddings and semantic tokens into cross-attention. The paper reports improved FID/KID/IS/Precision/Recall over SSD and SemCity on SemanticKITTI and CarlaSC, plus qualitative demonstrations of scene inpainting, outpainting, trailing-artifact removal, and cross-dataset generation on Occ-3D Waymo.

Significance. If the method performs as claimed, it would advance controllable 3D semantic scene generation, moving beyond unconditional triplane diffusion methods. The trimask asset library and the GSFM are practical contributions, and the paper includes an ablation study and acknowledges a known limitation with small objects. The paper also explicitly discloses the non-reproducibility of SemCity's evaluation protocol, which is commendable. However, the central conditioning mechanism—the semantic tokens computed via Eq. (5)—has an unspecified source for the triplane T, which creates a potential leakage/circularity issue that directly affects the interpretation of the reported gains.

major comments (2)
  1. [§3.3, Eq. (5)] The source of T in the semantic token definition T_i^sem = Spatial Pooling(M_i · T) is never specified. The text uses T for the triplane features, T0 for the clean latent, and T_t for the noised latent. If T in Eq. (5) is the clean target triplane T0, then during training the semantic tokens encode per-class appearance of the exact scene that the diffusion model must generate, so the conditioning leaks the target. The ablation in Table 3 shows that removing the semantic tokens increases FID from 47.93 to 53.67, so the tokens materially affect performance. At inference for novel scenes (e.g., Occ-3D Waymo in §4.5), no target triplane exists, and the paper does not explain how the semantic tokens are computed. The paper must clarify the source of T (e.g., a prior, a learnable per-class embedding, or a mask-derived feature) and, if T is the target, re-run the experiments with a non-leaky condition to validate the mask-to-scene claim.
  2. [§4.4, Table 1] The quantitative comparison with SemCity is not fully controlled. The paper states that SemCity does not disclose the image sets used for evaluation, making results non-reproducible, and then says that for a fair comparison the authors 'train on the training set and generate scenes on the validation set.' However, it is not specified whether the same real images and the same generated scenes are used for all methods, nor whether the validation split matches SemCity's. Since the reported FID gains (47.93 vs. 61.20) are a central claim, the evaluation protocol should be described with enough detail to ensure that the comparison is apples-to-apples, or the claim should be tempered accordingly.
minor comments (5)
  1. [§3.3, Eq. (8)] The diffusion equation has a typo: the Gaussian should be written with the clean latent T0, not T, i.e., N(√α_t T0, (1−α_t)I). The notation for the cumulative noise schedule also mixes α_t and ᾱ_t, which is confusing.
  2. [§3.3, first paragraph] The phrase 'The semantic tokens are generated from the features of a specific category' is vague; it should clearly state whether these are per-class learnable embeddings or computed from the scene triplane, and how they are obtained at inference.
  3. [§4.4] There is a typo in the text: 'SmeanticKITTI' should be 'SemanticKITTI'. Also, in Figure 5 the legend for the background colors is unclear, and 'Patking' appears to be a misspelling of 'Parking'.
  4. [§4.4, Table 2] The comparison with Symphonies and SCPNet in Table 2 is not apples-to-apples because SSEditor takes a 3D mask as input while the baselines use RGB or point clouds. The text should explicitly state that this is a task/methodology comparison, not a direct benchmark, and clarify what the IoU/mIoU numbers represent in this context.
  5. [§5, Limitations] The limitation paragraph is honest and useful; however, it could also mention the conditioning-source issue as a limitation, since the paper currently leaves it ambiguous.

Circularity Check

1 steps flagged · score 6.0 of 10

Eq. (5) defines the semantic token as a pooling of the very triplane the diffusion model is trained to generate; for mask-only inference the source of T is never specified, making the central claim partially circular or underspecified.

  1. self definitional [Section 3.3, Eq. (5); conditioning used in Eqs. (6)-(7); inference on Occ-3D Waymo in Section 4.5]
    "To address this, we introduce a finer-grained semantic token Tsem ∈ R^{N×Cemb}, which is defined as: Ti_sem = Spatial Pooling(Mi · T) (5) where i indicates the i-th semantic class and spatial pooling represents average pooling along the spatial dimension."

    Eq. (5) is written with the unsubscripted triplane T, while Eq. (8) distinguishes the clean target latent T0 from the noised latent Tt. If T in Eq. (5) is T0, then the semantic token is the spatial average of the target triplane inside each mask. This token is added to the label embedding and injected into cross-attention (Eqs. (6)-(7)), so the diffusion model is conditioned on a pooled version of the exact triplane it is trained to reconstruct. At inference on a novel scene, such as Occ-3D Waymo, no target triplane exists, and the paper never states that T is replaced by Tt or by an external reference. The ablation in Table 3 shows the semantic tokens are load-bearing (FID 53.67 without them vs.

full rationale

The paper's central derivation chain is short: the stage-1 autoencoder produces a triplane T, and the stage-2 diffusion model reconstructs a clean triplane T0 from a noised triplane Tt and a trimask M. The semantic branch defines a fine-grained token Tsem = SpatialPooling(M_i·T) and feeds it into the cross-attention conditioning. The problem is that the paper never says which triplane T is in Eq. (5). A literal reading, consistent with Sec. 3.1 where T is the scene triplane, makes the semantic condition a function of the target scene itself, so the claimed mask-to-scene generation is partly reconstruction rather than generation. A charitable reading would take T to be the noised latent Tt available at each denoising step, which would avoid target leakage, but the text does not state this and the inference-time procedure for Occ-3D Waymo is not described. Because the ambiguity is central and the semantic tokens are shown to be load-bearing, this is a partial circularity rather than a fully forced equivalence. No independent self-citation, uniqueness-import, or renaming circularity is present; the issue is confined to the undefined/leaky conditioning in Eq. (5).

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The method rests on standard latent diffusion plus a mask condition; the main uncharged assumption is the source of semantic tokens at inference. If tokens come from the target triplane, the generation task is partially solved by the condition. No new physical entities are posited.

free parameters (5)
  • loss weight alpha = 1
    Balances cross-entropy and Lovasz-softmax losses in Eq. 1; chosen by hand in Section 4.2.
  • downsampling factors d, dz = d=2, dz=1
    Control latent triplane resolution; set in Section 4.2.
  • latent channels = 16
    Channel count of triplane features; set in Section 4.2.
  • embedding channel Cemb = 64
    Dimension of mask and semantic embeddings; set in Section 4.2.
  • diffusion sampling steps = 100
    Number of DDPM steps for training and inference; set in Section 4.2.
assumptions (3)
  • domain assumption Triplane features are a sufficient latent representation for semantic scene generation and reconstruction.
    Adopted from SemCity and used throughout Sections 3.1 and 3.2.
  • domain assumption A 3D binary mask (trimask) encodes object position, size, and orientation well enough to control generation.
    Core input design of Section 3.2; no study shows how mask errors propagate.
  • domain assumption Semantic tokens in Eq. 5 can be computed from user inputs at inference without access to the target scene's triplane.
    Required for novel-scene generation; the paper never specifies the source of T in Eq. 5 during inference.

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Cite this review

Pith. "Pith review of SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model." pith.science (2026). https://pith.science/paper/IWCKINXN

@misc{pith2026241112290,
  author       = {Pith},
  title        = {Pith review of: SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IWCKINXN}},
  note         = {Machine review of arXiv:2411.12290}
}
read the original abstract

Recent advancements in 3D diffusion-based semantic scene generation have gained attention. However, existing methods rely on unconditional generation and require multiple resampling steps when editing scenes, which significantly limits their controllability and flexibility. To this end, we propose SSEditor, a controllable Semantic Scene Editor that can generate specified target categories without multiple-step resampling. SSEditor employs a two-stage diffusion-based framework: (1) a 3D scene autoencoder is trained to obtain latent triplane features, and (2) a mask-conditional diffusion model is trained for customizable 3D semantic scene generation. In the second stage, we introduce a geometric-semantic fusion module that enhance the model's ability to learn geometric and semantic information. This ensures that objects are generated with correct positions, sizes, and categories. Extensive experiments on SemanticKITTI and CarlaSC demonstrate that SSEditor outperforms previous approaches in terms of controllability and flexibility in target generation, as well as the quality of semantic scene generation and reconstruction. More importantly, experiments on the unseen Occ-3D Waymo dataset show that SSEditor is capable of generating novel urban scenes, enabling the rapid construction of 3D scenes.

Figures

Figures reproduced from arXiv: 2411.12290 by the authors.

Figure 1
Figure 1. Controllable 3D semantic scene generation by SSEditor. The proposed SSEditor enables users to customize the generation or [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of our SSEditor framework. It comprises two main processes: (a) a 3D autoencoder learns the triplane representation [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Pipeline of building 3D mask assets. The 3D mask is [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The details of editing 3D scenes with SSEditor: 1. When [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visualization of semantic scene generation comparing with SemCity [ [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Create a novel urban scene from masks. The novel scene [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Reference graph

Works this paper leans on

43 extracted references · 17 canonical work pages

  1. [1]

    Blended diffusion for text-driven editing of natural images

    Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208–18218, 2022. 2

    2022

  2. [2]

    Se- mantickitti: A dataset for semantic scene understanding of lidar sequences

    Jens Behley, Martin Garbade, Andres Milioto, Jan Quen- zel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. Se- mantickitti: A dataset for semantic scene understanding of lidar sequences. In Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 9297–9307,

  3. [3]

    The lov´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

    Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lov´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4413–4421,

  4. [4]

    Demystifying mmd gans

    Mikołaj Bi ´nkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmd gans. arXiv preprint arXiv:1801.01401, 2018. 6

    arXiv 2018

  5. [5]

    Efficient geometry-aware 3d generative adversarial networks

    Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123–16133, 2022. 2

    2022

  6. [6]

    3d-r2n2: A unified approach for single and multi-view 3d object reconstruction

    Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In Com- puter Vision–ECCV 2016: 14th European Conference, Am- sterdam, The Netherlands, October 11-14, 2016, Proceed- ings, Part VIII 14, pages 628–644. Springer, 2016. 2

    work page 2016

  7. [7]

    Build-a-scene: Interactive 3d layout control for diffusion-based image gen- eration

    Abdelrahman Eldesokey and Peter Wonka. Build-a-scene: Interactive 3d layout control for diffusion-based image gen- eration. arXiv preprint arXiv:2408.14819, 2024. 2, 3

    arXiv 2024

  8. [8]

    Make-a-scene: Scene- based text-to-image generation with human priors

    Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman. Make-a-scene: Scene- based text-to-image generation with human priors. In Eu- ropean Conference on Computer Vision , pages 89–106. Springer, 2022. 2

    2022

Show all 43 references
  1. [9]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems , 30, 2017. 6

    2017

  2. [10]

    Classifier-free diffusion guidance

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 5

    2022 arXiv

  3. [11]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2, 3, 6, 8

    2020

  4. [12]

    Sym- phonize 3d semantic scene completion with contextual in- stance queries

    Haoyi Jiang, Tianheng Cheng, Naiyu Gao, Haoyang Zhang, Tianwei Lin, Wenyu Liu, and Xinggang Wang. Sym- phonize 3d semantic scene completion with contextual in- stance queries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20258– 20...

    2024

  5. [13]

    Diffindscene: Diffusion-based high-quality 3d indoor scene generation

    Xiaoliang Ju, Zhaoyang Huang, Yijin Li, Guofeng Zhang, Yu Qiao, and Hongsheng Li. Diffindscene: Diffusion-based high-quality 3d indoor scene generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4526–4535, 2024. 1

    2024

  6. [14]

    Holodiffusion: Training a 3d diffusion model using 2d images

    Animesh Karnewar, Andrea Vedaldi, David Novotny, and Niloy J Mitra. Holodiffusion: Training a 3d diffusion model using 2d images. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 18423–18433, 2023. 1

    2023

  7. [15]

    Dif- fusion probabilistic models for scene-scale 3d categorical data

    Jumin Lee, Woobin Im, Sebin Lee, and Sung-Eui Yoon. Dif- fusion probabilistic models for scene-scale 3d categorical data. arXiv preprint arXiv:2301.00527, 2023. 1, 2, 5, 6

    2023 arXiv

  8. [16]

    Semcity: Semantic scene genera- tion with triplane diffusion

    Jumin Lee, Sebin Lee, Changho Jo, Woobin Im, Juhyeong Seon, and Sung-Eui Yoon. Semcity: Semantic scene genera- tion with triplane diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 28337–28347, 2024. 1, 2, 3, 5, 6, 7

    2024

  9. [17]

    Diffusion- sdf: Text-to-shape via voxelized diffusion

    Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. Diffusion- sdf: Text-to-shape via voxelized diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12642–12651, 2023. 2

    2023

  10. [18]

    Instructscene: Instruction- driven 3d indoor scene synthesis with semantic graph prior

    Chenguo Lin and Yadong Mu. Instructscene: Instruction- driven 3d indoor scene synthesis with semantic graph prior. arXiv preprint arXiv:2402.04717, 2024. 2, 3

    2024 arXiv

  11. [19]

    Pyramid diffusion for fine 3d large scene generation

    Yuheng Liu, Xinke Li, Xueting Li, Lu Qi, Chongshou Li, and Ming-Hsuan Yang. Pyramid diffusion for fine 3d large scene generation. arXiv preprint arXiv:2311.12085, 2023. 1, 2, 3

    2023 arXiv

  12. [20]

    Repaint: Inpainting using denoising diffusion probabilistic models

    Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11461–11471, 2022. 2, ...

    2022

  13. [21]

    Text2mesh: Text-driven neural stylization for meshes

    Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13492– 13502, 2022. 2

    2022

  14. [22]

    Autosdf: Shape priors for 3d comple- tion, reconstruction and generation

    Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, and Shub- ham Tulsiani. Autosdf: Shape priors for 3d comple- tion, reconstruction and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 306–315, 2022. 2

    2022

  15. [23]

    Difffacto: Controllable part-based 3d point cloud generation with cross diffusion

    George Kiyohiro Nakayama, Mikaela Angelina Uy, Jiahui Huang, Shi-Min Hu, Ke Li, and Leonidas Guibas. Difffacto: Controllable part-based 3d point cloud generation with cross diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14257–14267...

    2023

  16. [24]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021

    Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021. 2

    2021 arXiv

  17. [25]

    Hierarchical text-conditional image gener- 9 ation with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- 9 ation with clip latents. arXiv preprint arXiv:2204.06125, 1 (2):3, 2022. 2

    2022 arXiv

  18. [26]

    Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies

    Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4209–4219, 2024. 1, 2

    2024

  19. [27]

    Lmscnet: Lightweight multiscale 3d semantic completion

    Luis Roldao, Raoul de Charette, and Anne Verroust-Blondet. Lmscnet: Lightweight multiscale 3d semantic completion. In 2020 International Conference on 3D Vision (3DV), pages 111–119. IEEE, 2020. 4

    2020

  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, 5

    2022

  21. [29]

    Palette: Image-to-image diffusion models

    Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 conference proceedings, pages 1–10,

    2022

  22. [30]

    Improved techniques for training gans

    Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016. 6

    2016

  23. [31]

    3d neural field generation using triplane diffusion

    J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein. 3d neural field generation using triplane diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20875–20886, 2023. 1

    2023

  24. [32]

    Diffuscene: Denoising diffu- sion models for generative indoor scene synthesis

    Jiapeng Tang, Yinyu Nie, Lev Markhasin, Angela Dai, Justus Thies, and Matthias Nießner. Diffuscene: Denoising diffu- sion models for generative indoor scene synthesis. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20507–20518, 20...

    2024

  25. [33]

    Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving

    Xiaoyu Tian, Tao Jiang, Longfei Yun, Yucheng Mao, Huitong Yang, Yue Wang, Yilun Wang, and Hang Zhao. Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving. Advances in Neural Information Processing Systems, 36, 2024. 5, 7, 8

    2024

  26. [34]

    Lion: Latent point dif- fusion models for 3d shape generation

    Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al. Lion: Latent point dif- fusion models for 3d shape generation. Advances in Neural Information Processing Systems, 35:10021–10039, 2022. 2

    2022

  27. [35]

    Occsora: 4d occupancy generation models as world simulators for au- tonomous driving

    Lening Wang, Wenzhao Zheng, Yilong Ren, Han Jiang, Zhiyong Cui, Haiyang Yu, and Jiwen Lu. Occsora: 4d occupancy generation models as world simulators for au- tonomous driving. arXiv preprint arXiv:2405.20337, 2024. 1, 2

    2024 arXiv

  28. [36]

    Pretraining is all you need for image-to-image translation

    Tengfei Wang, Ting Zhang, Bo Zhang, Hao Ouyang, Dong Chen, Qifeng Chen, and Fang Wen. Pretraining is all you need for image-to-image translation. arXiv preprint arXiv:2205.12952, 2022. 2

    2022 arXiv

  29. [37]

    Motionsc: Data set and network for real- time semantic mapping in dynamic environments

    Joey Wilson, Jingyu Song, Yuewei Fu, Arthur Zhang, An- drew Capodieci, Paramsothy Jayakumar, Kira Barton, and Maani Ghaffari. Motionsc: Data set and network for real- time semantic mapping in dynamic environments. IEEE Robotics and Automation Letters, 7(3):8439–8446, 2022. 5, 6, 7

    2022

  30. [38]

    Scpnet: Se- mantic scene completion on point cloud

    Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, and Yu Qiao. Scpnet: Se- mantic scene completion on point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 17642–17651, 2023. 5

    2023

  31. [39]

    Disn: Deep implicit surface network for high-quality single-view 3d reconstruction

    Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. Disn: Deep implicit surface network for high-quality single-view 3d reconstruction. Ad- vances in neural information processing systems , 32, 2019. 2

    2019

  32. [40]

    Commonscenes: Generating commonsense 3d indoor scenes with scene graphs

    Guangyao Zhai, Evin Pınar ¨Ornek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, and Benjamin Busam. Commonscenes: Generating commonsense 3d indoor scenes with scene graphs. Advances in Neural Information Process- ing Systems, 36, 2024. 1, 2, 6

    2024

  33. [41]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 2

    2023

  34. [42]

    Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation

    Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, and Xi Li. Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 22490–22499, 2023. 2

    2023

  35. [43]

    3d shape generation and completion through point-voxel diffusion

    Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceed- ings of the IEEE/CVF international conference on computer vision, pages 5826–5835, 2021. 1 10

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.