Pith. sign in

REVIEW 4 major objections 5 minor 54 references

LiDARDraft claims that a single 3D-layout representation—simple semantic shapes raycast into semantic and depth range images—lets one frozen diffusion model generate realistic LiDAR point clouds from text, images, semantic maps, or point cl

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-03 14:28 UTC pith:2CBOMSHI

load-bearing objection Useful architecture for layout-conditioned LiDAR generation, but the multi-modal evaluation doesn't actually test the multi-modal claim. the 4 major comments →

arxiv 2512.20105 v2 pith:2CBOMSHI submitted 2025-12-23 cs.CV

LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs

Haiyun Wei , Fan Lu , Yunwei Zhu , Zehan Zheng , Weiyi Xue , Lin Shao , Xudong Zhang , Ya Wu
show 1 more author
Guang Chen
This is my paper
classification cs.CV
keywords LiDAR point cloud generation3D layoutControlNetdiffusion modelrange imageconditional generationautonomous driving simulationtext-to-LiDAR
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

LiDARDraft aims to show that the gap between user intent and dense LiDAR geometry can be closed by inserting a deliberately coarse 3D layout as a universal intermediate. Text descriptions, images, and point cloud scans are each converted into a set of simple semantic shapes; those shapes are raycast from the sensor position into a range-image-like pair of semantic and depth channels; and a ControlNet drives a pre-trained unconditional diffusion model to produce a realistic scan aligned to that condition. The paper reports that this single framework outperforms existing per-modal generators across all four input types on standard distribution-matching metrics, and that editing the layout edits the scene. If the claim holds, autonomous-driving simulation moves closer to being generated from scratch—from a sentence, a photo, or a rough sketch instead of a hand-built 3D world.

Core claim

On the paper's own terms, the central discovery is that a coarse 3D layout—semantic shapes such as cuboids for cars, ellipsoids for vegetation, and planes for roads—can be turned into a pixel-aligned semantic-and-depth control image by raycasting from the LiDAR sensor, and that this control image is enough to steer a frozen unconditional range-image diffusion model into generating realistic scans. The paper presents this as the first direct layout-to-point-cloud generation with point-wise control, and supports it with ablations showing that removing raycasting, semantic channels, or depth channels degrades quality, and with comparisons reporting the best numbers across semantic-map, text, im

What carries the argument

The load-bearing mechanism is the unified 3D layout: a set of semantic primitives that can be produced by a large language model from text, by segmentation-and-depth plus clustering from an image, by semantic segmentation plus clustering from a point cloud, or directly by a user. The layout is raycast from the sensor origin into a range-image-format condition with two channels—semantic label and depth—so every pixel of the eventual scan has a corresponding control signal. A ControlNet, a trainable side-branch with zero-initialized convolutions, takes this condition and steers the frozen decoder of a pretrained diffusion model; only the ControlNet is fine-tuned. The training objective is nois

Load-bearing premise

The load-bearing premise is that a 3D layout extracted from the target dataset itself is a faithful stand-in for a user-supplied condition, so the reported improvements measure genuine layout control rather than information leaked from the ground-truth scan used to build the layout.

What would settle it

Render a layout in which a car is moved five meters laterally from a position seen in training; if the generated point cloud does not move the corresponding vehicle cluster by that same translation, then the model is following dataset statistics rather than the pixel-aligned layout, and the controllability claim collapses.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • One trained generative backbone can be conditioned on semantic maps, text, images, or point clouds; the paper reports that LiDARDraft beats the relevant single-modality baselines on all four distribution-matching metrics for every input type.
  • Because only the ControlNet side-branch is trained while the diffusion backbone stays frozen, adding new input modalities or editing layouts does not require retraining the generator; the paper reports convergence in about 5,000 steps instead of 100,000.
  • Scene editing becomes direct: moving or deleting a shape in the 3D layout moves or deletes the corresponding object in the generated point cloud, demonstrated by a 'remove car' manipulation.
  • A single LiDAR scan can be transformed into many diverse but layout-consistent scans, which the paper offers as a data-augmentation route for downstream perception.
  • Text-to-simulation is demonstrated as a proof of concept: a language model turns a sentence into layout code, and sampling from the resulting layout preserves stated vehicle counts, road types, and spatial relations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the quantitative evaluation uses layouts derived from the target scans themselves, so the reported scores should be read as an upper bound on layout-following fidelity; the text and image paths add upstream errors from language understanding, segmentation, depth estimation, and clustering that are not reflected in the main comparison table.
  • Editorial inference: the same recipe—raycast a semantic-primitive layout into pixel-aligned semantic and depth channels and feed it through a ControlNet-style side branch—should transfer to other generative backbones (autoregressive or VQVAE range-image models) and other sensor modalities, because nothing in the conditioning format is tied to diffusion or LiDAR specifically.
  • Editorial inference: a decisive test of whether the model actually follows the layout, rather than reproducing dataset statistics, would be to place an object in an impossible or rare configuration (e.g., a vehicle floating above the road) and check whether the generated scan places it there; the paper does not include such counterfactual layouts.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes LiDARDraft, a diffusion-based framework for LiDAR point cloud generation conditioned on multiple input modalities (semantic maps, text, images, and point clouds) through a shared 3D layout representation. Layouts are converted into semantic and depth range images by raycasting and fed into a ControlNet attached to a pretrained unconditional range-image diffusion model. The authors report quantitative results on KITTI-360 and nuScenes using FRD, MMD, JSD, and FPD, plus qualitative demonstrations on SemanticKITTI, image-to-LiDAR, text-to-LiDAR, and point-cloud transformation. Ablations study the effect of ControlNet fine-tuning, raycasting versus BEV projection, and the semantic/depth channels.

Significance. The unified layout interface and pixel-aligned range-map ControlNet are a sensible and potentially useful design. If the multi-modal claims were validated end-to-end, the paper would offer a practical “simulation from scratch†tool. Strengths include the explicit layout-to-range-map transformation, cross-dataset evaluation, efficiency analysis, and the training-cost reduction from fine-tuning a pretrained model. However, the quantitative evidence for the text and image modalities is not yet established: the evaluation protocol for those rows of Table 1 is not specified and appears to rely on layouts extracted from the target data rather than on the actual text/image inputs. The closest layout-based baseline, LiDARDM, is not compared, and no uncertainty estimates are provided for any reported metric. The central claim is therefore plausible but currently under-supported.

major comments (4)
  1. [§4.1 and Table 1] The evaluation protocol for the Text and Image rows of Table 1 is not described. Section 4.1 states that cross-dataset evaluation is performed 'using layouts extracted from each dataset to compute metrics,' and Section 4.7 says that for non-semantic-map inputs the framework 'directly encodes them into layouts.' It is never specified whether the Text row uses actual GPT-5-generated layouts from text prompts, whether the Image row uses actual camera images through SAM+DepthAnything+DBSCAN, or whether both simply use the ground-truth semantic/depth layout extracted from the target point cloud itself. If the latter is the case, the reported FRD/JSD values for Text and Image measure layout-conditioned reconstruction, not text or image understanding. This is load-bearing for the paper's central claim of 'consistently superior performance across all input types.' The authors must state the exac
  2. [§4.7 and Table 1] The closest layout-based baseline, LiDARDM [53], is discussed in the introduction and related work but is absent from the quantitative comparison. Since LiDARDraft's contribution is direct layout-to-point-cloud control, omitting the strongest layout-conditioned method makes it impossible to assess whether the proposed ControlNet/raycasting pipeline improves on the state of the art. A direct comparison under the same evaluation protocol is needed.
  3. [§4.1 and Tables 1–2] All reported quantitative results are point estimates without error bars, number of seeds, or significance tests. Differences such as FRD 23.01 versus 24.91 in the ablation table may be within sampling noise. The authors should report multiple seeds, confidence intervals, or statistical tests, especially for the cross-modal claims where the margins are modest.
  4. [§3.2, §4.3, §4.4] The full image-to-LiDAR and text-to-LiDAR pipelines rely on external, untrained components (SAM, DepthAnything, DBSCAN, GPT-5) whose output quality is never quantitatively evaluated. The qualitative examples in Figures 3 and 4 are not backed by end-to-end metrics, and there is no ablation or failure analysis of layout-extraction errors. Without this, the 'consistent superior performance' claim applies only to the ControlNet stage under oracle-like layout conditions. At minimum, the authors should evaluate layout extraction accuracy and its downstream effect on generation, or explicitly restrict the claim to layout-conditioned generation.
minor comments (5)
  1. [Figure 4 caption] There are typos: 'sanple2' should be 'sample2', and 'inpu t' should be 'input'.
  2. [References] Reference [33] has corrupted title markup: 'lhigh-resolution image synthesis with latent diffusion modelsl' should be 'High-Resolution Image Synthesis with Latent Diffusion Models'.
  3. [Abstract and Section 1] The abstract and contributions mention sketches as an input, but no sketch experiments are reported. Please either add sketch results or remove this claim from the scope.
  4. [Section 3.2] The sentence 'we utilize ControlNet [50] framework:as illustrated' has a capitalization/colon error, and 'V AEs' in the introduction has an unwanted space. Also, 'SAM [21]' is described as a semantic segmentation model, but SAM is a promptable segmentation model; the actual role of SAM in the pipeline should be clarified.
  5. [Section 3.2 and Figure 7] The claim of 'reducing the training cost by 95,000 steps' is unclear without specifying the baseline training schedule. Figure 7 lacks axis labels and units; 'Intel-class CPU' is vague.

Circularity Check

1 steps flagged

Table 1's quantitative comparison reduces to layout-conditioned reconstruction because the paper's only stated evaluation protocol extracts the conditioning layout from the target point cloud that is later scored.

specific steps
  1. fitted input called prediction [§4.1 Setup; §4.7 Comparison with Baselines; Table 1]
    "To assess generalization, we perform cross-dataset evaluation on KITTI-360 and nuScenes, using layouts extracted from each dataset to compute metrics (Figure 6). ... for the other three inputs, our framework directly encodes them into layouts and generates samples accordingly. ... As shown in Table 1, our approach consistently achieves superior performance across all input types."

    The only quantitative protocol described obtains the conditioning layout by extracting it from the target dataset, then scores generated point clouds against that same dataset. Because the layout is a lossy function of the target point cloud (semantic segmentation, clustering, then raycasting to semantic/depth range images), the conditional input is derived from the output variable itself. Generating from such an oracle condition and computing FRD/JSD/FPD against the same target measures inversion of a compressed encoding of the target, not whether text or image inputs control generation. No separate procedure is given for Table 1's Text/Image rows, so the claimed 'superior performance across all input types' is, on the paper's own description, a layout-reconstruction result rather than a

full rationale

The paper's other components are not circular: the range-image representation, score-matching losses, and ControlNet adaptation are standard and independent of the conditioning protocol. Self-citations (GeoNLF, LiDAR4D) appear only in related work and are not load-bearing. The qualitative sections (4.3, 4.4) do exercise real image and text inputs, but they provide no quantitative metrics. The central quantitative claim in Table 1, however, depends on the stated evaluation protocol in §4.1, which uses layouts extracted from the same dataset used for scoring. As described, the conditional signal is a function of the target point cloud, so the reported gains largely reflect reconstruction from a target-derived oracle rather than generation from arbitrary text, image, or point-cloud inputs. This is a partial but central circularity in the evaluation of the paper's main multi-modal claim.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 0 invented entities

The central claims rest on a chain of representations: range images are a faithful proxy for LiDAR, coarse semantic 3D layouts carry enough information to reconstruct realistic scans, and external perception/LLM components produce trustworthy layouts. None of these are proven in the paper; each is an unmeasured assumption. The quantitative results further depend on several unstated hyperparameters and on conditions extracted from the target data.

free parameters (5)
  • Noise schedule / score-matching weighting sigma_i = not stated
    Eqs. (4)-(5) use per-noise-level weights sigma_i^2, and the paper says 'noise adjustment plays a critical role', but the actual schedule and number of levels are not specified.
  • ControlNet architecture hyperparameters = channels {128,128,256,256}, each block repeated twice
    Chosen by hand and copied from the pretrained Stable Diffusion model; no ablation of this architecture choice is provided.
  • Raycasting resolution / sensor model = 64x1024
    All point clouds are projected to 64x1024 regardless of sensor, discarding sensor-specific vertical resolution; this is a fixed design choice affecting all results.
  • DBSCAN clustering parameters and layout-fitting thresholds = not stated
    Image and point-cloud layout extraction in §4.3 depends on DBSCAN clustering and bounding-box fitting, but eps, min_samples, and fitting rules are not given.
  • GPT-5 text-to-layout prompt/code template = not stated
    Text-to-layout relies on an external LLM generating Blender code; the exact prompting and geometry conventions are not provided, so text-conditioned results are not reproducible.
axioms (5)
  • domain assumption LiDAR point clouds can be faithfully represented as dense 64x1024 range images with one range value per pixel.
    §3.1 adopts this representation; it ignores ray drop / missing returns and assumes every beam direction has a measured depth, which is not true for real LiDAR and is not discussed.
  • domain assumption A coarse 3D layout of cuboids/ellipsoids/planes plus semantic labels is a sufficient condition to reconstruct a realistic LiDAR scan.
    The entire method rests on this: §3.2 states the layout 'encapsulates a coarse semantic and geometric distribution of the scene' and is enough to guide high-fidelity generation.
  • standard math The score-matching objective of Song & Ermon applies correctly to range-image diffusion with the specified weighting.
    Eqs. (4)-(5) assume this standard objective; no convergence, stability, or error analysis is provided for this specific representation.
  • domain assumption Semantic segmentation (SAM), depth estimation (DepthAnything), and clustering of a single perspective image contain enough information to build a layout matching the 360-degree LiDAR scene.
    §4.3 uses this to convert images to layouts; no quantitative validation of the intermediate pseudo point cloud or layout is given.
  • domain assumption External components (GPT-5, SAM, DepthAnything) are treated as reliable oracles and are not fine-tuned.
    §4.3-4.4 use these off-the-shelf; any errors propagate into the layout and final point cloud but are never measured.

pith-pipeline@v1.3.0-alltime-deepseek · 12263 in / 13390 out tokens · 130679 ms · 2026-08-03T14:28:59.061244+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs." pith.science (2026). https://pith.science/paper/2CBOMSHI

@misc{pith2026251220105,
  author       = {Pith},
  title        = {Pith review of: LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2CBOMSHI}},
  note         = {Machine review of arXiv:2512.20105}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Generating realistic and diverse LiDAR point clouds is crucial for autonomous driving simulation. Although previous methods achieve LiDAR point cloud generation from user inputs, they struggle to attain high-quality results while enabling versatile controllability, due to the imbalance between the complex distribution of LiDAR point clouds and the simple control signals. To address the limitation, we propose LiDARDraft, which utilizes the 3D layout to build a bridge between versatile conditional signals and LiDAR point clouds. The 3D layout can be trivially generated from various user inputs such as textual descriptions and images. Specifically, we represent text, images, and point clouds as unified 3D layouts, which are further transformed into semantic and depth control signals. Then, we employ a rangemap-based ControlNet to guide LiDAR point cloud generation. This pixel-level alignment approach demonstrates excellent performance in controllable LiDAR point clouds generation, enabling "simulation from scratch", allowing self-driving environments to be created from arbitrary textual descriptions, images and sketches.

Figures

Figures reproduced from arXiv: 2512.20105 by Fan Lu, Guang Chen, Haiyun Wei, Lin Shao, Weiyi Xue, Xudong Zhang, Ya Wu, Yunwei Zhu, Zehan Zheng.

Figure 1
Figure 1. Figure 1: Overview of LiDARDraft. Various inputs are unified into layout representations and projected into range images using RayCast￾ing, which are then fed into ControlNet to guide unconditional LiDAR point cloud generation. Image first undergoes semantic segmentation and depth estimation to form a pseudo-point cloud and then clustered to create the layout. Point cloud is semantically segmented and then clustered… view at source ↗
Figure 2
Figure 2. Figure 2: Layout to LiDAR Point Cloud results. The white boxes indicate vehicle locations in the layout, and the blue boxes show the generated vehicle positions. The rightmost column presents samples of the remove-car manipulation. hicles with consistent shapes and sizes.Furthermore, point cloud manipulations are highly efficient, as modifying the layouts alone allows seamless adjustments. We present the results aft… view at source ↗
Figure 3
Figure 3. Figure 3: Image to LiDAR Point Cloud generation. The orange boxes mark the area covered by the input image.In the left column, the vehicles in the input image are accurately sampled by both our method and LiDARDiffusion. However, the LiDARDiffusion sampling results contain multiple interfering vehicles. In the right column, the input image shows an empty road, and our method generates a point cloud along the straigh… view at source ↗
Figure 4
Figure 4. Figure 4: Text (left half) / Point Cloud (right half) to LiDAR Point Cloud generation. For text: an automated driving scene layout description is generated with GPT and used to sample the point cloud. LiDARDraft accurately samples the point cloud consistent with the text description, recognizing road types, vehicle counts, and their relative positions. The blue and pink boxes highlight specific details of the sample… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Results on the SemanticKITTI Dataset. We [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A cross-dataset comparison shows that the model [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Module Runtime and Memory Usage [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Incorporating different channels leads to distinct effects [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

54 extracted references · 13 linked inside Pith

  1. [1]

    Gzscenic: Automatic scene generation for gazebo simu- lator.arXiv preprint arXiv:2104.08625, 2021

    Afsoon Afzal, Claire Le Goues, and Christopher S Timper- ley. Gzscenic: Automatic scene generation for gazebo simu- lator.arXiv preprint arXiv:2104.08625, 2021. 2

    Pith/arXiv arXiv 2021

  2. [2]

    Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles

    Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, and Daniela Rus. Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles. In2022 International Conference on Robotics and Automation (ICRA), pages 2419–2426. IEEE, 2022. 2

    2022

  3. [3]

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

    Jens Behley, Michael Garbade, Andres Milioto, Jan Quen- zel, Sven Behnke, Cyrill Stachniss, and J ¨urgen Gall. Se- mantickitti: A dataset for semantic scene understanding of lidar sequences. InProceedings of the IEEE/CVF Interna- tional Conference on Computer Vision (ICCV), pages 9297– 9307, 2019. 5

    2019

  4. [4]

    Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023. 1

    Pith/arXiv arXiv 2023

  5. [5]

    Efficient online seg- mentation for sparse 3d laser scans.PFG–Journal of Pho- togrammetry, Remote Sensing and Geoinformation Science, 85:41–52, 2017

    Igor Bogoslavskyi and Cyrill Stachniss. Efficient online seg- mentation for sparse 3d laser scans.PFG–Journal of Pho- togrammetry, Remote Sensing and Geoinformation Science, 85:41–52, 2017. 3

    2017

  6. [6]

    Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

    Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Lan- guage models are few-shot learners.Advances in Neural In- formation Processing Systems, 33:1877–1901, 2020. 6

    1901

  7. [7]

    Deep generative modeling of lidar data

    Lucas Caccia, Herke Van Hoof, Aaron Courville, and Joelle Pineau. Deep generative modeling of lidar data. In2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5034–5040. IEEE, 2019. 1, 2

    2019

  8. [8]

    nuscenes: A mul- timodal dataset for autonomous driving

    Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yin Zhou Pan, Giulio Baldan, and Oscar Beijbom. nuscenes: A mul- timodal dataset for autonomous driving. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11621–11631, 2020. 5

    2020

  9. [9]

    Ricardo J. G. B. Campello, Davoud Moulavi, and Joerg Sander. Density-based clustering based on hierarchical den- sity estimates. InAdvances in Knowledge Discovery and Data Mining, pages 160–172, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg. 5

    2013

  10. [10]

    Part-aware data augmentation for 3d object detection in point cloud

    Jaeseok Choi, Yeji Song, and Nojun Kwak. Part-aware data augmentation for 3d object detection in point cloud. In2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3391–3397. IEEE, 2021. 7

    2021

  11. [11]

    Understanding of blender software.Models and methods in modern science, 2(13):40–45, 2023

    Iqbol Djurayev. Understanding of blender software.Models and methods in modern science, 2(13):40–45, 2023. 2

    2023

  12. [12]

    Carla: An open urban driv- ing simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Anto- nio Lopez, and Vladlen Koltun. Carla: An open urban driv- ing simulator. InConference on robot learning, pages 1–16. PMLR, 2017. 2

    2017

  13. [13]

    Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets.Advances in neural information processing systems, 27, 2014. 1, 2

    2014

  14. [14]

    Lidar snowfall simulation for robust 3d object detection

    Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, and Luc Van Gool. Lidar snowfall simulation for robust 3d object detection. InPro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16364–16374, 2022. 2

    2022

  15. [15]

    Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022. 2

    Pith/arXiv arXiv 2022

  16. [16]

    Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020. 1, 4

    2020

  17. [17]

    Imagen video: High definition video generation with diffusion mod- els.arXiv preprint arXiv:2210.02303, 2022

    Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion mod- els.arXiv preprint arXiv:2210.02303, 2022. 1

    Pith/arXiv arXiv 2022

  18. [18]

    Context-aware data augmentation for lidar 3d object detection

    Xuzhong Hu, Zaipeng Duan, Xiao Huang, Ziwen Xu, Delie Ming, and Jie Ma. Context-aware data augmentation for lidar 3d object detection. In2023 IEEE International Conference on Image Processing (ICIP), pages 11–15. IEEE, 2023. 7

    2023

  19. [19]

    Neural lidar fields for novel view synthesis

    Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad Schindler, and Or Litany. Neural lidar fields for novel view synthesis. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 18236–18246, 2023. 2

    2023

  20. [20]

    Auto-encoding variational bayes.arXiv preprint arXiv:1312.6114, 2013

    Diederik P Kingma. Auto-encoding variational bayes.arXiv preprint arXiv:1312.6114, 2013. 1, 2

    Pith/arXiv arXiv 2013

  21. [21]

    Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4015–4026, 2023. 5

    2023

  22. [22]

    Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3292–3310, 2022

    Yiyi Liao, Jun Xie, and Andreas Geiger. Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3292–3310, 2022. 5

    2022

  23. [23]

    A conditional point diffusion-refinement paradigm for 3d point cloud completion.arXiv preprint arXiv:2112.03530, 2021

    Zhaoyang Lyu, Zhifeng Kong, Xudong Xu, Liang Pan, and Dahua Lin. A conditional point diffusion-refinement paradigm for 3d point cloud completion.arXiv preprint arXiv:2112.03530, 2021. 1

    Pith/arXiv arXiv 2021

  24. [24]

    Robot operating system 2: Design, architecture, and uses in the wild.Science robotics, 7(66):eabm6074, 2022

    Steven Macenski, Tully Foote, Brian Gerkey, Chris Lalancette, and William Woodall. Robot operating system 2: Design, architecture, and uses in the wild.Science robotics, 7(66):eabm6074, 2022. 2

    2022

  25. [25]

    Lidarsim: Realistic lidar simulation by leveraging the real world

    Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. Lidarsim: Realistic lidar simulation by leveraging the real world. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11167–11176, 2020. 2

    2020

  26. [26]

    Sdedit: Guided image synthesis and editing with stochastic differential equa- tions.arXiv preprint arXiv:2108.01073, 2021

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia- jun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equa- tions.arXiv preprint arXiv:2108.01073, 2021. 2

    Pith/arXiv arXiv 2021

  27. [27]

    Rangenet++: Fast and accurate lidar semantic segmentation

    Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyrill Stachniss. Rangenet++: Fast and accurate lidar semantic segmentation. In2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 4213–4220. IEEE, 2019. 3

    2019

  28. [28]

    Lidar data synthesis with denoising diffusion probabilistic models.arXiv preprint arXiv:2309.09256, 2023

    Kazuto Nakashima and Ryo Kurazume. Lidar data synthesis with denoising diffusion probabilistic models.arXiv preprint arXiv:2309.09256, 2023. 2

    Pith/arXiv arXiv 2023

  29. [29]

    Generative range imaging for learning scene priors of 3d li- dar data

    Kazuto Nakashima, Yumi Iwashita, and Ryo Kurazume. Generative range imaging for learning scene priors of 3d li- dar data. InProceedings of the IEEE/CVF Winter Confer- ence on Applications of Computer Vision, pages 1256–1266,

  30. [30]

    Gpt-5 technical report.https://openai.com,

    OpenAI. Gpt-5 technical report.https://openai.com,

  31. [31]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023. 1

    Pith/arXiv arXiv 2023

  32. [32]

    Towards realistic scene generation with lidar diffusion models

    Haoxi Ran, Vitor Guizilini, and Yue Wang. Towards realistic scene generation with lidar diffusion models. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14738–14748, 2024. 1, 2, 4, 5, 8

    2024

  33. [33]

    lhigh-resolution image synthesis with latent diffusion modelsl

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. lhigh-resolution image synthesis with latent diffusion modelsl. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 1, 4, 5, 8

    2022

  34. [34]

    Projected gans converge faster.Advances in Neural Information Processing Systems, 34:17480–17492, 2021

    Axel Sauer, Kashyap Chitta, Jens M ¨uller, and Andreas Geiger. Projected gans converge faster.Advances in Neural Information Processing Systems, 34:17480–17492, 2021. 1

    2021

  35. [35]

    Airsim: High-fidelity visual and physical simulation for autonomous vehicles

    Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. InField and Service Robotics: Re- sults of the 11th International Conference, pages 621–635. Springer, 2018. 2

    2018

  36. [36]

    Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019

    Yang Song and Stefano Ermon. Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019. 2, 4

    2019

  37. [37]

    Improved techniques for training score-based generative models.Advances in neural information processing systems, 33:12438–12448, 2020

    Yang Song and Stefano Ermon. Improved techniques for training score-based generative models.Advances in neural information processing systems, 33:12438–12448, 2020. 1, 4

    2020

  38. [38]

    Lidar-nerf: Novel li- dar view synthesis via neural radiance fields.arXiv preprint arXiv:2304.10406, 2023

    Tang Tao, Longfei Gao, Guangrun Wang, Yixing Lao, Peng Chen, Hengshuang Zhao, Dayang Hao, Xiaodan Liang, Mathieu Salzmann, and Kaicheng Yu. Lidar-nerf: Novel li- dar view synthesis via neural radiance fields.arXiv preprint arXiv:2304.10406, 2023. 2

    Pith/arXiv arXiv 2023

  39. [39]

    Lion: Latent point dif- fusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039, 2022

    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. 1

    2022

  40. [40]

    Neural discrete representation learning.Advances in neural information pro- cessing systems, 30, 2017

    Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning.Advances in neural information pro- cessing systems, 30, 2017. 2

    2017

  41. [41]

    Learn- ing interactive driving policies via data-driven simulation

    Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, and Daniela Rus. Learn- ing interactive driving policies via data-driven simulation. In 2022 International Conference on Robotics and Automation (ICRA), pages 7745–7752. IEEE, 2022. 2

    2022

  42. [42]

    Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in Neural Information Processing Systems, 36, 2024

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in Neural Information Processing Systems, 36, 2024. 1

    2024

  43. [43]

    Text2lidar: Text-guided lidar point cloud gen- eration via equirectangular transformer.arXiv preprint arXiv:2407.19628, 2024

    Yang Wu, Kaihua Zhang, Jianjun Qian, Jin Xie, and Jian Yang. Text2lidar: Text-guided lidar point cloud gen- eration via equirectangular transformer.arXiv preprint arXiv:2407.19628, 2024. 5, 8

    Pith/arXiv arXiv 2024

  44. [44]

    Omnigen: Unified image generation.arXiv preprint arXiv:2409.11340, 2024

    Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xin- grun Xing, Ruiran Yan, Shuting Wang, Tiejun Huang, and Zheng Liu. Omnigen: Unified image generation.arXiv preprint arXiv:2409.11340, 2024. 1

    Pith/arXiv arXiv 2024

  45. [45]

    Learning compact representations for lidar com- pletion and generation

    Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, and Raquel Urtasun. Learning compact representations for lidar com- pletion and generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1074–1083, 2023. 1, 2

    2023

  46. [46]

    Geonlf: Geometry guided pose-free neural lidar fields.arXiv preprint arXiv:2407.05597, 2024

    Weiyi Xue, Zehan Zheng, Fan Lu, Haiyun Wei, Guang Chen, and Changjun Jiang. Geonlf: Geometry guided pose-free neural lidar fields.arXiv preprint arXiv:2407.05597, 2024. 2

    Pith/arXiv arXiv 2024

  47. [47]

    Depth anything: Unleashing the power of large-scale unlabeled data

    Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371–10381, 2024. 5

    2024

  48. [48]

    Routledge,

    Alf Yarwood.Introduction to AutoCAD 2010. Routledge,

    2010

  49. [49]

    Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models

    Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6796–6807, 2024. 1

    2024

  50. [50]

    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, 4, 5, 8

    2023

  51. [51]

    Lidar4d: Dynamic neural fields for novel space-time view lidar synthesis

    Zehan Zheng, Fan Lu, Weiyi Xue, Guang Chen, and Changjun Jiang. Lidar4d: Dynamic neural fields for novel space-time view lidar synthesis. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5145–5154, 2024. 2

    2024

  52. [52]

    Learning to generate realistic lidar point clouds

    Vlas Zyrianov, Xiyue Zhu, and Shenlong Wang. Learning to generate realistic lidar point clouds. InEuropean Conference on Computer Vision, pages 17–35. Springer, 2022. 1, 2, 4

    2022

  53. [53]

    Lidardm: Generative lidar simulation in a generated world

    Vlas Zyrianov, Henry Che, Zhijian Liu, and Shenlong Wang. Lidardm: Generative lidar simulation in a generated world. arXiv preprint arXiv:2404.02903, 2024. 1, 2

    arXiv 2024

  54. [2024]

    Accessed: 2025-01-01. 6

    2025