La La LiDAR: Large-Scale Layout Generation from LiDAR Data

Ao Liang; Ben Fei; Lingdong Kong; Runnan Chen; Tongliang Liu; Weidong Yang; Xin Li; Youquan Liu

REVIEW 3 major objections 5 minor 1 cited by

La La LiDAR: Large-Scale Layout Generation from LiDAR Data

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read This paper claims that a scene-graph-guided diffusion framework makes LiDAR point-cloud scenes editable and achieves state-of-the-art generation and downstream perception performance.

desk verdict Genuinely useful controllable LiDAR generation, but the SOTA claim rests on an unfair conditional-vs-unconditional comparison and the missing appendix blocks verification. read the letter →

arxiv 2508.03691 v1 pith:DWI4IIYG submitted 2025-08-05 cs.CV cs.RO

Youquan Liu , Lingdong Kong , Weidong Yang , Xin Li , Ao Liang , Runnan Chen , Ben Fei , Tongliang Liu This is my paper · ORCID

classification cs.CVcs.RO

keywords controllableLiDARgenerationscenegraphdiffusionlayout-guidedforeground-awarecontrolinjectionautonomousdrivingsimulationdatasetsdownstreamperceptiontasks

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

La La LiDAR sets out to make LiDAR scene generation controllable without sacrificing realism. The paper's proposal is a two-stage generative model: first, a scene graph—a directed graph whose nodes are foreground objects and whose edges label nine spatial and comparative relations—is diffused into 3D bounding-box layouts; second, the synthesized foreground point clouds are injected as structured conditions into another diffusion model that completes the full scene. The authors construct two scene-graph datasets from existing autonomous-driving data and define relationship-accuracy metrics for layout synthesis. On those benchmarks, the model reports better layout accuracy, lower collision rates, higher scene fidelity, and improved downstream segmentation, detection, and completion than prior LiDAR generators. If the claim holds, editing a scene graph is a direct way to design driving scenarios for simulation and safety validation.

What carries the argument

The load-bearing mechanism is a scene-graph-conditioned diffusion pipeline. A scene graph represents each foreground object as a node and each pairwise spatial or comparative relation (front of, behind, left of, right of, close to, bigger than, smaller than, taller than, shorter than) as a directed edge; a graph convolutional network enriched with text-encoder semantic features produces latent node embeddings, and a time-dependent cross-attention denoiser refines noisy 3D bounding-box states so that object placements stay consistent with the graph. The completed layouts are populated by retrieving or generating object point clouds, and a Foreground-aware Control Injector (FCI) converts that foreground geometry into multi-scale scale and shift parameters with a spatial attention gate, modulating the scene denoiser's features while a mask suppresses invalid sparse regions. The same conditional-diffusion training objective is used for the layout and scene stages, with extra IoU- and collision-based losses to keep layouts physically plausible.

What would settle it

Generate a scene from one graph, then flip a single edge—for example change 'car front of pedestrian' to 'car behind pedestrian'—and check the rendered layout and point cloud; if the two objects do not swap relative positions in most samples, the claimed relation-aware control is not real. A complementary check is to remove one relation type from training graphs or use object pairs absent from training; if the relationship-accuracy metrics stay flat or the collisions do not track the missing relation, the graph is not carrying the control signal.

Watch

Extended reading notes

Core claim

The central discovery is that explicit relational structure, not just geometric boxes, is what lets a LiDAR generator obey object-level edits. The paper argues that by treating object placement as a conditional diffusion process over a semantic scene graph, and then completing the scene with a foreground-aware control injection, a user gains fine-grained control over object categories, positions, and pairwise relations while the model continues to produce a coherent environment. It reports that this framework outperforms previous LiDAR scene generation methods on layout synthesis, scene fidelity, and three downstream perception tasks, and demonstrates that inserting, replacing, or removing object nodes in the graph propagates coherent changes through the generated LiDAR point cloud.

Load-bearing premise

The method's controllability rests on the assumption that the nine relation types extracted from ground-truth 3D boxes truly capture the spatial and comparative structure a user would want to edit, and that the two new relationship-accuracy metrics measure that structure rather than quirks of graph construction.

Editorial extensions

If this is right

Users can edit a scene graph by adding, replacing, or removing object nodes, and the generated LiDAR scene follows the edit while preserving a coherent background.
Explicit pairwise relations give the model a control channel that a plain 3D box layout cannot provide, and the paper's ablations tie this channel to improved scene fidelity.
The same generated scenes improve LiDAR semantic segmentation, 3D object detection, and scene completion when used as extra training data, especially where real annotations are scarce.
The constructed LiDAR scene graph datasets and the relationship-accuracy metrics establish a reusable evaluation setup for layout-guided outdoor scene generation.

Reading between the lines

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

If the nine-relation schema transfers, the same layout control could drive other ego-sensor modalities—radar, camera bird's-eye view, or synthetic LiDAR—by changing only the object-node attributes and leaving the two-stage diffusion structure intact.
A stricter test than the reported aggregate metrics would edit a single edge and verify that only the corresponding pairwise relation changes while all other relations in the scene remain unchanged.
Because foreground point clouds come from a small retrieval database or a category-conditioned generator, novel long-tail scenes could be composed purely by arranging unusual object combinations in the graph, without retraining the scene model.
The low-annotation downstream gains rely on pseudo-labels from a pretrained segmentation network, so auditing pseudo-label quality in generated scenes would separate true data-quality gains from simple occupancy-count effects.

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.

Desk Editor's Note

read the letter

Two things to know. The paper actually delivers a new capability: scene-graph-conditioned layout generation for outdoor LiDAR, with two new datasets and a foreground-aware control injector. That is worth attention. But the headline "state-of-the-art LiDAR generation" claim is built on an unfair comparison. In Table 2, La La LiDAR is conditioned on the foreground point cloud H0, while LiDARGen, R2DM, LiDM, and Text2LiDAR are unconditional. It is solving an easier task—completion given foreground geometry—and the large FRD/FPD margins can largely be explained by that extra information. The paper never adapts baselines to the same conditioning, and never evaluates an unconditional variant against external methods. The internal ablation (Table 7, row a vs. g) does show that conditioning helps when everything else is held fixed, which is genuine evidence, but it doesn't license the external SOTA claim.

What is actually new: applying scene graph diffusion (CommonScenes/EchoScene lineage) to outdoor LiDAR, plus the FCI modulation and the two new scene graph datasets. The editing demo in Figure 6 is the most interesting part; it shows the controllability promised in the abstract. The ablations are self-consistent and the design choices are sensible.

Soft spots: the appendix is missing from the preprint, so the relation schema, dataset construction, metric definitions, and downstream augmentation protocol (number of generated samples, pseudo-label filtering) cannot be checked. No error bars or repeated-seed statistics anywhere; some of the small downstream margins could easily be noise. The RAE/RAD metrics are defined on the same relation schema that was used to construct the graphs, so there's a mild circularity risk, but that is only a real problem if the schema is inconsistent across datasets. The paper cites a lot of prior work and the citations look appropriate.

Bottom line: this is a solid applied contribution that deserves a serious referee, but the manuscript as submitted is not ready. The SOTA framing needs to be replaced with a fair conditional baseline comparison, and the appendix and code need to be released so the datasets and metrics can be checked. If that happens, this is a useful benchmark for controllable LiDAR generation and would get cited.

Referee Report

3 major / 5 minor

Summary. The paper proposes La La LiDAR, a two-stage generative framework for controllable LiDAR scene generation. In the first stage, a scene-graph representation of foreground objects is encoded with a triplet GCN and semantic CLIP embeddings, and a conditioned diffusion model generates object layout boxes. In the second stage, foreground point clouds are synthesized or retrieved, and a Foreground-aware Control Injector (FCI) conditions a range-image diffusion model on this foreground to complete the full scene. The authors introduce two scene-graph datasets, Waymo-SG and nuScenes-SG, and new layout metrics RAE/RAD. They report state-of-the-art results in layout generation, scene generation, and downstream segmentation, detection, and completion, together with qualitative graph-editing demonstrations.

Significance. If the results hold, the paper makes a useful step toward controllable LiDAR generation for autonomous-driving simulation: explicit object-level and relational control is a genuine gap relative to prior unconditional LiDAR diffusion models. The architecture is coherent, the ablation studies are informative, and the graph-editing examples in Figure 6 are compelling evidence of controllability. The construction of two scene-graph datasets is also a potentially valuable resource. However, the central 'state-of-the-art LiDAR generation' claim is weakened by a comparison protocol that gives La La LiDAR a conditioning signal the baselines do not receive, and the new metrics and datasets are not defined in the available manuscript. The paper should be revised to make the comparison fair and the evaluation reproducible.

major comments (3)

[Table 2 and Sec. 3.3 (Eqs. 10-13)] The headline scene-generation comparison is not apples-to-apples. La La LiDAR's scene denoiser is explicitly conditional: Eq. (11) minimizes the noise-prediction error given the foreground point cloud H0, and the FCI injects H0 at multiple scales. The baselines LiDARGen, R2DM, LiDM, and Text2LiDAR are unconditional (or text-conditioned in the case of Text2LiDAR, but not foreground-conditioned). The reported FRD/FPD margins (211.0 vs. 253.8 for R2DM; 9.8 vs. 14.4 for FPD) could therefore reflect the additional information supplied by H0 rather than superior generative modeling. The same concern applies to the downstream augmentation tables, where La La LiDAR samples are conditioned on layouts derived from ground-truth scene graphs while baseline samples are unconditional. Please either report an unconditional variant of La La LiDAR, adapt the baselines to receive the same control signal, and/or explicitly scope the claim to conditional generation; as written, the 'state-of-the-art LiDAR generation' claim in the abstract and Sec. 4.2 is not supported.
[Sec. 4.1 and Tables 1, 3-5] The new layout metrics RAE and RAD are defined only by name, and their formulas, along with the scene graph construction rules and the nine relation definitions, are deferred to an appendix that is not present in the preprint. This is load-bearing because the relation schema simultaneously defines the datasets, the layout generation objective, and the evaluation metrics; a mismatch between the rule-based labeling and the metric can inflate RAE/RAD without reflecting semantic quality. Please provide the exact graph construction protocol, relation thresholds, metric definitions, and a validity check (e.g., human agreement or a correlation analysis with downstream task performance) in the main text or a complete appendix.
[Tables 3-5 and Sec. 4.2] The downstream augmentation protocol is underspecified, and no variance is reported. The paper does not state how many generated samples are added at each supervision level (1%, 5%, 10%, 20%, 50%), how pseudo-labels are filtered, whether the same pretrained SPVCNN is used for all methods, or how class balance is handled. Several reported improvements are small (e.g., 65.4 vs. 64.6 mIoU for SPVCNN at 1% in Table 3; 27.0 vs. 26.4 mAP for CenterPoint at 1% in Table 4), and no error bars or repeated-seed statistics appear anywhere. Please specify the augmentation protocol and report mean and standard deviation over at least three seeds; without this, the downstream claims cannot be distinguished from noise or from protocol choices.

minor comments (5)

[Sec. 3.1, Eq. (1)] The spherical projection formula contains formatting artifacts (the bracket after '1' and the 'f' in the denominator) that should be cleaned up in the camera-ready version.
[Sec. 3.2, Eq. (6)] The collision penalty uses IoU(\hat b_i, \hat b_j) but the reader is not told whether \hat b_i are the predicted layouts from the denoiser or the final decoded boxes; please define the variables and the decoding step explicitly.
[Sec. 3.2 and Sec. 3.3] The loss weights \lambda_1,...,\lambda_5, the collision tolerance \delta, the range-image resolution (H, W), the number of diffusion timesteps T, and the GCN depth K are never reported. These are needed for reproducibility, even if the values are only given in the appendix.
[Sec. 4.2, Table 3] The table header 'MinkU.' is a typo for MinkUNet, and the entry for 'Sup.-only' should be labeled consistently across the two backbones.
[Sec. 4.1 and References] The text cites nuScenes as (Fong et al. 2022), but Fong et al. is the Panoptic nuScenes paper; the original dataset reference is Caesar et al. 2020, which is listed in the bibliography but should be cited here.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are empirical and the generation losses are standard conditional-diffusion objectives; no prediction reduces to a fitted input by construction.

full rationale

The paper does not derive a prediction from a parameter that was fitted to that same prediction. The layout stage uses a standard DDPM objective (Eq. 8) plus geometric losses (Eqs. 6-7), and the scene stage uses noise reconstruction losses (Eqs. 11-13) conditioned on a foreground signal H0. These are inputs to a learned denoiser, not fitted constants that later reappear as the reported metric. The RAE/RAD metrics are defined on the same relation schema used for conditioning, but the definitions are deferred to an appendix; without seeing them, no formal reduction can be exhibited, and the hard rules require quoting a specific equation-level reduction before flagging circularity. The Table 2 comparison against unconditional models is a fairness/correctness concern about extra conditioning information, not a circularity by construction: H0 is a conditioning input, and the background completion is still learned. Self-citations (e.g., LaserMix) are used as baselines or related work and are not load-bearing for the main claims. The downstream perception evaluations provide external grounding. Therefore the central claims are not circular, though the self-built benchmark carries a mild self-referential risk that would need the appendix definitions to assess further.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new physical entities are postulated; the scene graph relation types and FCI module are representational or architectural constructs, not independent entities. The central claim rests on standard diffusion mathematics plus domain assumptions about range image sufficiency, relation schema completeness, CLIP semantic alignment, and pseudo-label reliability.

free parameters (4)

Loss weighting coefficients (lambda_1..lambda_5 in Eqs. 9 and 13) = not reported
Hand-set balancing weights in the layout and scene objectives; no sensitivity analysis is given.
Collision tolerance delta (Eq. 6) = not reported
Threshold in the pairwise IoU collision penalty, chosen by hand; value not stated.
Range image resolution (H, W) in Eq. 1 = not reported
Representation resolution that determines the generative capacity; exact values deferred to appendix or omitted.
Diffusion timesteps T and GCN depth K = not reported
Architecture and schedule choices that affect generation quality; values not stated in the main text.

assumptions (5)

standard math DDPM noise prediction objective is a valid generative training framework (Eq. 2).
The paper adopts the standard DDPM formulation and its reverse-step update from prior literature.
domain assumption Spherical projection to a range image is sufficient for LiDAR scene generation and completion (Eq. 1).
The method operates on range images; any information lost in projection cannot be recovered in generated point clouds.
domain assumption Nine hand-defined relation types extracted from 3D boxes capture the spatial semantics needed for controllable layout generation (Section 3.2, Figure 3).
The relation schema defines both the conditioning signal and the evaluation metrics, so its completeness is load-bearing.
domain assumption CLIP text embeddings of class and relation prompts provide semantically meaningful conditioning (Section 3.2).
The model relies on CLIP's alignment of text and geometry for node and edge features.
domain assumption Pseudo-labels from a pre-trained SPVCNN on generated scenes are reliable enough to improve downstream segmentation (Section 4.2, Table 3).
The augmentation results depend on pseudo-label quality, which is not quantified.

how reviews work

0 comments

Cite this review

Pith. "Pith review of La La LiDAR: Large-Scale Layout Generation from LiDAR Data." pith.science (2026). https://pith.science/paper/DWI4IIYG

@misc{pith2026250803691,
  author       = {Pith},
  title        = {Pith review of: La La LiDAR: Large-Scale Layout Generation from LiDAR Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DWI4IIYG}},
  note         = {Machine review of arXiv:2508.03691}
}

read the original abstract

Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics. While recent diffusion-based models achieve high-fidelity LiDAR generation, they lack explicit control over foreground objects and spatial relationships, limiting their usefulness for scenario simulation and safety validation. To address these limitations, we propose Large-scale Layout-guided LiDAR generation model ("La La LiDAR"), a novel layout-guided generative framework that introduces semantic-enhanced scene graph diffusion with relation-aware contextual conditioning for structured LiDAR layout generation, followed by foreground-aware control injection for complete scene generation. This enables customizable control over object placement while ensuring spatial and semantic consistency. To support our structured LiDAR generation, we introduce Waymo-SG and nuScenes-SG, two large-scale LiDAR scene graph datasets, along with new evaluation metrics for layout synthesis. Extensive experiments demonstrate that La La LiDAR achieves state-of-the-art performance in both LiDAR generation and downstream perception tasks, establishing a new benchmark for controllable 3D scene generation.

Figures

Figures reproduced from arXiv: 2508.03691 by the authors.

**Figure 1.** Motivation of customizable LiDAR scene generation from “La La LiDAR”. Our framework consists of three key stages: 1) LiDAR layout generation using scene graphs, where nodes represent objects and edges capture their spatial relationships; 2) foreground point cloud synthesis, either by retrieving from a database or by generating conditioned on layout parameters; and 3) foregroundconditioned scene generation, where… view at source ↗

**Figure 2.** The proposed LiDAR point cloud layout generation framework. Our approach begins with scene graph construction, establishing both node embeddings (oi) and edge embeddings (oi→j ) to capture spatial relationships. These are enhanced with semantic features from a CLIP text encoder (gi , gi→j ), creating a comprehensive semantic graph. Graph Encoder then processes this information to produce a latent semantic graph with… view at source ↗

**Figure 3.** The schematic definition of the nine relationships (foreground objects) in our LiDAR scene graph formulation. k, node vi and edge ei→j are updated as: (α (k) vi , β(k+1) ei→j , α(k) vj ) = MLP1(β (k) vi , β(k) ei→j , β(k) vj ) , β (k+1) vi = α (k) vi + MLP2 Avg α (k) vj | vj ∈ NG(vi) , (3) where MLP1, MLP2 are multi-layer perceptrons, and NG(vi) denotes neighbors of vi in the scene graph. To enhance semantic … view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Architecture of our foreground-aware LiDAR scene generation framework. Upper Part: The diffusion-based generation process, where initial Gaussian noise XT ∼ N (0, σ2 I) is progressively denoised to generate the final scene Z0, conditioned on a foreground input H0 via…

**Figure 5.** Figure 5: Qualitative comparisons of La La LiDAR against state-of-the-art LiDAR scene generation approaches on the nuScenes [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Controllable scene synthesis via graph-level editing. [PITH_FULL_IMAGE:figures/full_fig_p007_6.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

3D and 4D World Modeling: A Survey
cs.CV 2025-09 conditional novelty 5.0 of 10

A survey that defines 3D/4D world modeling, organizes methods into VideoGen, OccGen, and LiDARGen categories, and compiles datasets, metrics, and benchmark numbers.

Reference graph

Works this paper leans on

5 extracted references · 4 canonical work pages · cited by 1 Pith paper

[184]

Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion

Springer. Zhai, G.; ¨Ornek, E. P.; Wu, S.-C.; Di, Y .; Tombari, F.; Navab, N.; and Busam, B. 2023. CommonScenes: gener- ating commonsense 3D indoor scenes with scene graph dif- fusion. In Advances in Neural Information Processing Sys- tems, 30026–30038. Zhang, S.; Zhao, A.; Yang, L.; Li, Z.; Meng, C.; Xu, H.; Chen, T.; Wei, A.; GU, P. P.; and Sun, L. 2024...

work page Pith review arXiv 2023
[2020]

In IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, 11621–11631

nuScenes: A multimodal dataset for autonomous driv- ing. In IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, 11621–11631. Choy, C.; Gwak, J.; and Savarese, S. 2019. 4D spatio- temporal convnets: Minkowski convolutional neural net- works. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3075–3084. Dhamo, H.; Manhardt, F.;...

work page 2019
[2022]

LiDPM: Rethinking Point Diffusion for Lidar Scene Completion

Homogeneous multi-modal feature fusion and inter- action for 3D object detection. In European Conference on Computer Vision, 691–707. Springer. Liu, Y .; Chen, R.; Li, X.; Kong, L.; Yang, Y .; Xia, Z.; Bai, Y .; Zhu, X.; Ma, Y .; Li, Y .; Qiao, Y .; and Hou, Y . 2023. UniSeg: A unified multi-modal LiDAR segmentation network and the OpenPCSeg codebase. In ...

work page Pith review arXiv 2023
[2024]

In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 28337–28347

SemCity: Semantic scene generation with triplane diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 28337–28347. Li, X.; Ma, T.; Hou, Y .; Shi, B.; Yang, Y .; Liu, Y .; Wu, X.; Chen, Q.; Li, Y .; Qiao, Y .; et al. 2023. LoGoNet: Towards ac- curate 3D object detection with local-to-global cross-modal fusion. I...

work page 2023
[2025]

In Pro- ceedings of the Computer Vision and Pattern Recognition Conference, 17019–17028

WeatherGen: A unified diverse weather generator for LiDAR point clouds via spider mamba diffusion. In Pro- ceedings of the Computer Vision and Pattern Recognition Conference, 17019–17028. Xie, H.; Chen, Z.; Hong, F.; and Liu, Z. 2025. City- Dreamer4D: Compositional generative model of unbounded 4D cities. arXiv preprint arXiv:2501.08983. Xiong, Y .; Ma, W...

arXiv 2025

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [184]

Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion

Springer. Zhai, G.; ¨Ornek, E. P.; Wu, S.-C.; Di, Y .; Tombari, F.; Navab, N.; and Busam, B. 2023. CommonScenes: gener- ating commonsense 3D indoor scenes with scene graph dif- fusion. In Advances in Neural Information Processing Sys- tems, 30026–30038. Zhang, S.; Zhao, A.; Yang, L.; Li, Z.; Meng, C.; Xu, H.; Chen, T.; Wei, A.; GU, P. P.; and Sun, L. 2024...

work page Pith review arXiv 2023

[2] [2020]

In IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, 11621–11631

nuScenes: A multimodal dataset for autonomous driv- ing. In IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, 11621–11631. Choy, C.; Gwak, J.; and Savarese, S. 2019. 4D spatio- temporal convnets: Minkowski convolutional neural net- works. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3075–3084. Dhamo, H.; Manhardt, F.;...

work page 2019

[3] [2022]

LiDPM: Rethinking Point Diffusion for Lidar Scene Completion

Homogeneous multi-modal feature fusion and inter- action for 3D object detection. In European Conference on Computer Vision, 691–707. Springer. Liu, Y .; Chen, R.; Li, X.; Kong, L.; Yang, Y .; Xia, Z.; Bai, Y .; Zhu, X.; Ma, Y .; Li, Y .; Qiao, Y .; and Hou, Y . 2023. UniSeg: A unified multi-modal LiDAR segmentation network and the OpenPCSeg codebase. In ...

work page Pith review arXiv 2023

[4] [2024]

In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 28337–28347

SemCity: Semantic scene generation with triplane diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 28337–28347. Li, X.; Ma, T.; Hou, Y .; Shi, B.; Yang, Y .; Liu, Y .; Wu, X.; Chen, Q.; Li, Y .; Qiao, Y .; et al. 2023. LoGoNet: Towards ac- curate 3D object detection with local-to-global cross-modal fusion. I...

work page 2023

[5] [2025]

In Pro- ceedings of the Computer Vision and Pattern Recognition Conference, 17019–17028

WeatherGen: A unified diverse weather generator for LiDAR point clouds via spider mamba diffusion. In Pro- ceedings of the Computer Vision and Pattern Recognition Conference, 17019–17028. Xie, H.; Chen, Z.; Hong, F.; and Liu, Z. 2025. City- Dreamer4D: Compositional generative model of unbounded 4D cities. arXiv preprint arXiv:2501.08983. Xiong, Y .; Ma, W...

arXiv 2025