Title: Spiral: Semantic-Aware Progressive LiDAR Scene Generation and Understanding
Venue: NeurIPS 2025
Accepted: yes
Keywords: LiDAR Diffusion

LiDAR Generation

LiDAR Semantic Segmentation
Abstract: Leveraging diffusion models, 3D LiDAR scene generation has achieved great success in both range-view and voxel-based representations. While recent voxel-based approaches can generate both geometric structures and semantic labels, existing range-view methods are limited to producing unlabeled LiDAR scenes. Relying on pretrained segmentation models to predict the semantic maps often results in suboptimal cross-modal consistency. To address this limitation while preserving the advantages of range-view representations, such as computational efficiency and simplified network design, we propose Spiral, a novel range-view LiDAR diffusion model that simultaneously generates depth, reflectance images, and semantic maps. Furthermore, we introduce novel semantic-aware metrics to evaluate the quality of the generated labeled range-view data. Experiments on SemanticKITTI and nuScenes datasets demonstrate that Spiral achieves state-of-the-art performance with the smallest parameter size, outperforming two-step methods that combine the best available generative and segmentation models. Additionally, we validate that Spiral’s generated range images can be effectively used for synthetic data augmentation in the downstream segmentation training, significantly reducing the labeling effort on LiDAR data.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents SPIRAL, a novel method for generating semantic LiDAR scenes in range-view. During training, SPIRAL employs two encoders to separately process LiDAR and semantic features and enables generation in both unconditional and conditional modes. At inference, it first produces semantic maps and LiDAR data concurrently in an unconditional step, then uses the predicted semantic maps as conditional inputs for further refinement. Additionally, the paper introduces S-FPD and S-MMD metrics to evaluate semantic generation quality. Experiments demonstrate that SPIRAL outperforms existing two-stage methods in semantic label generation and enhances segmentation model performance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. It proposes an innovative pipeline for simultaneous generation of LiDAR scenes and semantic information.\n2. It introduces novel metrics (S-FPD and S-MMD) to assess semantic generation quality.\n\n**Weaknesses**\n1. The pipeline is limited to generating single-frame LiDAR data, which is restrictive compared to recent advances in temporal LiDAR sequence generation. Extending the model to handle sequential data would strengthen its applicability.\n2. The method only supports unconditional generation of LiDAR and semantic maps, limiting its use primarily to data augmentation for segmentation tasks. Moreover, the segmentation performance improvement is marginal compared to traditional two-stage methods, suggesting limited contributions to the field.\n\nQUESTIONS:\n1. The evaluation and comparison in the paper are unclear for me. The authors compare SPIRAL to methods like LiDM and R2DM. These methods claim to address multiple tasks and provide clear performance evaluations for each of them. However, it is unclear which task—conditional or unconditional generation—is evaluated in SPIRAL’s experiments. If unconditional generation is evaluated, what is the ground truth? If conditional generation is evaluated, what are the model’s inputs, given that the only condition mentioned is the semantic map, which cannot be provided during generation?\n2. Table 3 illustrates the impact of generative data augmentation on segmentation performance. The term “w/o” in the table presumably means “without generative data”. However, the mIoU of the “w/o” model still increases with a higher ratio of synthetic data, which seems contradictory. Could the authors clarify the experimental setup and explain these results?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the challenge of generating large-scale labeled LiDAR datasets, which are costly and time-consuming to collect and annotate. To overcome these limitations, the authors propose SPIRAL, a novel semantic-aware progressive range-view LiDAR diffusion model that jointly generates depth, reflectance, and semantic maps from Gaussian noise.\n\nSPIRAL operates in both unconditional and conditional modes, progressively refining predictions through a closed-loop mechanism where intermediate semantic maps are used to guide the generation process. This approach enhances cross-modal consistency between geometry and semantics. Additionally, the authors introduce new semantic-aware evaluation metrics enabling an assessment of the geometric, physical, and semantic fidelity of generated LiDAR scenes.\n\nThrough these, SPIRAL enables labeled LiDAR scene generation and demonstrates the utility of such data for training segmentation models, suggesting strong potential for generative data augmentation in autonomous driving and related applications.\n\nSTRENGTHS AND WEAKNESSES:\n## Strength\n- The proposed method achieves state-of-the-art performance in labeled LiDAR scene generation while maintaining a smaller parameter size compared to existing models. \n- The paper demonstrates the practical utility of the generated data by showing that it can be effectively used for synthetic data augmentation in downstream segmentation tasks. This highlights the potential of the method to reduce manual labeling efforts, especially in resource-constrained settings.\n\n- The paper introduces a closed-loop inference mechanism that incorporates confidence-filtered semantic predictions as conditional inputs. This strategy improves cross-modal consistency between geometry and semantics.\n\n## Weakness\n- While the paper introduces a closed-loop inference mechanism that alternates between unconditional and conditional modes based on semantic prediction confidence, it does not provide quantitative analysis or statistics on when this mode switching occurs during the generation process. Including such information would help clarify the behavior and stability of the proposed inference strategy.\n\n- The paper does not report a comparison of inference time or time consumption for labeled LiDAR scene generation. Since SPIRAL is positioned as an efficient generative model, empirical results on generation speed would further support its suitability for large-scale data generation tasks.\n\nQUESTIONS:\n## Questions\n- Is there any statistical analysis or empirical data on the denoising time step at which the model begins alternating between unconditional and conditional modes (when over 80% of the segmentation pixels surpass the confidence threshold) ?\n  - If the switching occurs too early, the inference process could resemble a two-step pipeline, running both the LiDAR generation and segmentation components multiple times, potentially leading to inefficiencies.\n  - If the switching occurs too late, it raises questions about the actual influence of semantic guidance on the final output.\n- In Figure 4, what is the source of the ground-truth (GT) semantic map used for comparison with the generated LiDAR scenes?\n- Could the authors provide a comparison of inference time for labeled LiDAR scene generation?\n\n## Suggestions\n- It may be worth exploring the use of classifier-free guidance (CFG)-style guidance with semantic maps during inference. Providing additional semantic guidance signals could potentially improve generation quality and enhance semantic-geometric consistency.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose SPIRAL, a LiDAR diffusion model that jointly generates depth, reflectance, and pixel-wise semantic labels from noise. They tune the diffusion model to be conditioned on the semantic map as well as outputting the map, thus yielding a closed-loop in terms of both training and inference. They also propose several new metrics for semantic alignment evaluation of the LIDAR generative models. Through the experiment, they show their method are superior compared to their baselines, which is previous LIDAR generative models with a semantic prediction network.\n\nSTRENGTHS AND WEAKNESSES:\n* The closed-loop training/inference scheme of diffusion models is novel and reasonable.\n* Generating Semantic information along with the LIDAR makes sense for achieving a better and more precise alignment \n* The experiments show that their method passes the baseline in both scene and semantic map quality\n\nQUESTIONS:\n- What is the memory and runtime comparing to your baselines?\n- The proposed S-x metrics compares the semantic map with the ones in the dataset, but **it does not account for the alignment between the generated scene and the map**. For example, say one model generates a garbage scene, but the segmentor segments the scene perfectly; in this case, the metrics would be double-dropped. In other words, the scene generation quality is overly out-weighted to the segmentation quality in such measurement.\n- Closed/Open loop inference: if I understand it correctly, the open loop is directly diffusing the semantic map and LIDAR. Intuitively, I think this would cause more problems in terms of discrepancy between the two modalities. The metrics, however, are not able to reflect if the map is aligned with the generated scene.\n- The Data Augmentation results should be more extensively compared, as this is one of the main applications mentioned in the paper\n    - It would be great if the authors show the mIOU of SPVCNN++ without any augmented data for comparison\n    - The author listed a column of “None” in Table C but failed to explain this column.\n    - Will the result still be significant in ratios where the real data is abundant, such as 80%?\n\nLIMITATIONS:\nThe authors included limitations and societal impact in the appendix.\n\nSee questions for weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
The paper introduces SPIRAL, a semantic-aware range-view LiDAR diffusion model for jointly generating semantic labels, depth, and reflectance images. The authors further extend evaluation metrics with semantic awareness to comprehensively assess the generated LiDAR scenes, showing good results across the benchmarks.

STRENGTHS AND WEAKNESSES:
Strengths:
1) Simultaneously generating LiDAR scenes with semantic labels is a useful capability for the community, as it enables the creation of synthetic data for training and evaluating perception models.
2) Semantic-aware metrics are introduced for a comprehensive evaluation, and the numerical results are good when compared to baselines.
3) The paper is well-written, the problem is clearly specified and the solution is well presented.

Weaknesses:
1) Limited semantic label set: Semantic output is constrained to pre-defined categories from SemanticKITTI and nuScenes, reducing applicability to varied domains. 
2) Additionally, long-tailed label distributions in LiDAR data (e.g., rare classes like bicyclists) can cause the model to underfit these classes. The paper doesn’t detail any strategies (e.g., re-weighting) to balance this.
3) Although the approach reduces collection & annotation costs, since the model is trained on a specific dataset, it is questionable how well it captures real-world variability and complexity, which may limit the practical utility of the generated data.

QUESTIONS:
All of my concerns are listed in the weaknesses section, and I may adjust the rating if they are well addressed.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03