Spiral: Semantic-Aware Progressive LiDAR Scene Generation and Understanding
Authors: Dekai Zhu
Yixuan Hu
Youquan Liu
Dongyue Lu
Lingdong Kong
Slobodan Ilic
Venue: NeurIPS 2025 poster ()
Keywords: LiDAR Diffusion
LiDAR Generation
LiDAR Semantic Segmentation
Primary area: applications
TL;DR: Spiral is a new type of LiDAR generation model that enables semantic awareness and progressive diffusion.

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.

G
SPIRAL: Semantic-Aware Progressive LiDAR Scene Generation and Understanding
Dekai 14., Yixuan Hu?.*
Youquan Liu² Slobodan Ilic.
Dongyue Lu
Lingdong Kong"
Technical University of Munich
Fudan University
National University of Singapore
*Munich Center for Machine Learning
https://dekai21. gi io/bPIRA//PI
broy
moto
tuc
oweh
ped
b.ist
muist
road
park
walk
curro
2uid
trun
pole
Figure I: Visualization of LiDAR scenes and their semantic labels jointly generated by SPIRAL, exhibiting high geometric fidelity and semantic-geometric consistency.
Abstract
Leveraging recent diffusion models, LiDAR-bard Raaaedd large-scale 3D scene generation has achieved great success. While recent voxel-based approaches can generate both geometric structures and semantic labels, existing range-view methods are limited producing unlabeled LiDAR scenes. Relying 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 representa- tions, such as computational efficiency and simplified network design, we propose SPIRAL, 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 the SemanticKITTI and nuScenes datasets demonstrate that SPIRAL achieves sate-offthee-rr performance with the smallest parameter size, outperforming two-step methods that combine the generative and segmentation models. Additionally, we validate that range images generated by SPIRAL can be effectively used for synthetic data augmentation in the downstream segmentation training, significantly reducing the labeling effort on LiDAR data.
Introduction
By providing accurate distance measurements regardless of ambient illumination, LiDAR plays crucial role in scene understanding and navigation for robotics and autonomous driving [11, 60, 10, 13,
Equal contributions.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Conventional Two-Step Pipeline
(c) Quantitative Comparison
Step
Step
Generative
LIDAR
Sugimentation
Sumaanic Labels
Saaw [xx 10 10
LDDM [320M)
LOME (372M)
LIDAROwN (127M)
Model
Boories
LIDAGGen (80M)
(b) SPIRAL: Unified One-Step Closed-Loop Framework
Spiral
Clased-nop
18 (61M)
RZDM (81M)
Model: * PargaNar-
RZOMF (120M)
Spiral
Semantic
Soor @5
Labels
500
1003
1500
2000
S-FPD
Existing range-view LiDAR generative models typically generate only depth and reflectance images, requiring an additional pre-trained segmentation model to predict semantic labels. (b) SPIRAL: In contrast, Spiral jointly generates depth, reflectance, and semantic maps. A closed-loop inference mechanism (highlighted in the dash arrow) further improves cross- modal consistency. (c) Results: Spiral achieves state-of-the-art performance with the smallest parameter size (61M) among the related methods.
Figure 2: (a) Two-step methods:
32, 26, 58). However, collecting and annotating large-scale LiDAR datasets is both expensive and tine-consuming [12, 64, 41, 38, 633. J. To address this issue, recent research has increasingly focused on using denoising diffusion probabilistic models (DDPMs) [14] for LiDAR generative modeling, aiming to create tools capable of generating unlimited LiDAR scenes [73, 48, 16, 42, 43, 37, 34].
Existing generative approaches can be categorized into voxel- -ased methods [43, 29, 49, 4] and range- view-based methods [73, 42, 48, 16]. The former divides the 3D space into regular volumetric grids (i.e., voxels) and captures detailed geometric structures with 3D convolutional networks [8]. However, they often suffer from high memory consumption and computational overhead [52]. Methods based on range-view, on the other hand, project LiDAR point clouds onto 2D cylindrical image plane using the sensor azimuth and elevation angles [73, 48, 16, 33]. This leads to compact 2D representations of depth and reflectance, allowing for efficient processing via 2D convolutional networks [1, 23]. These methods are significantly more memory-efficient and computationally lightweight [62, 61].
In this work, we aim to address two limitations in existing range-view generative methods:
1. While recent models such as LiDARGen [73] and R2DM [42] generate high-fidelity LiDAR scenes, their outputs are restricted to depth and reflectance images, without producing semantic labels.
2. Existing evaluations extract global features for each scene from three perspectives (range-view image, Cartesian point cloud, and BEV projection) to assess the distributional similarity between generated and real scenes. However, none of these evaluation methods consider semantic labels.
For the first limitation, a straightforward solution to adopt two-step pipeline: first, produce unlabeled LiDAR scenes using generative models [73, 42, 48], and then apply pretrained segmen- tation model (e.g., RangeNet++ [40]) predict the corresponding semantic labels, as depicted in Figure (a). However, this approach often results in suboptimal performance due to two key issues: (1) The generative and segmentation models are trained independently, which hinders shared represen- tations between the two tasks and reduces training efficiency. (2) The semantic maps, being predicted post hoc, cannot serve as conditional guidance during generation, leading to limited consistency between semantics and other modalities, including depth and reflectance.
Therefore, we propose a novel semantic-aware range-view view LiDAR diffusion model, named SPIRAL. as depicted in Figure 2 (b), with the following key features:
Semantic-aware generation: Our framework aims to jointly generate depth, reflectance, and semantic maps from the Gaussian noise, different from existing models that lack semantic awareness and require separate segmentation models to obtain semantic labels.
Progressive semantic prediction: At each denoising step, SPIRAL outputs an intermediate semantic map which is aggregated via exponential moving averaging (EMA) to suppress noise and produce stable, per-pixel confidence scores. The EMA trace serves as both the final semantic output and the basis for the closed-loop inference.
2

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Closed-loop inference: Once the prediction confidence exceeds a threshold. the semantic map fed back into the model as the condition to guide the generation of both depth and reflectance. By alternating between conditional and unconditional steps, SPIRAL. enables joint refinement of geometry and semantics, thereby enhancing cross cross-modal consistency.
For the second limitation, we extend all three types of metrics with semantic awareness, enabling comprehensive assessment of geometric, physical, and semantic quality in the generated LiDAR scenes. (1) For the evaluation in range-view image and Cartesian point cloud perspectives that rely on pretrained models such as RangeNet++ [40] and PointNet [47] extract learning-baad based global features, we integrate the semantic map conditional module from LiDM [48], which is originally designed for semantic-to LiDAR generation, encode semantic labels. The encoded semantic features are then concatenated with the original global features to form semantic-aware global features. (2) For the evaluation in BEV projection perspective that produces rule-based global features using 2D histograms over the xy-plane, we compute 2D histograms for each semantic category individually and then concatenate them into a unified semantic-awe aware histogram representation.
Experiments on the SemanticKITTI [3] and nuScenes [5] datasets demonstrate that Spiral achieves state-of-the-art performance in labeled LiDAR scene generation with the smallest parameter size, as depicted in and Fig. and Fig. 2 (c). Moreover, we demonstrate that Spiral-generated samples can be effectively used as synthetic data augment downstream training, which is particularly valuable for autonomous driving tasks that require large-scale training data [39,7] 70, 71J. To summarize, the key contributions of this work are follows:
We propose a novel -fate-of-the-ar semantic-aware range-view LiDAR diffusion model, SPIRAL, which jointly produces depth and reflectance images along with semantic labels.
We introduce unified evaluation metrics that comprehensively evaluate the geometric, physi- cal, and semantic quality of generated labeled LIDAR scenes.
We demonstrate the effectiveness of the generated LiDAR scenes for training segmentation models, highlighting Spiral's potential for generative data augmentation.
2
Related Works
LiDAR Generation from Range Images. LiDARGen [73] pioneers diffusion- LiDAR genera- tion by learning a score function [18] that models the log-likelihood gradient in range-view image space. Building on this foundation, LiDM [48] advances conditional generative modeling, enabling synthesis from multiple input modalities. R2DM [42] enhances unconditional LiDAR scene genera- tion through diffusion models and provides in-depth analysis of crucial components that improve generation fidelity. RangeL.DM [16] optimizes the computational efficiency to achieve real-time generation, while Tex12LiDAR [59] develops text-guided generation capabilities for semantic control. However, for semantic segmentation applications, these methods still require additional dedicated models, increasing computational cost.
Several studies investigate the generation of complete LiDAR scenes in Cartesian space, aiming to preserve geometric reconstruction accuracy [29, 43, 4, 49, 30, 57, 67. 54, 56, 45]. SemCity [29] leverages a triplane representation to model outdoor LiDAR scenes by projecting the 3D space into three orthogonal 2D planes. XCube [49] utilizes a hierarchical voxel latent diffusion model to generate large-scale scenes. [43] proposes to generate semantic LiDAR scenes using the latent DDPM without relying on intermediate image projections or coarse-to-fine multi-resolution modeling. DynamicCity [4] utilizes a VAE [22] model and a DiT-based [44] DDPM to generate large-scale, high-quality dynamic 4D scenes.
LIDAR Generation from Voxel Grids.
LiDAR Semantic Segmentation. Various approaches are proposed for LiDAR scene segmentation from different representations, including range-view [40, 1, 23, 62, 25, 71, BEV [65], voxel [8, 72, 24, 15], and multi-view fusion [19, 20, 36, 6, 46, 39, 31, 35, 17]. As a representative range-view segmentation model, RangeNet++ [40] offers real-time and efficient solution for LiDAR semantic segmentation. Cylinder3D [72] proposes a cylindrical representation that particularly well-suited for outdoor scenes, addressing the irregularity and sparsity issues in LiDAR point clouds. SPVCNN [55] serves as point-voxel fusion framework that integrates point cloud and voxel representations to leverage their complementary advantages for improved segmentation performance.
3

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(a) Unconditional Step
(b) Conditional Step
Nolsy UDAR
Progreste resressve A
8
Filter
SPIRAL
SPIRAL
no
Predicted labo. P.
Traning& inference
Open-Looped Mode
Closed-Looped Mode
Traning only
Unconditional Stop
Unconditional
Conditional Step
Inferonce only
(c) Open-/Closed-Loove Mode Switch
Figure 3: (a) Unconditional Step: Spiral takes noisy LiDAR scenes 24 input and predicts both the semantic map ii and the noise é,, where the switch AA off and B is on. (b) Conditional Step: Spiral predicts & conditioned Spiral begins in an open-loop mode with unconditional steps. Once the predicted semantic map smoothed by the progressive filter reaches high confidence, Spiral switches to a closed-loop mode that alternates between unconditional and conditional steps, enhancing cross-modal consistency.
the given semantic map y. where A is and B is off. (c) During inference,
Methodology
3.1 Preliminaries of Diffusion Models for Range-View LiDAR Generation
The diffusion models [14] generate data by simulating stochastic process. Starting from a real sample 2o q(=r). forward process gradually adds Gaussian noise over T' steps, such that the final sample approximates a standard normal distribution, ie g(ær) R NN(,, I). For range-view LiDAR generation, the sample is represented as Ip E R ##xWxx where H and W denote the height and width of the range image, and d denotes the number of modalities. In LiDARGen [73] and R2DM [42], d = 2 as both depth and reflectance images are generated, while d = 1 in LiDM [48] since it only generates depth images. The model co with parameters @ is trained to predict the noise added at an intermediate step E [1....,T), by minimizing the following objective:
L= Bi.ror [Ilo(ar, =113]
where (N(0, 7) is the random noise added during the forward process, and a denotes the conditional input, such as textual description or semantic maps y E RHXWCC where C denotes the number of categories. During inference, the model starts from a random noise sample 27 ~ N I) and iteratively denoises it to generate a novel sample zng At each step, an additional noise 1 (0, I) added on x; to diversify the final results. Building upon the vanilla DDPM, more flexible diffusion
framework [50] is proposed by introducing a continuous time variable I E [0, IJ. which enables flexible control over the number of sampling steps during inference, allowing a trade-off between generation speed and quality.
3.2 SPIRAL: Semantic-Aware Pregressive LiDAR Generation
As previously discussed, although existing range-view LiDAR generative models [73, 48, 42] have demonstrated impressive performance, they are limited to producing only depth and reflectance modalities. While LiDM [48] generates LiDAR scenes conditioned on semantic maps, it requires the semantic maps to be provided beforehand. Alternatively, two-step pipelines that first generate LiDAR scenes and then predict semantic labels suffer from low training efficiency and limited cross-modal consistency. Inspired by the insight that diffusion models serve powerful representation learners for various tasks such as classification and segmentation [2, 68, 28, 69], we propose a novel semantic-aware progressive range- view LiDAR diffusion model, named SPIRAL, as illustrated in Figure (b), to address these limitations. Spiral contains three major innovations:
Complete Semantic Awareness. The semantic awareness of Spiral contains two aspects: (1) In addition to generating depth and reflectance images, Spiral also predicts the corresponding semantic maps; (2) Spiral enables conditional generation of depth and reflectance images guided by given semantic map. It alternates between two types of steps: unconditional and conditional. To control the switching between them, we introduce two control switches. A and B, as illustrated in Figure 3.

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Switches A and B follow an exclusive-or (XOR) relationship. Their on/off states are as follows:
A = 0, B = 1; for the unconditional step, A = 1, B = 0: for the conditional step,
(2)
where O denotes the off state and I denotes the on state. During training, in the unconditional step, the Spiral with learnable parameters co, simultaneously predicts both the semantic map y/ and the noise & on the noisy LiDAR scene 2t, i.e.,
Ea Dr + E
and the corresponding training loss Cu is calculated as:
Lu = Lmoise + Leem MSF((e, + H v),
where MSE(-) denotes the mean squared error, and H-) represents the cross-entropy loss. In the conditional step, Eg takes the semantic map y as conditional input and only predicts the denoising residual:
& - ()
(5)
with the training loss Le calculated as:
Le = Luoise = MSF((E2, ét)
(6)
We use a random variable v ~ Uniform 1) determine the mode for each training step. Therefore,
C= I(p/ S vbe) + C. I[ > 4b)).
where I(-) is the indicator function and th is the ratio of training the conditional step. Empirically, we set 3. as 0.5 to balance the training of these two step types.
Progressive Semantic Predictions. During inference, Spiral predicts the semantic map de at each unconditional step. To mitigate the inherent stochasticity of the diffusion process and improve stability, we apply an exponential moving average (EMA) to obtain the smoothed predictions ÿ E HX The EMA is initialized as ýr = ÿr, and updated recursively for / T to
T a. Uc + (1 a1) Mi+1,
(8)
where denotes the EMA smoothing factor. At the end of inference, Spiral outputs not only the depth and reflectance images, but also the final smoothed semantic prediction jo-
Closed-Loop Inference. Two-step methods suffer from limited cross-modality consistency, since the predicted semantic map post hoc cannot guide the previous generation. address this issue, Spiral introduces a novel closed-loop inference mechanism, where the semantic predictions Tr are continuously fed back into the model as conditional inputs during inference. Notably, to alleviate the potential artifacts in It that could degrade the generation quality, Spiral employs a confidence-basee filtering strategy to select the reliable predictions as conditions. Specifically, during inference from step t T to 0, Spiral starts with an open-loop mode by default. However, if more than the proportion 5 of the pixels in Ze have confidence scores exceeding depicted in Figure 3 (c). For instance, setting 5 to 0.8 requires that over 80% of the pixels in yt have prediction confidence scores exceeding 0.8. Once this condition is met, Spiral enters an alternating loop between unconditional and conditional steps: (1) In the unconditional step, Spiral predicts both & and dc. (2) the conditional step. Spiral predicts é conditioned on Tt. thereby improving the consistency between To and eventually. A quantitative study on the benefits of the closed-loop mode and the effect of the confidence threshold 8 is presented in the experimental section.
switches to the closed-lood loop mode, as
Overall Architecture. Spiral adopts a 4-layer Efficient U-Net [50] as the backbone and follows the default continuous DDPM framework. Spiral takes as input the perturbed depth and reflectance images Zt, along with semantic maps y encoded as RGB images. Notably, in unconditional step, the semantic maps are replaced with zero padding to disable semantic guidance. On the output side, Spiral utilizes two heads to separately predict the diffusion residuals é, and the segmentation labels Út- Fach output branch consists of a convolutional layer followed by a sequential MI.P layer. More details about the architecture of Spiral are provided in the appendix.
5

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(a) Range-Viewe Vange--lew-Based Metrics
(b) BEV-Based Metrics
road
vegetarian
building
RungeNoree
LIDAR
Encoder
cateegary
hestogram
LIDH
Samantic Encoder
Samentic
Labels
BEV
Semantic-avan Histogram? cxxx
Figure 4: (a) Range-view based semantic-aware feature fe is constructed by concatenating the features extracted by the RangeNet++ [3] encoder and the LiDM [48] semantic encoder from the LiDAR scene x and the semantic map y. respectively. (b) BEV-based semantic- feature h" is constructed by aggregating per-category 2D histograms.
3.3 Semantic-Aware Metrics for Better LiDAR Generation Evaluations
Recent range-view LiDAR studies [73, 42, 48] assess the quality of generated scenes from three perspectives: range-view images, Cartesian point clouds, and BEV projections. (1) For the range image- and point cloud-based evaluations, they rely on pretrained models such as RangeNet++ [40] and PointNet [47] to extract learning-based features for each scene. The extracted features from the real and generated sets are then used to compute the Fréchet Range Distance (FRD) [73], Fréchet Point Cloud Distance (FPD) [42], and Maximum Mean Discrepancy (MMD), which quantify the distributional similarity between these two sets. (2) For the BEV-based evaluation, rule-based descriptions
computed for each scene using 2D histograms over the xy-plane. These fea- tures, extracted from both the real and generated sets, are then used to calculate the MMD and Lensen-Shannon Divergence (JSD). However, none of these evaluation methods take semantic labels into account. Since the "ground truth" for semantic labels on the generated scene is unavailable, standard metrics like mloU cannot be directly applied. Thereby, we propose assess the quality of the generated labeled LiDAR scenes based on semantic-aware features f and h..
Learning-based Semantic Features. For the range-view based evaluation, following LiDAR- Gen [73], we use the RangeNet++ [40] as encoder E. Additionally, we propose to use a semantic map encoder extract the semantic latent features. For we use the semantic conditional module in LiDM [48]. Given LiDAR scene x with semantic labels y, the semantic-aware features S are obtained by the concatenation of these two features, as depicted in Figure (a):
f E(x) ① G(y).
The extracted features from the real and generated sets, {f*), and { are ultimately used to compute the semantic-aware Fréchet Range Distance (S-FRD) and semantic Maximum Mean Discrepancy (S-MDD), extending FRD and MMD to incorporate semantic information. For the Cartesian- based evaluation, we adopt the same procedure extract f*, while the only difference is that the RangeNet++ [40] is replaced with PointNet [47]. Similarly, (J*), and ([²), " are used to compute the semantic-aware Fréchet Point Cloud Distance (S-FPD) and S-MMD.
Rule-based Semantic Features. The BEV-based evaluation in [73, 42] divides the xy-plane into grid of BxB bins and computes a 2D BEV histogram as the rule-based feature h E R BxB However, the spatial distribution of points belonging to different categories is obviously distinct. For instance, "road" points are typically concentrated near the region along the x-axis, while "building" and "vegetation" points tend to appear farther away. The distribution of points across different categories within a scene encodes rich semantic information and effectively reflects the overall semantic structure of the scene. Thereby, we propose to compute histograms for each category individually and aggregate them into a semantic-aware histogram he RCxBxB as depicted in Figure 4 (b). Hence, only when generated and a real scene share both similar point distributions and semantic classifications can their histograms be similar. The extracted descriptions from the real and generated sets, {h*), and {h* gg are used to compute the semantic-awee JSD (S-JDD) and S-MMD.

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4 Experiments
4.1 Experimental Setup
Datasets. We conduct an extensive experimental study on SemanticKITTI [3] and nuScenes [5] datasets and follow their official data splits. SemanticKITTI contains 23k annotated LiDAR scenes with 19 semantic classes, while nuScenes contains 28k LiDAR scenes with 16 semantic classes. During pre pro-peceessnng the LiDAR scenes are projected into range-view images of spatial resolutions 64x1024 and 32x1024. respectively. To further assess robustness, we also evaluate Spiral- based generative data augmentation the fog and wet-ground subsets of Robo3D [24], which simulate adverse weather conditions for of-iistribuutinn testing.
Details on Training & Inference. We train SPIRAL on NVIDIA A6000 GPUs with 48 GB VRAM for 300k steps using the Adam optimizer [21] with a learning rate of le-4. The training process takes ~ 36 hours. For the generative models in two-step baseline methods, including LiDARGen [73], LiDM [48], and R2DM [42], we follow the official training settings. To obtain semantic labels for the generated unlabeled LiDAR scenes, we use RangeNet++ [40] with official pretrained weights and SPVCNN+ [36], an improved implementation of SPVCNN [55] provided in UniSeg [36] codebase. During inference, the number of function evaluations (NFE) [42], i.e., the number of sampling steps, is set to 256 for both Spiral and R2DM. We also run LiDM using the DDIM [51] sampling method with 256 steps for fair comparison. LiDARGen models the denoising process with 232 noise levels and requires 5 steps per level by default, resulting in a total NFE of 1160. Following [73]. we generate 10k samples per method for evaluation.
Evaluation Metrics. We follow previous works [73, 42] to assess the quality of generated LiDAR scenes from the perspectives of range images. point clouds, and BEV projections. Importantly, we report the evaluation of the generated labeled LiDAR scenes using our newly proposed semantic- aware metrics introduced in Section 3.3, including S-FRD, S-FPD, S-MMD, and S-JSD. For all the metrics, lower values indicate better generative quality. Note that the samples generated by LiDM [48] contain only depth images, which prevents their evaluation in range- vee--veee-bed benchmarks where the RangeNet++ [16] encoder requires the reflectance channel as input.
4.2 Experimental Results
Evaluation on SemanticKITTI. We report the experimental results on the SemanticKITTI [3] dataset in Table Despite having the smallest parameter size of only 61M, Spiral achieves the best performance across all semantic-aware metrics, outperforming the two-step method, R2DM [42] & SPVCNN+ [36], by 31.03%, 56.33% and 50.94% % on S-FRD, S-FPD, and S-ISD, respectively. For the previous metrics that evaluate only the unlabeled LiDAR scenes, Spiral outperforms R2DM on most metrics, indicating that the additional semantic prediction task does not compromise the generation quality of depth and reflectance images. Surprisingly. the more advanced segmentation model SPVCNN+ performs worse than RangeNet++ on the unlabeled scenes generated by LiDAR- semantic-aware metrics. We attribute this drop to the higher sensitivity of larger models to noise, compounded by the greater noise present in the LiDAR scenes generated by LiDARGen and LiDM. Although the performance of SPVCNN+ improves after jittering- based fine-tuning, it still lags behind RangeNet++. Further discussion of this issue is provided in the appendix. The generated labeled LiDAR scenes from Spiral and other baseline methods, as shown in Figure 5, demonstrate the superior performance of Spiral in both geometric and semantic aspects.
Gen [73] and LiDM [48], resulting in inferior performance
Evaluation on nuScenes. We report the experimental results on the nuScenes [5] dataset in Table 2. Spiral consistently outperforms the other baseline methods on all metrics with the smallest parameter size. Compared with the second best method (R2DM [42] & RangeNet++ [40]), Spiral achieves improvements of 49.03% %. 67.84,, and 46.79% on S-FRD, S-FPD, and S-JSD, respectively. Figure 6 presents the generated scenes from Spiral and the baseline methods for qualitative comparison, showing the superior performance of Spiral in both geometric and semantic aspects.
Generative data augmentation. We evaluate the effectiveness of using Spiral's generated samples to augment the training set for segmentation learning on SemanticKITTI [3]. Using SPVCNN+ [36] as the segmentation backbone, we compare Spiral with R2DM [42] RangeNet++ [40] under different ratios of available real labeled data. As shown in the first row of Table 3, the generated samples
7

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Table Comparisons with state-of-the-art LiDAR generation models on the SemanticKITTI [3]
dataset. We evaluate methods using the Range View, Cartesian, and BEV representations. Symbols
i and $ denote the RangeNet++ [40] backbone and the SPVCNN+ [36] backbone, respectively.
The parameter size includes both the generative and segmentation models. The best and second best scores under each metric are highlighted bold and underline.
Range View
Cartesian
Method
(M)
FRDN
MMDD
S FRD]
S MMD
FPD1
MMD.
MMD)
JSDJ
MMD]
JSDJ
MMD)
(x1077)
(x3)
(x30")
(x10"")
(x10")
(x307")
(x1077)
(x1o"")
(xtor")
LIDARGes! [13]
LIDARGea [73]
30+57
30+50
1100
651.37
47.87
121661
30.65
61.42
130.17
0837
1396.17
710,79
103.12
52.71
13.23
2.19
28.63
10,95
LIDM [38]
276-50
255
108.70
13.87
158.55
28.00
4.56
0.29
10.60
6.00
LIDM
275+97
6.23
R20M [42]
31+50
206
192.81
4.03
$30.00
11.50
10:20
2.30
15.00
9.73
0.18
18:13
3.91
820M
31+97
305.00
11:50
13:00
18.67
SPIRAL (Uam)
$92.87
1.10
3.70
0.16
9.10
Ground
Spiral (Oun))
R2DM (CRA'24)
LIDM (CVPR/24
9
car bayy
1 moto true o.vh pod b.iss milst road
park walk oggoo
build fenc C vog trun Bar
polo + sign
Figure 5: Visualizations of generated LiDAR scenes on SemanticKITTI [3]. For two-step methods, we use the labels produced by RangeNet++ [40] due to its superior performance over SPVCNN++ [36].
Artifacts are highlighted with dashed boxes. Examples of semantic artifacts are shown in ① x and O, while geometric artifacts such as local distortion and large noise are illustrated in 10) and 1..
from Spiral consistently improve the performance of SPVCNN+ and outperform those from R2DM. Detailed per-category results are provided in the appendix. Additionally, we evaluate SPVCNN+ under the same settings on out-of distribbutioon subsets, fog and wet-ground, from Robo3D [24]. As shown in the second and third rows of Table 3, although Spiral is not fine-tuned for such extreme weather conditions, its generated data still enriches the training set and improves performance under these challenging scenarios.
4.3 Experimental Analysis
Impact of the NFE. Intuitively, increasing the number of function evaluations (NFE) improves the quality of the generated samples but results linear increase inference cost. We eval- uate the performance of Spiral under different NFE settings in (32, 64, 128, 256, 512, 1024) on SemanticKITTI [3]. The results shown in Figure 7 indicate that Spiral's performance improves significantly when NFE < 256, while further increases in NFE yield only marginal gains most metrics. Therefore, we set NFE = 256 as the default configuration. On an A6000 GPU, Spiral achieves average inference speed of 5.7 seconds per sample.
Closed-Loop vs. Open Inference. Closed-loop inference a key innovation in Spiral that enhances mooas-mo consistency. To quantify the impact of closed-loop inference, we disable the feedback of the predicted semantic map to Spiral as conditional input, maintaining the open-loop inference throughout the whole generative process. The experimental results on SemanticK/TTI [3], as listed in the first row of Table 4, show that adopting the open-loop setting leads to performance drop of Spiral on all metrics, e.g., drop of 3. 3.34% and 8. 8.68% on S-FRD and S-FPD, respectively.
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Table 2: Comparisons with state-of-the-art LiDAR generation models on the nuScenes [5] dataset.
We evaluate methods using the Range View, Cartesian, and BEV representations. Symbols i and & denote the RangeNet++ [40] backbone and the SPVCNN+ [36] backbone, respectively. The parameter size includes both the generative and segmentation models. The best and second best scores under each metric are highlighted bold and underline.
View
Cartesian
Method
(MF
FRDD
MMDD (x10"")
S FRD]
5 MMD.
FPD]
MMD.
(PD)
MMD)
JSDJ
MMD]
JSDJ
MMD) (xtom")
(x10")
(x10"")
(x10"))
6x3077)
(x1o7")
LIDARGea [73]
30+50
1100
188.80
3.03
1381.42
20.63
06.52
12335
1890.83
92.31
5.98
0.15
37.37
16.21
50.20
LIDM [38]
276-50
255
124.55
90.14
1472.81
120.65
4.00
0.29
27.50
11.75
LIDM
275+97
[17]
31+50
206
197.02
2.06
971.25
10.13
16.80
1.81
805.33
25.30
3.12
0.05
17:14
4.94
820M
31+97
1308
25.27
1198.16
100-38
(0am)
3.10
Spiral [Oun]
R2DM ICRA/241
LiDM (CVPR224)
car nicy O moto truc c.flat pad bus co.vch bart traffic cone
tralier side torrain
vex manmade driveable surf.
Figure 6: Visualizations of generated LiDAR scenes nuScenes [5]. For two-step methods, use the labels produced by RangeNet++ [40] due to its superior performance over SPVCNN+ [36]. Artifacts are highlighted with dashed boxes. Examples of semantic artifacts are shown in x ① and
2 while geometric artifacts such as local distortion and large noise are illustrated in ? and (i)
Impact of the Confidence Threshold in Closed-loop Inference. the closed-loop mode. Spiral adopts a confidence- filtering strategy to exclude unreliable semantic maps that frequently occur during the early stages the denoising process. To quantify the effect of the confidence threshold we evaluate the performance of Spiral under different 8 settings in {0.3, 0.5, 0.6, 0.7, 0.8, 0.9}. The results listed in Table indicate that Spiral performs well when 8 E (0.7, 0.8, 0.9) and achieves slightly best performance at = 0.8. Therefore, we set 8 0.8 the default configuration of Spiral. However, the performance Spiral starts to deteriorate when < 0.6. With d 0.3, the performance of the closed-loop inference even falls behind that of the open-loop inference.
Table 3: Generative Data Augmentation (GDA) for segmentation training. We assess GDA
using synthetic samples from R2DM [42] and Spiral, under different ratios (1 (1%, 10%, 20%) of real
labeled data from SemanticKITTI [3], as well as fog and wet-ground scenes from Robo3D [24].
Symbol denotes using RangeNet++ as the segmentation model. Results are reported in mloU (%).
Setting
1%
10%
20%
GDA
w/o
R2DM [42]
SPIRAL.
w/a
R2DM [42]
SPIRAL
w/a
R2DM [42]
SPIRAL
SemanticKITTI
37.76
44.16
47.41
59.07
60.62
61.14
61.16
61.21
62.35
Robo3D (fog)
32.07
36.82
44.06
53.93
54.20
58.61
55.89
56.07
61.24
Robo3D (wet-ground)
34.26
54.70
57.53

---

S-FRD
S-MMD
S-FPD (cntession) R2OM
S-MMD
S-JSD
S-MMD
642
12ac
(corteslon)
PERKEAARAA R2DM
4Paiot
K2DM
7eato
RIDM
154
06412
1axc
0 512
1024
512
1024
0
512
1024
0
512
1024
0
512
1024
512
1024
(a)
(h)
(d)
(c)
(f)
Figure 7: Impact of the number of function evaluations (NFE), Increasing the NFE (32, 64, 128, 256, 512, and 1024) improves the performance across all metrics. With fewer sampling steps, Spiral outperforms R2DM [42] using its default setting of NFE = 256, indicated by the dashed line. Note: The x-axis denotes NFE, and the y-axis denotes the evaluation metric (lower is better).
Table 4: Impact of the closed-loop inference and confidence threshold. The best and second best scores under each metric are highlighted in bold and underline. The highlighted row indicates the default configuration of Spiral.
Range View
Cartesion
BEV
Confiltence
Close-Joop
Threshold 5
FRD
MMD]
S-RRD]
S-MMDL (x10P)
FPDJ
MMD]
S-FPIJ
S-MMDL
JSD.
MMDJ
S-JD]
S. S-MMD, (x10%)
(xl)
(x109)
(x1)
(x.1
(x:00
(x.1FF)
(x.10P)
(x10F)
173.93
395.61
1.82
100.95
9.29
y
190.45
5.56
461.31
6,06
47.87
17.97
265.79
17.65
11.31
2.51
0.5
174.01
5.15
396.71
4.67
22.31
6.81
187.90
6.51
3.96
0.16
9.55
1.71
y
173.25
392.36
2.59
2.87
9.40
1.30
170.93
1.87
385.05
4.17
8.52
1,80
161.47
3.77
3.60
0.15
0.22
1.50
0.8
170.18
4.81
3K2.87
4.10
8.06
1.10
153 61
3.20
3.78
0.15
9.16
1.41
170.22
381.42
1,22
106.28
8.27
9:18
1.42
Open-/Closed-Lopg Mode Switching Point. We performed statistical analysis on 1,000 samples each from SemanticKITTI and nuScenes. With the default 256 denoising steps, closed-loop inference activated (i.e., once 2 80% of semantic predictions exceed the confidence threshold) at an average step of 1800=36 on SemanticKITTI and 189 39 on nuScenes. This indicates that switching typically occurs during the last 30% of the generation process, when semantic predictions have stabilized, thereby avoiding early-stage noise and ensuring reliable semantic-geomett alignment.
Inference Efficiency. We report the average sam- pling time per sample for LiDARGen [73], LiDM [48]. R2DM [42], and SPIRAL on an A6000 GPU in Ta- ble 5. Additionally, the inference times per sample for
Table 5: Average sampling time per
sample of LiDARGen [73], LiDM [48], R2DM [42], and Spiral on an Nvidia A6000 GPU.
RangeNet++ [40] and SPVCNN+ [36] are 0.08s and
0.05s respectively on the same hardware. Unlike the two-step methods, Spiral does not require segmentation model to generate semantic labels. Spiral demonstrates superior inference efficiency compared to LiDM and Li- DARGen. Although it is approximately 2.05 slower than R2DM combined with SPVCNN+ when using the same number of generation steps. Spiral remains 0.75 faster with 128 steps and achieves higher generative quality.
Method
NFE
Average Time
LiDARGen [73]
1160
72.0
LiDM [48]
256
R2DM [42]
256
3.6
SPIRAL
SPIRAL
128
2.9
Conclusion
We present SPIRAL, the first semantic-aware range-view LiDAR diffusion model thinttyltly generates depth, reflectance, and semantic labels in unified framework. We further introduce novel semantic- aware evaluation metrics, enabling holistic assessment of generative quality. Through extensive evaluations on SemanticKITTI and nuScenes, Spiral achieves state-of-the-aar performance across multiple geometric and semantic-an aware metrics with minimal model size. Additionally, Spiral demonstrates strong utility in downstream segmentation via generative data augmentation, reducing the reliance on manual annotations. We believe Spiral offers a new perspective on scene generation and opens up promising directions for scalable, label-efficient 3D perception.
LiDAR
10

---

Acknowledgments
The authors would like to sincerely thank the Program Chairs, Area Chairs, and Reviewers for the time and effort devoted during the review process.
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Appendix
Evaluation Metries
22
6.1
Evaluation on Range View and Cartesian Point Clouds
22
6.2
Evaluation on Bird's Eye View
23
7
Data Preprocessing
23
7.1
Rescaling of the Depth and Reflectance Remission Channels
23
7.2
Semantic Encoding
23
8
Model Architecture Details
24
8.1
Spatial Feature Encoding
24
8.2
Temporal Feature Encoding
24
Discussions of SoTA Segmentation Models in Two-Step LiDAR Generation
24
9.1
Experimental Setup
24
9.2
Observations and Analyses
24
10 Additional Qualitative Results
24
10.1
Visualizations of Reflectance Remission Images
24
10.2 Visualizations of Generated LiDAR Scenes
25
11 Generative Data Augmentation
25
11.1 Full-supervision Setup
25
11.2 Semi-supervision Setup
25
12 Broader Impact & Limitations
29
12.1 Broader Impact
29
12.2 Societal Influence
30
12.3 Potential Limitations
30
13 Public Resource Used
13.1 Public Datasets Used
30
13.2 Public Implementations Used
31
Evaluation Metries
In the main paper, we evaluate the generative quality of Spiral for the LiDAR scene æ with semantic map y from three perspectives: range-view images, Cartesian point clouds, and BEV projections. Here, we elaborate on the details of evaluations on each representation.
6.1 Evaluation on Range View and Cartesian Point Clouds
For range-view image-based and point cloud-based evaluations, we extract a unified semantic- aware feature fa by concatenating the geometric feature E(x), extracted by RangeNet++ [40] or
22

---

PointNet [47], with the semantic feature (gy) from the conditional module in LiDM [48]:
f - (()) @ ((v).
(10)
The features from real and generated sets, {f fr and {f*) are used compute S-FRD, S-FPD, and S-MMD. The calculation formula for S-FRD is as follows:
S-FRD ||,, 1gll Tr Er + -2 - ((EE,
(11)
where Hy and μt, are the mean of }r and { is the matrix trace. The calculation of S-FPD follows the same formula. For S-MMD, it can be measured through the kernel trick:
g. E, and Eg are the covariance matrices, and Tr(-)
N ΣΣ
N M ΣΣ NM
M ΣV 5'
2
S-MMD N2
M
k0),)f))
(12)
M
where N and M are the number of samples in the real and generated sets, respectively. Following [42]. we use 3rd-order polynomial kernel function:
k(f", = (8*)
+ 1)s
(13)
d1
where d is the dimension f
6.2 Evaluation on Bird's Eye View
For the BEV-based evaluation, first compute the semantic- histogram for the real and gener- ated sets, {h*}, Jr and {h*] and then compute the BEV-based S-JSD and S-MMD. The calculation of
S-JSD is follows:
ISD(PIIQ) -K11 + +
(Q1I)M
(14)
where P and are the approximation of Gausian distribution (h*), and {h*) h and M is the mean of them: M 1 (P Q)
7 Data Preprocessing
In this section, we provide more details regarding data preprocessing. For both SemanticKITTI [3] and nuScenes [5], we first project the raw point cloud to range-view images including the depth. reflectance remission, and semantic channels. Then we rescale the depth and reflectance remission channels and encode the semantic maps to RGB images.
7.1 Rescaling of the Depth and Reflectance Remission Channels
For the depth channel, we first convert the range-view depth images x to log-scale representation olgg as follows:
log(2" + +1)
Flog log( xrd 1)
(15)
where nd is the maximum depth value. Then we linearly rescale the reflectance remission and log-scale depth images to [-1, 1].
7.2 Semantic Encoding
For the semantic channels, we use the official color scheme convert the one-hot semantic map E IR #xxxx xC' into 2D RGB images, where C' is the number of categories. Notably, Spiral is trained
both conditional and unconditional modes, where RGB images in the unconditional mode are
replaced with zero padding. To distinguish between zero-padded images from the unconditional mode and the black regions in semantic images from the conditional mode, we add an additional channel to indicate whether semantic map provided.
23

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8 Model Architecture Details
8.1 Spatial Feature Encoding
We adopt a 4-layer Efficient U-Net [50] as the backbone of Spiral, with intermediate feature di- mensions of 128, 256, 384, and 640, respectively. Each layer consists of three residual blocks and downsamples the spatial resolution by factor of two along both the row and column dimensions, The prediction heads for generative residual and semantic predictions are composed of 2D convolutional layer followed by two-layer MLP.
8.2 Temporal Feature Encoding
For temporal and coordinate encoding, follow the same strategy R2DM [42]. For coordinate encoding. we map the per-pixel azimuth and elevation angles 32-dimensional Fourier features [53], which are then concatenated with the input data. Specifically, the diffusion timestep is encoded to 256-dimensional sinusoidal positional embedding, which then integrated by the adaptive group normalization (AdaGN) [9] modules in each layer.
Discussions of SoTA Segmentation Models in Two-Step LiDAR Generation
In the two-step methods, we report results using both RangeNet++ [40] and SPVCNN+ [36] as segmentation backbones in the main paper. Although SPVCNN++ outperforms RangeNet++ real-world datasets, it performs worse on the generated LiDAR scenes of LiDARGen [73] and LiDM [48]. In this section, we further present the performance of RangeNet++, SPVCNN+ and state-of-the-art segmentation model, RingeViT [1], on the generated LiDAR scenes of LiDARGen. As we observe that larger-scur scale jittering can improve SPVCNN++'S robstnnes,, we train all models with both the default jittering and an increased jittering scale.
9.1 Experimental Setup
We the official implementation of all models. In the default setting, RangeNet++ and Range ViT are trained without jittering augmentation, while SPVCNN+ trained with the Gaussian noise jittering, where a 0.1. In the setting of larger-scale jittering, increase the of jittering to 0.3 across all models.
9.2 Observations and Analyses
Detailed results of RangeNet++, SPVCNN++, and RiT under different jittering scales are presented Table 6. Although fine-tuning SPVCNN+ and RiT with stronger jittering (a 0.3) improves its performance, they still lag behind RangeNet++, while RangeNet++ presents the best robustness on the generated samples. These experimental results indicate that segmentation models achieving high performance generated scenes. This highlights the limitation using predictions from of-t----thhrrr segmentation models pseudo "ground truth" for evaluating generated scenes. Instead, measuring the distributional similarity of semantic-aware features between real and generated scenes provides a more reliable assessment of the quality of predicted semantic maps, proposed in this work.
"clean" real-world scenes do not necessarily perform well on
10
Additional Qualitative Results
In this section, we present additional visualizations the generated reflectance remission images, along with more examples of generated LiDAR scenes and the corresponding semantic maps.
10.1 Visualizations of Reflectance Remission Images
We visualize the generated reflectance remission images on SemanticKITTI [3] in Figure 8. Since LiDM does not produce reflectance remission images, we only present results from Spiral, Li- DARGen [73], and R2DM [42]. Among these, Spiral demonstrates strong cross-modal consistency across depth, reflectance intensity, and semantic labels. In contrast, scenes generated by LiDARGen

---

Table 6: Performance RangeNet++ [40], SPVCNN+ [36], and RangeViT [1] trained with different jittering scales on the SemanticKITTI [3] dataset.
Range View
Cartesian
REV
Generative Model
Segmentation Backbone
Jittering
S-FRD]
S-MMDJ
S-FPD].
S-MMDL
S-ISDL
S-MMD
Scale
(x1)
(x10
(x1)
(x107))
(x10 9)
(x10-
default
1216.61
35.65
710.79
52.71
28.65
10.96
RangeNett+ [40]
large
1202.45
34.97
702.38
50.03
28.49
10.85
default
1978.13
70.33
1826.54
210.67
56.40
68.97
LiDARGen [73]
SPYCNN++ [36]
large
1708.05
61.42
1366.17
103.12
54.84
68.16
default
2034.15
72.09
1726.54
195.44
55,20
67.47
RangeVil
large
1891.42
70.17
1625:27
187.02
54.13
65.09
and R2DM often exhibit artifacts and inconsistencies across modalities, as highlighted by the red bounding boxes.
10.2 Visualizations of Generated LiDAR Scenes
demonstrate more generated LiDAR scenes in SemanticKITTI [3] and nuScenes [5] Figure
and Figure 10, respectively. The geometric and semantic artifacts of the generated LiDAR scenes highlighted by dashed boxes.
11 Generative Data Augmentation
11.1 Full-supervision Setup
In Table 7, we report the experimental results of using the samples generated by two-step method (RangeNet++ [40] & R2DM [42]) and Spiral for generative data augmentation in the segmentation training of SPVCNN+ [36] on SemanticKITTI [3] dataset. We train SPVCNN+ using full- supervision method. Besides different ratios (1%, 10%, 20%) of available real labeled data, we extend the real data subsets with the generated samples (10k). The results show that the generated samples from Spiral consistently improve the performance of SPVCNN+ and outperform those from R2DM.
11.2 Semi-supervision Setup
Two switches in Spiral, AA and B, provide high flexibility alternate between the unconditional and conditional modes. In fully-supervised training, where all imput samples have semantic labels, A and 3 operate in an exclusive-or (X-OR) manner: (1) in the unconditional mode, A is off and B is
(2) in the conditional mode, AA is on and is off. When both switches are turned off (AA = 0 and B = 0), Spiral degenerates into normal unconditional LiDAR generative model, i.e., non-labeled training. Specifically, when only a small fraction of training samples are labeled (e.g., 10%). Spiral is trained under labeled data using the conditional or unconditional modes described in the main paper and is trained with the remaining unlabeled samples under the non-labeled mode.
mode. This design enables Spiral to support semi-supervee
We train Spiral on a training set where only 10% of the samples are labeled, with the remaining samples unlabeled. For the two-step bascline methods, the generative model is trained the full training set, while the segmentation models are trained solely on the labeled 10% subset. The results presented in Table 8 show that the generative performance of Spiral consistently outperforms other baseline two-step methods in this case.
Additionally, we leverage the samples generated by Spiral, trained under the semi-supervised setting, to augment the labeled training set within the state-of-the-a semi-supervised LiDAR segmentation framework, LaserMix [25]. We adopt Mink [8] and FIDNet [66] as the segmentation backbones. The experimental results Table 9 demonstrate that incorporating Spiral's generated samples further improves the performance of both Mink and FIDNet within the LaserMix framework.
25

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Ground Truth
Ronge Imagi
Intersityy
Label
Ronge Imegle
Intersiity
Label
Spiral (Ours)
Range image
Intercity
Peeudo Label
Range Imaga
Intersity
Pacudo Label
LIDARGen (ECCV'22)
Range image
Intensity
Range Imago
Intersity
Lebel
R2DM (ICRA'24)
Range
Intersity
Pscudo Label
Range Imagie
Intensity
Pacudo Label
bicy
ped
road
walk
augro
build
fenc
veg
trun
terr
pole
sign
Figure 8: Visualizations of generated depth, reflectance remission, and semantic labels in range- view perspective on SemanticKITTI [3]. Generation artifacts and cross cross-modal modal inconsistencies are highlighted by the bounding boxes.
26

---

Ground Truth
Spiral (Ours)
LIDARGen (ECCV22)
LIDM (CVPR224)
R2DM (ICRA/24)
blay
mato
suc
nush
ped
blist
milis
rad
park
ogo
pole sign
Figure 9: Visualizations of generated LiDAR scenes SemanticKITTI [3].
27

---

Ground Truth
Spiral (Ours)
LiDARGen (ECOV'22)
LIDM (CVPR'24)
R2DM (CRA24)
CDr
bicy
mata
truc
offat
ped
bus
coweh
barr
traffic cone
trailer
side
veg
manmade
driveable surt.
Figure 10: Visualizations of generated LiDAR scenes on nuScenes [5].
28

---

Table 7: Generative Data Augmentation (GDA) for segmentation training. We evaluate the effectiveness of GDA using synthetic samples generated by R2DM [42] & RangeNet++ [40] and Spiral, under different ratios (1%, 10%, 20%) of available real labeled data. Symbol f indicates that RangeNet++ [40] is used as the segmentation backbone.
Setting
10%
20%
(GDA) None
R2DM
SPIRAL
None
R2DM
SPIRAL
None
R2DM
SPIRAL
mloU
37.76
44.16
47.41
59.07
60.62
61.14
61.16
61.21
62.35
cur
89.82
87.65
92.87
94.52
94.53
95.13
95.78
94.52
96.03
bicycle
0.00
0.00
12.67
25.04
43.23
31.57
25.92
42.62
34.07
motorcycle
13,98
23.36
25.68
62.78
55.99
55.47
66.22
58.98
58.50
truck
24.15
7.93
47.92
52.89
49.40
60.19
50.53
48.44
57.82
other-vehicle
15.50
29.60
33.71
42.06
52.30
53.58
55.85
51.19
55.44
person
25.82
28.56
26.18
68.32
60.25
61.87
69.48
65.98
73.85
bicyclist
0.00
53.54
0.00
82.72
77.55
88.02
83.32
85.20
motorcyelist
1.97
0.00
0.20
0.00
0.26
0.00
0.00
1.52
0.00
road
78.37
86.14
92.79
91.59
92.55
93.23
92.95
92.64
93.42
per-class mloU
parking
15.41
18.99
51.05
38.99
43.49
47.73
43.61
43.03
47.94
sidewalk
60.78
70.56
79.61
77.87
79.90
80.62
79.96
79.72
80.52
other-ground
0.01
0.25
0.67
2.09
1.30
2.11
3.31
1.40
building
79.20
86.93
88.38
88.58
90.40
90.52
89.87
90.13
90.03
fence
32.62
52.14
53.14
56.13
62.22
61.82
59.80
60.77
60.45
vegetation
83.62
86.28
88.07
87.97
89.49
89.82
88.35
89.21
88.69
trunk 52.78
46.19
61.62
66.11
66.58
68.27
66.29
66.69
68.51
terrain 69.38
72.84
77.67
74.97
78.36
78.28
75.63
75.34
pole
54.32
44.96
54.26
63.03
61.80
62.81
64.27
62.12
63.85
trafficsiggn
19.77
43.12
14.21
51.44
47.39
53.66
Table 8: Semi-supervised training of Spiral and the baseline two-step methods on the Se- manticKITTI [3] dataset. We evaluate methods using the Range View, Cartesian, and BEV represen- tations. Symbols & denotes the RangeNet++ [40] trained with 10% labeled samples. The parameter size includes both the generative and segmentation models.
Range View
Cartesian
BEV
Param (M)
Method
NFE
S-FRD]
S-MMDJ
S-FPD]
S-MMDJ
S-JSDL
S-MMDJ
x10
x1
x
x10
x10
LiDARGen! [73]
30+50
1160
1285.55
47.56
750.68
58.26
34.49
12.66
LiDM [48]
275+50
256
486.22
29.78
16.91
6.33
R2DM [42]
31+50
256
528.82
7.82
281.96
8.95
16.13
2.99
SPIRAL
61
256
508.86
5.77
211.97
4.79
11.47
2.65
12
Broader Impact & Limitations
In this section, we elaborate on the broader impact, societal influence, and potential limitations of the proposed approach.
12.1 Broader Impact
This work presents several significant implications for both academic research and practical ap- plications in autonomous driving and computer vision. SPIRAL advances the field of LiDAR scene generation by introducing unified framework for joint depth, reflectance, and semantic label generation. This breakthrough could substantially impact:
1. Research Community
29

---

Table 9:
Generative Data Augmentation (GDA) for semi-supervised training in the Laser-
Mix [25] framework. We evaluate the effectiveness of GDA using synthetic samples generated by Spiral in the semi-supervised training framework LaserMix.
Backbone
MinkUNet [8]
FIDNet [66]
LaserMix +SPIRAL
LaserMix
Method
sup. only
LaserMix [25]
sup. only
LaserMix [25]
+SPIRAL
mloU
64.0
66.6
67.2
52.2
60.1
60.3
Establishes new benchmarks for semantic-aware LiDAR generation
Provides novel framework for multi modal scene understanding
Opens new research directions in 3D scene generation and understanding
Industry Applications
Reduces the dependency on expensive and time-consuming manual data annotation
Enables efficient development of autonomous driving systems
Provides cost-effective solution for synthetic data generation
12.2 Societal Influence
The development of SPIRAL could lead to several positive socictal outcomes:
1. Transportation Safety
Enhanced autonomous vehicle perception capabilities through better training data
Potential reduction in traffic accidents through improved scene understanding
2. Economic Impact
Reduced development costs for data collection
Accelerated deployment self-driving vehicles
12.3 Potential Limitations
While SPIRAL demonstrates promising results, several limitations and challenges should be acknowl- edged:
1. Technical Constraints: The quality of generated scenes may still have room for improvement.
2. Methodological Limitations: Generated data may not fully capture all real-world complexities.
Implementation Challenges: Real-time performance considerations for practical applications.
This work represents significant step forward in semantic-aware LiDAR scene generation while acknowledging the need for continued research to address these limitations and challenges. Future work could focus on enhancing the model's capabilities in handling complex scenarios and improving computational efficiency while maintaining generation quality.
13 Public Resource Used
In this section, we acknowledge the use of the public resources, during the course of this work:
13.1
Public Datasets Used
nuScenes"
CC CCBN-C--SA 4.0
hattps://ww.nusee www.noscenee....eeeeoooooooeeeee
30

---

nuScenes-Devie
Apache License 2.0
SemanticKITTII
CC BY-NC-SA 4.0
SemanticKITTI-AP
MIT License CC BY-NC-SA 4.0
Robo3D
13.2
Public Implementations Used
MMDetcction
Apache License 2.0
MMMDttcttion3
Apache License 2.0
RangeNet++
MIT License
OpenPCSeg 10
Apache License 2.0
RangeViT
Apache License 2.0
LiDARGen
MIT License
LIDM
MIT License
R2DM
MIT License
hccp://eemaniic--tttti......
org.
31


Reviews:
[{"review_id": "0XG0DyNwfN", "reviewer": "Reviewer_XYPz", "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.", "questions": "All of my concerns are listed in the weaknesses section, and I may adjust the rating if they are well addressed.", "limitations": "Yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1) 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.\n2) Semantic-aware metrics are introduced for a comprehensive evaluation, and the numerical results are good when compared to baselines.\n3) The paper is well-written, the problem is clearly specified and the solution is well presented.\n\nWeaknesses:\n1) Limited semantic label set: Semantic output is constrained to pre-defined categories from SemanticKITTI and nuScenes, reducing applicability to varied domains. \n2) 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.\n3) 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.", "was_revised": true, "final_justification": "I would like to thank the authors for addressing my questions point-by-point. While the rebuttal resolves some of my concerns, I still have the concern regarding the practical utility of the generated data. Therefore, I am inclined to maintain my original score.", "rebuttal": "We sincerely thank Reviewer `XYPz` for the thoughtful and constructive feedback. We are encouraged that you found our work “useful,” “well-written”, and that our results demonstrate “good” performance. Below, we address your key concerns in detail.\n\n---\n>**`Q1`:** *\"Limited semantic label set: semantic output is constrained to pre-defined categories from SemanticKITTI and nuScenes, reducing applicability to varied domains.\"*\n\n**A:** Thanks for your question. Our current design follows a widely accepted convention in 3D perception benchmarks, where tasks such as segmentation and detection rely on **fixed taxonomies**, as in SemanticKITTI and nuScenes. These datasets are **representative and diverse**, encompassing a broad range of scenes (urban, residential, highway, rural) across different geographic regions (Germany, Singapore, USA), and have become de facto standards in the field.\n\nWhile constrained to pre-defined classes, these benchmarks include out-of-distribution (OOD) labels, such as the “unlabeled” category in SemanticKITTI (e.g., tunnel walls, scaffolding, bus stops). Spiral is capable of modeling such categories as well, ensuring that OOD content is not omitted in our generative process.\n\nWe believe that extending Spiral to support an unlimited number of categories is a **meaningful direction**. However, it faces a critical bottleneck: the **lack of reliable text–LiDAR paired data**, as also observed by *LiDM* [`R1`] and *Text2LiDAR* [`R2`].\nAlthough *Text2LiDAR* [`R2`] introduces language annotations, they are typically **coarse, scene-level descriptions** (e.g., *\"cars and buses at a busy street\"*, *\"surrounded by cars\"*) and lack **fine-grained, point-level semantics** that are essential for point-wise labeled LiDAR scene generation. This makes open-vocabulary generation technically under-constrained. Addressing this challenge is a key goal in our ongoing research. Thanks again for your valuable suggestion.\n\n---\n> **`Q2`:** *\"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.\"*\n\n**A:** Thank you for highlighting this. We acknowledge the class imbalance present in both SemanticKITTI and nuScenes. To address this, Spiral incorporates a **class-reweighting** strategy, consistent with the official RangeNet++ codebase. \n\nSpecifically, the loss weight for category $i$ is defined as:\n$$\nw_i = \\frac{1}{\\text{freq}_i + \\epsilon},\n$$\nwhere $\\text{freq}_i$ is the normalized frequency of category $i$, and $\\epsilon$ is a small constant (default: $0.001$). \n\nThis strategy mitigates the dominance of frequent classes and encourages the model to attend to **under-represented semantic categories** during training. As suggested, we have further clarified this detail in the revised manuscript.\n\n---\n> **`Q3`:** *\"Although the approach reduces collection and 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.\"*\n\n**A:** We appreciate this concern. To evaluate how well Spiral captures **real-world variability** and how robust it is to **real-world complexity**, we conduct experiments on generative data augmentation (GDA) under **two challenging scenarios**:\n1. **Cross-Domain Generalization (Waymo Test):**\n    - We use LiDAR scenes generated by Spiral, trained on SemanticKITTI, to augment the training set of Waymo [`R4`] (when 10% of real data is available), and evaluate the model's performance on the Waymo val set. \n    - The segmentation model SPVCNN++ is trained under three settings:\n      1. without GDA,\n      2. with synthetic data generated by R2DM & RangeNet++,\n      3. with our Spiral-generated data. \n    - The segmentation performance (mIoU) under these settings is **55.89%**, **55.93%**, and **57.52%**, respectively. These results demonstrate the **strong generalization ability** of our Spiral-based GDA approach.\n\n2. **Out-of-Distribution (OOD) Extreme Weather (Robo3D Test):**\n    - We evaluate SPVCNN++ trained under the same three settings as in the above \"Cross-Domain Generalization Test\", on challenging out-of-distribution subsets, `fog` and `wet ground`, from *Robo3D* [`R3`]. \n    - The results demonstrate that, although Spiral is not specifically fine-tuned for these OOD extreme weather conditions, the data generated by Spiral still **effectively enriches the training set** and **improves performance** under these challenging scenarios.\n\n> ***Table.** Experiments under `fog` scenarios:*\n| Ratio of Real Data | w/o GDA | R2DM & RangeNet++ | Spiral (Ours) |\n|:---:|:---:|:---:|:---:|\n| 1% | 32.07 | 36.82 | **44.06** |\n| 10% | 53.93 | 54.20 | **58.61** |\n| 20% | 55.89 | 56.07 | **61.24** |\n\n> ***Table.** Experiments under `wet ground` scenarios:*\n| Ratio of Real Data | w/o GDA | R2DM & RangeNet++ | Spiral (Ours) |\n|:---:|:---:|:---:|:---:|\n| 1% | 34.26 | 37.96 | **44.19** |\n| 10% | 54.70 | 55.92 | **59.31** |\n| 20% | 56.81 | 57.53 | **62.02** |\n\nThese results confirm that Spiral generates **semantically and structurally diverse scenes** that enhance real-world performance under both domain and weather shifts — supporting its practical utility.\n\n---\n**References:**\n- [`R1`] Ran, Haoxi, et al. \"Towards Realistic Scene Generation with LiDAR Diffusion Models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.\n- [`R2`] Wu, Yang, et al. \"Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer.\" European Conference on Computer Vision, 2024.\n- [`R3`] Kong, Lingdong, et al. \"Robo3D: Towards Robust and Reliable 3D Perception against Corruptions.\" Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.\n- [`R4`] Sun, Pei, et al. \"Scalability in Perception for Autonomous Driving: Waymo Open Dataset.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.\n\n---\nLast but not least, we would like to sincerely thank Reviewer `XYPz` again for the valuable time and constructive feedback provided during this review."}, {"review_id": "Fbru31vYbt", "reviewer": "Reviewer_gApr", "summary": "The 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.", "questions": "- 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%?", "limitations": "The authors included limitations and societal impact in the appendix.\n\nSee questions for weakness.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "* 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", "was_revised": true, "final_justification": "I think the proposed data synthetic method makes sense, and in the rebuttal, the authors addressed my concern about runtime and applications. Unlike other reviewers, I don't think segmentation is utterly insignificant, and the author did show that the synthetic data improves the models, which could be a good application.\n\nOn the other hand, the improvement is still quite marginal, and only a percentage is provided. This could potentially be an indication of the diversity of synthesized data. The paper could benefit from a wider range of experiment on data augmentation.\n\nThus, I remain my score as 4, weakly accept", "rebuttal": "We sincerely thank Reviewer `gApr` for the thoughtful and constructive feedback. We are encouraged that you found our closed-loop inference design both “novel” and “reasonable,” and appreciated our improvements in semantic-scene alignment. Below, we address each of your concerns in detail.\n\n---\n> **`Q1`:** *\"What is the memory and runtime compared to your baselines?\"*\n\n**A:** Thank you for this practical question. The parameter sizes of Spiral and baselines are reported in the “Param” column of Table `1` and Table `2` in the manuscript. The **average inference time** per LiDAR scene is presented in Table `B` and Section `F` of the supplementary material. Specifically, Spiral takes **5.7 seconds per scene** on an NVIDIA A6000 GPU, which is **comparable to or faster** than two-stage baselines, thanks to its streamlined end-to-end generation pipeline.\n\n---\n> **`Q2`:** *\"The proposed S-x metrics compare 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, a garbage scene with perfect segmentation would still pass the metric. This might over-weight scene generation in the evaluation.\"*\n\n**A:** We appreciate this insightful concern. In practice, we find that the scenario of a *“garbage scene”* with strong semantic segmentation performance **rarely occurs**. From both empirical observation and theoretical reasoning:\n- In our two-stage baselines (e.g., LiDARGen + RangeNet++), **moderately corrupted** LiDAR scenes already lead to significant segmentation failures, even when the segmentation network is extensively trained with noise augmentations. This is expected, since **3D segmentation is fundamentally geometry-dependent** — poorly generated structures yield unreliable spatial features and semantic predictions.\n- Our **semantic-aware evaluation metrics** are explicitly designed to measure cross-modal alignment, not scene quality in isolation. As illustrated in Fig. `3`(a), we encode the depth image and semantic map **independently** using two modality-specific encoders (RangeNet++ and LiDM-Seg), and fuse the extracted features into a **unified semantic-aware feature** $f_s$. Therefore, even in the rare cases where a scene may have poor geometric quality but good semantic map, the geometric quality is **not** *\"overly out-weighted\"*.\n- At the BEV level (please see Fig. `3`(b)), we compute **semantic histograms** across classes and spatial cells. These are highly sensitive to both the **correct geometry** (e.g., point distribution) and the **correct semantic allocation** (e.g., class labels). If either modality fails, due to noisy generation or poor segmentation, the BEV histogram will diverge from the real data, and the model will be penalized accordingly.\n\nIn summary, geometry and semantics contribute equally in our metrics by design, and misalignment across modalities is indeed captured. As suggested, we have further clarified this detail in the revised manuscript.\n\n---\n> **`Q3`:** *\"Regarding closed/open-loop inference: I understand that open-loop directly diffuses both modalities, which might lead to discrepancies. However, the metrics may not be able to reflect misalignment between the generated scene and the map.\"*\n\n**A:** Your understanding of the open-loop setting is correct — it involves jointly generating the LiDAR scene and semantic map **without** enforcing consistency during generation. This indeed introduces **cross-modality discrepancies**.\n\n- As shown in Table `4`, closed-loop inference with default hyperparameters (the highlighted row) outperforms open-loop inference (first row). Importantly, we emphasize that the semantic map in closed-loop inference is still generated by Spiral, and is **not** externally provided. Therefore, we believe that the proposed closed-loop inference strategy is a novel contribution of this work.\n- Our proposed semantic-aware metrics are designed to effectively evaluate **cross-modality alignment**.  \n  - For **range-view** and **Cartesian-based metrics**, as illustrated in Fig. `3`(a), the semantic-aware feature $f_s$ for each scene is formed by concatenating the global features extracted from the depth image and semantic map, respectively. This ensures that both geometric and semantic quality are jointly evaluated.  \n  - For **BEV-based metrics**, they assess alignment at a **finer** granularity. Specifically, for each generated scene, we compute semantic-aware histograms $h_s$ over the bird’s-eye-view by combining the histogram of each semantic class (please see Fig. `3`(b)). As a result, a generated scene with either:\n    1. good semantic segmentation but poor geometric quality, or;\n    2. poor semantic segmentation but good geometric quality\n\nwill fail to produce a histogram that matches the real scene. Additionally, this also ensures that the BEV metrics penalize the misalignment in geometry or semantics, making them strong indicators of overall generation fidelity.\n\nFurthermore, as elaborated in `Q2`, our semantic-aware metrics, including modality-specific encoders and BEV histograms, are designed to detect cross-modal misalignment, ensuring that **semantic and structural coherence** is thoroughly assessed.\n\n---\n> **`Q4.1`:** *\"The data augmentation results should be more extensively compared, especially as this is a main application of the paper.\"*\n\n**A:** We appreciate your emphasis on this point. In addition to the experiments presented in the manuscript, we further conduct a comprehensive evaluation of generative data augmentation (GDA) under two challenging tests:\n\n1. **Cross-Domain Generalization (Waymo Test):**\n    - We use LiDAR scenes generated by Spiral, trained on SemanticKITTI, to augment the training set of Waymo [`R1`] (when 10% of real data is available), and evaluate the model's performance on the Waymo val set. \n    - The segmentation model SPVCNN++ is trained under three settings:\n      1. without GDA,\n      2. with synthetic data generated by R2DM & RangeNet++,\n      3. with our Spiral-generated data. \n    - The segmentation performance (mIoU) under these settings is **55.89%**, **55.93%**, and **57.52%**, respectively. These results demonstrate the **strong generalization ability** of our Spiral-based GDA approach.\n\n2. **Out-of-Distribution (OOD) Extreme Weather (Robo3D Test):**\n    - We evaluate SPVCNN++ trained under the same three settings as in the above \"Cross-Domain Generalization Test\", on challenging out-of-distribution subsets, `fog` and `wet ground`, from *Robo3D* [`R2`]. \n    - The results demonstrate that, although Spiral is not specifically fine-tuned for these OOD extreme weather conditions, the data generated by Spiral still **effectively enriches the training set** and **improves performance**.\n\n> ***Table.** Experiments under `fog` scenarios:*\n| Ratio of Real Data | w/o GDA | R2DM & RangeNet++ | Spiral (Ours) |\n|:---:|:---:|:---:|:---:|\n| 1% | 32.07 | 36.82 | **44.06** |\n| 10% | 53.93 | 54.20 | **58.61** |\n| 20% | 55.89 | 56.07 | **61.24** |\n\n> ***Table.** Experiments under `wet ground` scenarios:*\n| Ratio of Real Data | w/o GDA | R2DM & RangeNet++ | Spiral (Ours) |\n|:---:|:---:|:---:|:---:|\n| 1% | 34.26 | 37.96 | **44.19** |\n| 10% | 54.70 | 55.92 | **59.31** |\n| 20% | 56.81 | 57.53 | **62.02** |\n\nAcross all scenarios, our Spiral-based GDA consistently outperforms all baselines. These results demonstrate our **effectiveness in both cross-domain and OOD generalization settings**. As suggested, we have improved the presentation of these results and emphasized the comparisons further in the revised manuscript.\n\n---\n> **`Q4.2`:** *\"Please show the mIoU of SPVCNN++ without any augmented data.\"*\n\n**A:** Thank you for pointing this out. The mIoU results of SPVCNN++ trained **without** any generative data augmentation (GDA) are already reported in the “None” column of Table `C` in the supplementary material (see also \"w/o\" column of Table `3` in the manuscript). \n\nThis baseline reflects the model trained using only real data. For example, under the 10% supervision setting on the Robo3D-fog set, this baseline achieves **53.93%** mIoU, while Spiral-based GDA improves the score to **58.61%**. We have revised the table caption to make this clearer.\n\n---\n> **`Q4.3`:** *\"The 'None' column in Table C is unclear and not explained.\"*\n\n**A:** Thanks for pointing out our oversight! The “None” column in Table `C` (corresponding to the \"w/o\" column in Table `3` in the main paper) refers to the baseline performance of SPVCNN++ **trained without any synthetic data**, i.e., **real-only training**. This column is essential for measuring the absolute contribution of generative augmentation. We have revised the table caption and corresponding text to explicitly define “None” and \"w/o\" to avoid confusion.\n\n---\n> **`Q4.4`:** *\"What happens when the real data ratio is high (e.g., 80%)?\"*\n\n**A:** Great question. We have conducted additional experiments under the **80% supervision setting**, where the amount of real data is abundant. The resulting mIoU scores on the SemanticKITTI are:\n- None (real only): 64.48%\n- R2DM + RangeNet++: 64.51%\n- Spiral (ours): **65.44%**\n\nEven in this data-rich regime, Spiral continues to provide **non-trivial performance gains**, while R2DM shows negligible benefit. This result highlights that Spiral not only excels in low-data regimes but also contributes value in high-data scenarios by generating **semantically aligned and diverse** LiDAR scenes.\n\n---\n**References:**\n- [`R1`] Sun, Pei, et al. \"Scalability in Perception for Autonomous Driving: Waymo Open Dataset.\" CVPR, 2020.\n- [`R2`] Kong, Lingdong, et al. \"Robo3D: Towards Robust and Reliable 3D Perception against Corruptions.\" ICCV, 2023.\n---\nLast but not least, we would like to sincerely thank Reviewer `gApr` again for the valuable time and constructive feedback provided during this review."}, {"review_id": "BVVsfo2tPg", "reviewer": "Reviewer_oNZH", "summary": "The 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.", "questions": "## 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "Although the fact that the proposed method is approximately 2.05 seconds slower than the two-stage baseline (R2DM + RangeNet++) sounds like a drawback to me, the authors have addressed the remaining concerns—particularly the statistical analysis on the denoising timestep.\n\nAccordingly, I will retain my score as Borderline Accept.", "rebuttal": "We sincerely thank Reviewer `oNZH` for the thoughtful and constructive feedback. We are encouraged that you recognize the state-of-the-art performance, the practical utility of our data augmentation strategy, and the novel closed-loop inference mechanism introduced in our work. We address your concerns in detail below:\n\n---\n> **`Q1`:** *\"The paper introduces a closed-loop inference mechanism, but does not provide quantitative statistics on when the mode switching occurs. Including this would help clarify the behavior and stability of the strategy.\"*\n\n**A:** Thank you for raising this insightful point. We conducted a statistical analysis over **1,000 samples** each from SemanticKITTI and nuScenes. Under the default **256 denoising steps**, the closed-loop inference is triggered (i.e., when ≥80% of semantic predictions surpass the confidence threshold) at an average step of:\n- 154 ± 34 for SemanticKITTI\n- 161 ± 37 for nuScenes\n\nThis implies that the switching typically occurs in the **last ~40%** of the generation process, when semantic predictions are sufficiently stable. This delayed activation helps avoid early-stage noise and ensures meaningful semantic–geometric alignment. As suggested, these statistics have been added to the revised supplementary material.\n\n---\n> **`Q2`:** *\"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\n**A:** Thanks for your comment. We fully agree with your point. As reported in Table `B` and Section `F` of the supplementary material, Spiral achieves an average generation time of **5.7 seconds per scene** on an NVIDIA A6000 GPU. Compared to two-stage baselines (e.g., LiDM + segmentor), Spiral offers **superior efficiency** due to its **end-to-end architecture**, though it is slightly slower than the fastest baseline (R2DM + RangeNet++) by ~2.05 seconds. These results support the practical viability of our approach for scalable labeled LiDAR scene generation.\n\n---\n> **`Q3`:** *\"Do you have empirical data on when closed-loop switching occurs?\"*\n> - *\"If switching occurs too early, it might resemble inefficient two-step pipelines.\"*\n> - *\"If switching occurs too late, the semantic conditioning might be ineffective.\"*\n\n**A:** Excellent observations. Our empirical analysis (please see the response to `Q1`) confirms that closed-loop inference is activated **neither too early nor too late**, typically during the **middle-to-late denoising phase**. This timing ensures that the semantic map is reliable enough for conditioning, while also leaving sufficient steps for influence.\n\nMoreover, since both geometric and semantic predictions are generated **simultaneously** at each step via our multi-head design, early switching (if it occurs) **does not incur additional computation** as would a separate two-step pipeline. Your concern regarding very early or very late switching degrading quality aligns with our findings in Table `4`, where extreme settings of the threshold $\\delta$ lead to suboptimal alignment. This further supports the effectiveness of our dynamic switching scheme.\n\n---\n> **`Q4`:** *\"In Figure 4, what is the source of the ground-truth (GT) semantic maps?\"*\n\n**A:** Thanks for your question. The ground-truth (GT) semantic maps shown in Figure `4` are from the official SemanticKITTI annotations. In contrast:\n- Spiral generates semantic maps **jointly** with the LiDAR scene in a **unified diffusion process**;\n- The baselines (e.g., R2DM, LiDM) generate semantic maps via **post-hoc segmentation** using a pretrained RangeNet++ model.\n\nThis illustrates a key distinction: Spiral performs **end-to-end semantic-aware generation**, while the others follow a **two-step pipeline**.\n\n---\n> **`Q5`:** *\"Could the authors provide a comparison of inference time for labeled LiDAR scene generation?\"*\n\n**A:** Thanks a lot for asking. Please refer to the details in our response to `Q2`.\n\n---\n> **`Q6`:** *\"[Suggestion] Consider classifier-free guidance (CFG) with semantic maps during inference.\"*\n\n**A:** Thank you for this thoughtful suggestion. We conducted a comprehensive study by applying CFG in our conditional steps with varying guidance weights $w \\in \\{ 1, 2, 4, 8 \\}$, using the formula:\n$$\n\\hat{\\epsilon} = (1 + w)\\epsilon_{\\text{cond}} - w\\epsilon_{\\text{uncond}},\n$$\nwhere:\n- $\\hat{\\epsilon}$: final noise prediction,\n- $\\epsilon_{\\text{cond}}$: noise predicted with conditional input,\n- $\\epsilon_{\\text{uncond}}$: noise predicted with unconditional input,\n- $w$: guidance strength controlling the influence of the condition.\n\nIn the conditional steps of Spiral, we set the guidance weight $ w = 0 $, which makes the inference to standard conditional generation.  \n\nThe results (see table below) show **minimal improvements** when $w = 1$, particularly in Cartesian metrics, and **degradation** for larger weights. We attribute this to **intermediate semantic predictions still containing artifacts**, which can mislead the generator when overly trusted. Thus, while promising in principle, CFG offers **limited gains** in our current setup. As suggested, we have included these results and the discussion in the revised supplementary material.\n\n|  | Range View | Range View | Range View | Range View | Cartesian | Cartesian | Cartesian | Cartesian | BEV | BEV | BEV | BEV |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| $w$ | FRD (x$1$) | MMD (x$10^{-2}$) | S-FRD (x$1$) | S-MMD (x$10^{-2}$) | FRD (x$1$) | MMD (x$10^{-2}$) | S-FRD (x$1$) | S-MMD (x$10^{-2}$) | JSD (x$10^{-2}$) | MMD (x$10^{-3}$) | S-JSD (x$10^{-2}$) | S-MMD (x$10^{-3}$) |\n| 0 | 170.18 | 4.81 | 382.87 | 4.10 | 8.06 | 1.10 | 153.61 | 3.20 | 3.76 | 0.15 | 9.16 | 1.41 |\n| 1 | 170.19 | 5.02 | 382.87 | 4.15 | 8.03 | 1.01 | 153.68 | 3.27 | 3.91 | 0.16 | 9.20 | 1.42 |\n| 2 | 170.30 | 5.07 | 383.71 | 4.24 | 8.48 | 1.27 | 155.98 | 3.80 | 4.02 | 0.17 | 9.25 | 1.43 |\n| 4 | 170.91 | 5.18 | 385.97 | 4.43 | 13.63 | 3.37 | 168.30 | 5.99 | 4.45 | 0.19 | 9.41 | 1.45 |\n| 8 | 173.98 | 5.42 | 391.68 | 4.61 | 26.86 | 9.45 | 195.15 | 11.04 | 5.15 | 0.23 | 9.81 | 1.54 |\n\n> ***Note:** All metrics are lower-is-better.*\n\n---\nLast but not least, we would like to sincerely thank Reviewer `oNZH` again for the valuable time and constructive feedback provided during this review."}, {"review_id": "DK8Xgzf9nV", "reviewer": "Reviewer_ZJCH", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I maintain my recommendation to reject this paper due to its insufficient scientific contribution to warrant publication. However, I acknowledge that my initial assessment was overly critical, so I am adjusting my score from 2.5 to 3, in recognition of the authors' thorough responses during the rebuttal phase.\n\nThe field already contains many works on generating temporal LiDAR data or achieving conditional generation (I do not consider semantic maps to be meaningful conditions). Therefore, I strongly recommend that the authors can extend this work to include at least temporal generation capabilities, which would align with the current development of LiDAR Generation. Furthermore, the generation of single-frame LiDAR data and semantic maps without conditioning limits the application primarily to data augmentation. Even in this limited context, the demonstrated improvements on the SemanticKITTI dataset remain modest.", "rebuttal": "We sincerely thank Reviewer `ZJCH` for the thoughtful and constructive feedback. We are encouraged to see that you found our pipeline “innovative” and recognized our evaluation metrics (S-FPD and S-MMD) as “novel”. Below, we respond to your comments in detail.\n\n---\n> **`Q1`:** *\"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.\"*\n\n**A:** Thank you for raising this point. While we acknowledge recent efforts on **temporal LiDAR sequence** generation, we respectfully note that **single-frame generation remains an essential and active research direction**, just as image generation continues to be studied alongside video generation. In our case, Spiral is the **first** to jointly generate **range-view LiDAR** and **semantic maps** in a unified framework — a task not addressed in prior works on single or sequential LiDAR generation. We agree that extending Spiral to handle temporal consistency is a promising future direction, and we plan to explore this in follow-up work. Based on your suggestion, we have revised our manuscript to better reflect this aspect.\n\n---\n> **`Q2`:** *\"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\n**A:** Thanks a lot for your comment. We would like to respectfully clarify a few points:\n- **(1) Spiral supports both unconditional and conditional generation.**\n  - The **unconditional generation** task is performed via two options:\n    1. **open-loop inference** that stays unconditional throughout, and;\n    2. **closed-loop inference** that switches to conditional mode when semantic confidence is high (please see Fig. `2`(b)).\n  - The **conditional generation** task is also supported: Spiral generates a LiDAR scene given an **externally provided semantic map**, using only the conditional mode during inference.\n\n- **(2) Unconditional generation is crucial for data augmentation.**\n  - Requiring ground-truth semantic maps as input (conditional mode) would defeat the purpose of reducing annotation costs. The ability of Spiral to perform semantic-aware unconditional generation is a key contribution that enables synthetic data augmentation in practical scenarios.\n\n- **(3) The improvement from Spiral is significant, not marginal.**\n  - For example, on the Robo3D-fog set [`R1`] (see the table below) with 1% real data, Spiral-based generative data augmentation boosts the segmentation mIoU from **32.07%** (w/o GDA) and **36.82%** (two-step R2DM & RangeNet++) to **44.06%** — a gain of **+11.99%** over the no-GDA baseline and **+7.24%** over the prior two-step method.\n\n> ***Table.** Experiments under Robo3D-`fog`*\n| Ratio of Real Data | w/o GDA | R2DM & RangeNet++ | Spiral (Ours) |\n|:---:|:---:|:---:|:---:|\n| 1% | 32.07 | 36.82 | **44.06** |\n| 10% | 53.93 | 54.20 | **58.61** |\n| 20% | 55.89 | 56.07 | **61.24** |\n\nWe hope the above clarifies and addresses your concern. We have refined our manuscript accordingly for better clarity.\n\n---\n> **`Q3`:** *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?\"*\n\n**A:** Thanks for asking. We clarify that the task evaluated in this work is **unconditional** LiDAR scene generation with both **geometric** and **semantic** information.\n\nSpiral is trained to **jointly generate** the LiDAR scene (geometry + reflectance) and its semantic map from Gaussian noise. During evaluation, we compare the **generated scenes** to the corresponding **ground-truth LiDAR frames** and their **annotated semantic maps** from datasets such as SemanticKITTI and nuScenes. These provide a reliable benchmark with **per-point labels** for both geometry and semantics.\n\nUnlike prior methods (e.g., R2DM, LiDM) that require post-hoc segmentation using external models, Spiral eliminates this need via **end-to-end semantic-aware generation**, which we believe marks an important step forward in the field.\n\n---\n> **`Q4`:** *\"In Table 3, the 'w/o' model’s performance still increases with a higher ratio of synthetic data, which seems contradictory. Could the authors clarify the experimental setup and explain these results?\"*\n\n**A:** Thank you for pointing out this potential confusion. We clarify as follows:\n- In Table `3`, the **“w/o”** (or “None”) column refers to training **without** any generative data augmentation (GDA) — i.e., using only real, labeled LiDAR scenes.\n- The **“Ratio”** column refers to the **amount of real labeled data** used in training (1%, 10%, 20%, etc.), as stated in (1) the third row of the Table 3 caption, and (2) line 265 of the main paper.\n\nTherefore, performance in the “w/o” column improves **not due to synthetic data**, but due to the **increased availability of real data**. For instance, in Table `3`, with 10% real data, SPVCNN++ achieves 59.07% mIoU; with 20%, it improves to 61.16%. We have revised the caption of Table 3 to make this setup and terminology clearer.\n\n---\n**References:**\n- [`R1`] Kong, Lingdong, et al. \"Robo3D: Towards Robust and Reliable 3D Perception against Corruptions.\" Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.\n---\nLast but not least, we would like to sincerely thank Reviewer `ZJCH` again for the valuable time and constructive feedback provided during this review."}]

Author rebuttal:

Meta-review: The paper presents a diffusion model for generating depth, reflectance images, and semantic images from range-view lidar data. Reviewers appreciated that the paper is well-written, the model achieves good performance vs. baselines, and the closed-loop training and inference schemes are novel and reasonable. They also highlighted the following weaknesses of the approach.
- The paper does not assess when the model switches between unconditional and conditional generation (to incorporate the semantic information).
- The evaluation lacks an assessment of inference speed.
- The model does not handle temporal generation of lidar data.
- The semantic output is limited to pre-defined categories from existing datasets.
- The method may not generalize to rare classes (e.g., bicyclists) or work well on real-world data since it is trained on a specific dataset.

The authors provided a rebuttal to respond to the concerns — they included a new analysis to assess when the model switches to conditional generation, an evaluation of inference speed, an assessment of cross-domain generalization and out-of-distribution performance, as well as a number of other clarifications. In response to the rebuttal, one reviewer raised their score (from Reject to Borderline Reject), and the final reviewer ratings remained split with two ratings of Borderline Accept and two ratings of Borderline Reject.

After reading the paper, I'm persuaded by the remarks of the positive reviewers. In particular, I found the evaluation of the method to be thorough, and the technique does appear to be the state-of-the-art for the task of lidar scene generation, with significant improvements in the consistency of the generated semantic labels with the ground truth datasets compared to baselines. Additionally, the authors appear to have responded to most concerns in the very thorough rebuttal, and including the new results will further improve the paper. Overall, while the paper does not provide significantly new capabilities (e.g., temporal generation), it does provide technical contributions that are potentially valuable to the community as well as improved results on an established problem. I recommend acceptance.
Decision: Accept (poster)