Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation
Authors: Wangkai Li
Zhaoyang Li
Yuwen Pan
Rui Sun
Yujia Chen
Tianzhu Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: LiDAR Semantic Segmentation
Autonomous Driving
Robust Learning for Adverse Weather
Data Augmentation
Domain Generalization
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Adverse weather conditions significantly degrade the performance of LiDAR point cloud semantic segmentation networks by introducing large distribution shifts.
Existing augmentation-based methods attempt to enhance robustness by simulating weather interference during training. 
However, they struggle to fully exploit the potential of augmentations due to the trade-off between minor and aggressive augmentations. 
To address this, we propose A3Point, an adaptive augmentation-aware latent learning framework that effectively utilizes a diverse range of augmentations while mitigating the semantic shift, which refers to the change in the semantic meaning caused by augmentations. 
A3Point consists of two key components: 
semantic confusion prior (SCP) latent learning, which captures the model's inherent semantic confusion information, and semantic shift region (SSR) localization, which
decouples semantic confusion and semantic shift, enabling adaptive optimization strategies for different disturbance levels.
Extensive experiments on multiple standard generalized LiDAR segmentation benchmarks under adverse weather demonstrate the effectiveness of our method, setting new state-of-the-art results.

Published as a conference paper at ICLR 2026
ADAPTIVE AUGMENTATION-AWANT LATENT LEARN- ING FOR ROBUST LIDAR SEMANTIC SEGMENTATION
Wangkai Li', Zhaoyang Li', Yuwen Pan', Rui Sun', Yujia Chen', Tianzhu Zhang!
Univerriity of Science and Technology of China National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory {1 [1wklwk, 1iz lizhaoyang, panyw, issunrui, yujia_chen) 8mail 1 .ww.cutc.... edu. cn, t izzhang@ustc. edu.on
ABSTRACT
Adverse weather conditions significantly degrade the performance of LiDAR point cloud semantic segmentation networks by introducing large distribution shifts. Existing augmentation--aased methods attempt to enhance robustness by simulating weather interference during training. However, they struggle to fully exploit the potential of augmentations due to the trade-off between minor and aggressive augmentations. To address this, we propose A3Point, an adaptive augmentation-awar latent learning framework that effectively utilizes a diverse range of augmentations while mitigating the semantic shift, which refers to the change in the semantic meaning caused by augmentations. A3Point consists two key components: semantic confusion prior (SCP) latent learning, which cap- tures the model's ' inherent semantic confusion information, and semantic shift re- gion (SSR) localization, which decouples semantic confusion and semantic shift, enabling adaptive optimization strategies for different disturbance levels. Exten- sive experiments multiple standard generalized LiDAR segmentation bench- marks under adverse weather demonstrate the effectiveness of our method, setting new state-of-the-ee results.
INTRODUCTION
LiDAR semantic segmentation vital for 3D vision tasks such as autonomous driving (Li & Ibanez- Guzman 2020; Li et al. 2020 Aksoy et al. 2020 Zhao et al.) 2023). However, existing methods (Ando et al. 2023] Choy al. 2019 Lai et al. 2023 Puy et al. 2023 often struggle in adverse weather (e.g., fog. snow, and rain) due to severe distribution shifts in point clouds, causing a mis- match between training and testing data. Since most outdoor scene point cloud datasets (Behley al. 2019 Fong al.) 2022 Xiao al. 2022b) are collected in normal weather, developing robust network that generalizes across diverse conditions is increasingly crucial. Addressing this challenge essential for achieving reliable, weather-invariant LiDAR semantic segmentation.
To mitigate performance degradation, existing studies (Xiao et al. 2023 Park et al., 2024 Kong al. 2023b) enhance network robustness by simulating adverse weather during training simulation-based or augmentation-ben based approaches. Simulation baaslttio--based methods (Bijelic et al.
2018 Hahner et al. 2022, (021) model physical equations replicate weather effects on point clouds but require separate modeling for each condition. making it impractical to cover all varia- tions. Augmentation-be based methods (Xiao et al. 22033 Park et al.) 2024 Kim al. 2023) introduce geometric perturbations and point drop to mimic weather-induced distortions more flexibly. How- ever, they remain underutilized for two reasons (Fig. D (1) mild augmentations fail to generalize to severe conditions, while (2) excessive augmentations distort point cloud distribution, causing mantic shift (Wang et al.) 2021 Bai et al. 2022 Yuan et al. 2021). where augmented regions no longer align with original semantics, thereby hindering training. This dilemma leads existing meth- ods to restrict augmentation range and magnitude, limiting their potential. How to utilize a larger augmentation space while mitigating semantic shift remains compelling challenge.
"Corresponding author

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Published as a conference paper at ICLR 2026
10
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(4) Examples of Adverse Weather
(b) Different Levels of Augmentation
Figure 1: (a) Point cloud distortions caused by adverse weather conditions. (b) Augmentation at different levels (light, moderate, and heavy), where we adjust the drop ratio (DR) for point drop and jitter std (JS) for random jittering. (c) Visualization of segmentation accuracy under different aug- mentation levels, showing that aggressive distortions lead to significant performance degradation.
This analysis motivates us to explore a broader, more aggressive augmentation space to better simu- late irnduce- distortions at varying intensities. However, ensuring its effectiveness requires addressing the potential semantic shift in augmented point clouds. Directly modeling semantic shift difficult, as prediction errors arise from two factors: (1) semantic confusion (Fig (a)). which is the network's 's inherent property that struggles to distinguish similar classes (e.g., road vs. sidewalk) despite correct labels; (2) semantic shift (Fig 2 (b)). where excessive augmentation distorts point cloud distributions and the original labels fail to describe the corresponding regions. For semantic confusion, original labels should be preserved to enhance the network's 's discriminative ability. In contrast, semantic shift requires adapting supervision avoid misleading signals. Thus, the key to solving semantic shift lies in disentangling these two factors in augmented point clouds.
In this paper, we model semantic shift in augmented point clouds enable their effective use during training. Observing that semantic confusion exists in both raw and augmented data and is consistent across domains, while semantic shift occurs only in augmented data, we propose a two-step strategy to identify it: (1) Mining semantic confusion priors from normal-condition predictions. Inspired by VQVAE (Van Den Oord et al. 2017). we frame this as a discrete latent representation learning task: class-specific local confusion patterns are encoded into latent space and represented by quantized latent variables, with representational capacity enforced via reconstruction. (2) Detecting semantic shift anomalies in augmented point clouds. We apply the learned latent encoder to augmented predictions, formulating semantic shift localization as an anomaly detection problem: by comparing augmented latent representations with the learned priors, distinguish semantic consistency regions (affected only by semantic confusion) from semantic shift regions (additionally affected by semantic shift). This separation enables targeted optimization during training.
Based on the above discussion, we propose Adaptive Augmentation-Awan latent learning for ro- bust LiDAR semantic segmentation in adverse conditions, namely A3Point, which involves two key components: semantic confusion prior (SCP) latent learning and semantic shift region (SSR) lo- calization, to fully explore the potential of a large and diverse augmentation space for robust point cloud segmentation. To capture the network's inherent semantic confusion, in the SCP latent learning module, we perform class-wise latent encoding on predictions from original point clouds, using quantized latent variables to represent local confusion patterns. Through vector quantization and reconstruction constraints, this process learns meaningful, representative embeddings capable of reconstructing prediction maps. To disentangle semantic confusion from semantic shift, in the SSR localization module, we dynamically track the representation distribution each quantized latent variable and use frozen prior latent encoder to implicitly represent augmented predictions. By treating semantic shift detection as an anomaly detection problem, we adaptively distinguish semantic consistency regions (SCR) from semantic shift regions (SSR). In SCR, original labels main effective for optimization, while in SSR, we apply knowledge distillation, selecting the global nearest quantized latent variable supervisory constraint. By jointly localizing SSR and adapt-
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Published as a conference paper at ICLR 2026
Confusion
Target Domain Confusion Matris
Orsttal
On Veg
(b.1) Prediction Result
(b.2) Distribation
(a) Semantic Confusion
(b) Semantic Shift
Figure 2: Demonstration of semantic confusion and semantic shift. (a) Confusion matrices from source (normal weather) and target (adverse weather) domains. Despite domain shifts, semantic confusion remains consistent. (b) Aggressive augmentations alter point cloud density and shape, leading to semantic misalignment (e.g.. car veg.).
ing optimization strategies accordingly, A3Point fully hamesses diverse augmentations improve segmentation robustness under varying disturbance levels.
Our contributions augmentation-basee approaches, enabling the effective utilization of a large and diverse augmen- tation space to improve LiDAR segmentation robustness under adverse weather. 2) We propose two-step framework to decouple semantic confusion and mitigate semantic shift, comprising seman- tic confusion prior (SCP) latent learning and semantic shift region (SSR) localization. 3) We validate our approach through extensive experiments on multiple standard generalized LiDAR segmentation benchmarks under adverse weather, achieving new state-of-the of-the-art results.
follows: 1) We introduce novel perspective to overcome limitations of
2 RELATED WORK
2.1 3D POINT CLOUD SEMANTIC SEGMENTATION
Semantic segmentation of 3D point clouds assigns a label to each point and is typically approached via three paradigms: paint-based, projection- based, and voxel-based. Point-based methods directly take 3D points input. PointNet (Qi et al. 2017) was the first to introduce this approach, utilizing multi-layer perceptrons to extract per- point features. Subsequent works (Thomas et al. 2019 Zhao
et al. 2021 Choe al. [2022] Fan et al. 2021 Li at. 22024 2025e) further advanced this paradigm. While these methods minimize information loss and achieve strong performance, they demand high computational resources when applied to large-scale LiDAR data. Projection-based methods (Ando)
let al., [023] Kong et af.] 2023a Milioto et al.] 2019 Xiao et al. [02T]] /Zhang et al.) 2020) map point clouds to 2D range-view images via spherical projection, enabling the use of 2D segmentation networks. These methods are computationally efficient but suffer from information loss during
projection, leading to slightly lower accuracy. Voxel-based methods (Choy al., 2019 Lai et al,, 2023 Zhu et al.] 2021 Graham et al.) 2018 Taag et al.) (020) divide 3D point clouds into sparse voxel grids and aggregate points within the same voxel. The introduction of sparse convolutions significantly reduces computational costs, making this paradigm well-suited for large-scale outdoor LiDAR scenes. this work, we adopt voxel- architectures (Choy et al. 2019. Tang et al., 2020) as our baseline to balance inference efficiency and segmentation performance.
2.2 LIDAR UNDER ADVERSE WEATHER CONDITIONS
In real-world applications, robust scene understanding under adverse weather is critical, especially given the safety demands of autonomous driving (Kong et al. 2023b Xiao et al.) 2023) Sakaridis jet al., 2021 Pan et al. 2025). However, extreme weather can significantly disturb point cloud dis- tributions (Filuueira al. 2017 Heinzler et al.) 2019 Peynot et al.) 2009 Ryde & Hillier 2009),
leading to a dramatic drop in the performance of LiDAR segmentation networks. To mitigate do- main discrepancy between normal and adverse weather conditions, unsupervised domain adapta- tion (UDA) approaches (Hahner et al., 2022] 2021 Luo et al.) 202T Xiao et al., 2022a b) Yang et al. 2021) have been explored. Drawing inspiration from semi-supervised learning (Sun et al.
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2025a b). these methods leverage labeled source domain data and unlabeled target domain data to learn
features. improving cross-doman performance (Li et. al. 2025b è a 2026: Chen et al. 2025b at He et al.; 2025a|b) b). However, UDA-based methods are limited to specific and visible target domains, making them insufficient for generalizing to unknown weather disturbances. In this paper,
adopt the domain generalization setting (Kim et al. [024 2023 [i al., 2023a b),
aiming to train a model using a single source domain under normal weather conditions, without access target data from adverse weather during training.
2.3 AUGMENTATION FOR ROBUST LIDAR SEGMENTATION
To enhance the robustness of LiDAR segmentation models, existing methods introduce point cloud corruptions during training to simulate the interference caused by adverse weather. Simulation- based methods (Bijelic et al. 2018 Hahner et al. 2022 202Ib rely on prior weather knowledge to construct physical models that artificially simulate point clouds under adverse conditions. Rather than explicitly modeling specific weather effects, augmentation-buee
methods (Xiao et al.] 2023
Park al. [2024] Kim et al. 2023) provide more general and flexible approach. Inspired by 2D
image augmentation, Mix-based methods (Kong et al.] 2023c Nekrasov et al.] 2021 Xiao et al.]
2022a Zhao et al., (024) blend two LiDAR scans enhance training diversity. To better simulate disturbances caused by adverse weather, recent works (Park et al. 20244] Xiao et al.
2023) identify
Iwo primary degradation patterns: (1) geometric perturbation and (2) point drop. PointDR (Xiao
ct al. 2023) primarily introduces the SemanticSTF benchmark and an accompanying augmentation-
based baseline that simulates weather induucd disturbances via geometric perturbation and point dropping. LiDARWeather (Park et al. (024) proposes learnable Point Drop strategy for adaptive augmentation. However, these methods remain constrained by a limited perturbation space. con- trast, our approach explores broader range of perturbations and explicitly addresses the semantic shift problem caused by aggressive augmentation, enabling more robust network training.
3 METHOD
3.1 PROBLEM DEFINITION
In domain generalization (DG) for LiDAR semantic segmentation, the network trained on labeled source domain data and need be generalized to unseen target domain data. To be specific, the source domain be denoted as D, {(05 .v. ) where x. Xs represents LiDAR point cloud with ue E Ys the corresponding point-wise one-hot label covering C classes. The target domain can be denoted D, ((x,] 2 where target label Yr shares the same label space with Ys. Since the target domain is not accessible during the training process, we omit the superscript S/T in the following notation for brevity.
3.2 PRELIMINARIES
Augmentation-basse Training for DG. Existing methods explore weather-induced disturbances and apply them data augmentation. This approach can be viewed as domain randomization paradigm for learning a domain-genn -enaaingenerallzaable network. During training, the loss first computed on the original scan to train neural network fi
Lee
Fce (f
Ns
where fee denotes the voxel-wise cross-entropy loss, and n, is the number of valid voxels in ø;- Then, for each I;, random augmentations are simultaneously applied to (r; v.), obtaining (f;, Øi) (x(,,,))) The augmented training pair is also implemented through the cross-entropy loss:
Ns
Av
Ece No
(f (6).
(2)
The total training loss be represented as: L Lee + Lec.
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Augmentiation-kaae Zrennngg
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Figure 3: Pipeline of A3Point. explore an abundant augmentation space (Sec and propose two key components: SCP latent learning to capture inherent semantic confusion (S5 and SSR localization decouple semantic shift (Sec 3.66.
Vector Quantized Variational AutoEncoder. VQ-VAE (Van Den Oord et al. 2017) is a variant of variational autoencoder (Kingma et al. 2013) that learns discrete latent representation. It consists of an encoder E, a decoder ID, and a codebook C = {ei, f2,...,ek! containing K learnable embed- continuous latent representation že E(x), which is then
dings. The encoder maps the input quantized to the nearest codebook entry using a nearest-neighbor lookup (we use a single random variable represent the discrete latent variables for simplicity):
Eq quantize(z.) = ck; where arg arg || - e,112.
(3)
The decoder reconstructs the input from the quantized latent representation: I [zz)). Total training objective is:
C -= 212 [|sg(=.) 12 1| sg(z,)1B,
(4)
which consists reconstruction loss, codebook loss, and commitment loss, where sg(.) denotes the stop-gradient operation. The codebook loss brings the selected latent variables close to encoder outputs, while the commitment loss encourages the encoder to produce latent representations close to the codebook entries. The discrete latent representation learned by VQ-VAE can capture the underlying structure of the data and has been successfully applied in various tasks, such as image generation (Razavi et al. 2019 De Fauw et al. 2019 Yu et al. (021) and unsupervised representa-
tion learning (Liu et al. 2023 Chen et al. 2024
Takida et al. 2023).
3.3 OVERVIEW OF A3POINT FRAMEWORK
Then, model a discrete latent representation learning process to learn semantic confusion prior (Sec 3.5). Next,
We first define the enhanced augmentation space used during training (Sec B.4
localize the semantic shift regions through form of anomaly detection (Sec3.6)) Finally, introduce region-specific optimization strategies (Sec 3.7 Our overall framework is shown in Fig
3.4 ENHANCED AUGMENTATION SPACE
Previous works (Xiao et al. 2023 Park et al. 2024) identify that the main disturbances caused by ad- verse weather can be summarized as (I) geometric perturbation, caused by perceived distance shifts, and (2) point drop, resulting from beam attenuation, beam missing, or potential occlusions. This motivates us to use random jittering and point drop as the primary and generic augmentation strate- gies. Unlike previous methods that only adopt limited range and magnitude, we define a broader augmentation space to fully simulate various levels of weather disturbances. Specifically, we define jitter std in a range of Limsn= for random jittering and drop ratio range of [dman- dmaz] for point drop, and uniformly sample the perturbation magnitudes during training. For other subsidiary augmentations, we follow previous works and employ random rotation, random scaling, random flipping, random noise perturbation, mix (Kong et al. 2023c Xiao al. 2022a)
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3.5 SEMANTIC CONFUSION PRIOR LATENT LEARNING
Semantic confusion, which refers to the network's 's inherent uncertainty in distinguishing between classes, as reflected in the predicted probability distribution. To decouple semantic confusion and semantic shift in the augmented point cloud, mine prior knowledge of semantic confusion from the original point cloud predictions.
Specifically, first introduce autoencoder for prior latent learning, which follows VQ-VAE (Van Den Oord et al., (017). Through quantized latent variables and a reconstruction process, it can effec- tively learn meaningful representations with sufficient representational power. The input for encoder E is obtained by first concatenating ((x) (processed through softmax) and x, then splitting the result class-wise according the label y to prevent inter-class interactions, and finally concatenating the resulting submatrices along the batch dimension. This can be expressed as:
2. = B((((C)) @ f e f( +
(5)
nx(C+3)
where fxx) @ x² represents the concatenation of prediction and coordinates for points with class
i, and n is the number of valid voxels. The [ notation denotes the concatenation operation along the batch dimension. The output for decoder D is reconstructed [f(x)); is used to optimize for this process without backpropagating the gradients f.
Eq
Compared to the K x D codebook VQ-VAE, we use class-specific sub-codebooks size Cxkx x D, where & is the number of latent variables in each sub-codebook, and D is the dimension of the latent variables. Each latent variable in the sub-codebook can considered as modeling a specific local semantic distribution pattern under corresponding class. The encoder E models the p (=)) which represents the prior distribution of the latent variables = given the predicted probabilities fx)) The decoder ID models the (x) which represents the posterior distribution of reconstructing the predicted probabilities f(x) conditioned on the latent variables Through this process, the autoencoder can model the network's semantic confusion online during the training process.
3.6 SEMANTIC SHIFT REGION LOCALIZATION
Excessive augmentation can cause semantic shift in certain regions, leading abnormal prediction results, as shown in Fig. (b). After modeling the semantic confusion prior, we can assess whether the network's 's predictions for the augmented point cloud conform to the normal semantic distribution patterns, thus treating the localization of semantic shift regions an anomaly detection problem.
During latent learning process, we dynamically track the representation distribution of each quan- tized latent variable. For each e,, we maintain statistics of its corresponding latent embedding distri- bution before quantization and store variance of of each channel, which is updated using exponential moving average. The representation distribution of each can be expressed as:
r(ex) = (ee,, diag(oo,,
ofp)));
(6)
Next, we use the frozen prior latent encoder E. to map predictions of augmented point cloud latent embedding space. Since the encoder models the class-wise p(2(f(z)). embeddings from regions affected by semantic shift will not fall into the representation distribution of their corresponding cob-codebooks. We locate the semantic shift regions by checking whether the embeddings lie within the representation distribution of their nearest latent variable in corresponding sub-codebooks.
After distinguishing all the embeddings, we remap them back to the prediction space, i.e., f(ă). to determine the semantic consistency regions (SCR) and semantic shift regions (SSR) in the prediction space. We use two masks to represent these two regions:
MSCR 1 1(ze ((N))s)())))).
Mssn = 1 (ze & r ((N
(8)
where 2, represents the latent embeddings of the augmented point cloud predictions, NN denotes the nearest latent variable of 2., and (.) is the indicator function.

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Published as a conference paper at ICLR 2026
3.7 OPTIMIZATION STRATEGIES
After determining the SCR and SSR, we assign different optimization strategies. For SCR, we use the original labels:
(c Mscn,y y @ Mscn) Lee MsCR;
where @ denotes element multiplication. For SSR, we propose latent variable- based dis- tillation loss to provide appropriate supervisory signals. Instead of querying the nearest neighbor from sub-codebook of the corresponding class, we obtain the closest semantic confusion pattern prior for this region by querying from global codebook. Then, we use this prior to distill the latent embeddings of SSR:
Ldistiti = ||=-- Nooooat
(10)
where N. ggoobal (2e) denotes the global nearest neighbor latent variable. Eq 10 similar to previous commitment loss while we do not update the encoder E, but backpropagate the gradient to the network f. Our total training loss is:
Cintal + Eoe + ALaistsll
(11)
where À is hyperparameter balancing loss terms. In this way, the semantic confusion prior learned from the original point cloud can provide meaningful supervisory signals for semantic shift regions, enhancing the model's performance on augmented data while preserving semantic consistency. For further implementation details, discussions and algorithm flow, please refer to Appendix B
E
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We four datasets: SemanticKITTI (Behley al. 2019 SynLiDAR (Xiao et al. 2022b), SemanticKITTI-C (Kong et al., 2023b), and SemanticSTF (Xiao et al. 2023). Se- manticKITTI: 19,130 training scans (sequences 10-10, except 08), collected in urban environments under standard weather conditions. @ SynLiDAR: 198,396 synthetic scans (19 billion points) gener- ated using Unreal Engine @ SemanticKrrTI-C: Corrupted version of SemanticKITTI generated via simulation. ! SemanticSTF: 2,076 LiDAR scans from STF (iijelic et al., 2020) under adverse weather (snow, dense fog, light fog, rain), split into 1,326 training, 250 validation, and 500 testing scans. SemanticKITTI and SynLiDAR serve as source domains, while SemanticKITTI-C and Se- ManticSTF assess robustness as target domains. We denote SemanticKITTI as [A], SynLiDAR as [B]. SemanticSTF as [C]. and SemanticKITTI-C as [D] for brevity.
Evaluation Metrics. We adopt MinkowskiNet- MinkkoskkNett-1/22iiddt (Choy et al. 2019) as the base model and also evaluate SPVCNN (Tang al. 2020). Performance is measured by Intersection over Union (IoU) per class and mean IoU (mloU) across classes, including breakdowns by weather conditions.
Implementation Details. The segmentation network is trained with SGD (learning rate 0.24, weight decay 0.0001), and the autoencoder with Adam (learning rate 0.001). The sub-codebook size is set to 32. These networks are updated alternately. For augmentation, 0.8] and jitter standard deviation to [0.01, 0.05). Training runs for 50 epochs with a batch size of on an RTX 3090 (24 GB). The balancing coefficient is set 0.1 maintain gradient stability.
set the drop ratio (0.2,
4.2 COMPARISON WITH EXISTING METHODS
Overall Quantiative Results. Tab. 12 compare A3Point with state-of-the-art domain generaliza- tion methods on the [A] - (C) and 73| [C] benchmarks. The baseline is trained only with C.e- A3Point outperforms all methods, achieving 9.9% and 11.7% mloU improvements over the baseline. Class-wise analysis shows that A3Point performs particularly well on safety-criticall classes such as motorcycle, bicyclist, traffic sign, and car, which typically challenging due to unique geometries and susceptibility to adverse conditions. The local pattern encoding in VQ- VAE and region -seccffcc optimization enhance robust feature capture for these classes under diverse weather conditions.
Weather-level Comparison. As shown in Tab. 12 A3Point demonstrates superior robustness across all weather conditions, with substantial leads even in the most severe cases like dense fog
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Table 1: Comparison results of [A] + [C].
denotes the reproduced result with the same backbone.
Methods
mloUi goin
61.2
19.0
58.
45.6
63.6
$1.9
61.6
57.9
Baseline
52.7
93.9
12.7
50.7
PainDDR Xino al
2023
69.2
41.9
48.9
26
87
15.9
32
40
7
46.
540
43.9
23.7
57.3
31.5
35.5
26.9
+2.5
DGUU Kimera
Dizz
78.2
2.5
33.0
20.7
61
40.8
088
40.9
67.1
7.2
18
2.
70.8
54.4
64.1
56.8
52.1
51.0
40.0
56.1
54.5
55.5
55.3
37.6
46.2
WADG U68
72.0 0.0
52.9
57.0
19
57.7
68
52.9
59.9
10.7
31.8
2.2
76.0
48.5
02.7
54.0
42.
23.6
20
59.5
52.5
51.7
29.4
34.8
43.4
DGLSS
57.6
36.2
LiDARWescor
29.1
26.6
55.4
50.4
63.7
37.9
5.5
75.2
52.7
60.
44.9
30.1
40.8
36.0
37.5
37.6
33.1
NTN Park ol. 2725
83.3
3.7
51.3 362
18.2
53.3
6.8
55.9
62.2
18.1
37.2
54
72.1
41.8
58.0
36.0
46.0
26.2
39.8
55.3
55.1
55.7
52.4
38.9
47.5
A3Pvint (ours)
88.3
4.1 57.5 29.0
7.6 45.3 24.0
46.4
69.2
16.9
38.4 33 74.8
48.
63.1 42.9 49.2
32.6
41.8
41.1
38.5
38. 37.2
41.3
+9.9
Table Comparison results of [B] + [C]. denotes the reproduced result with the same backbone.
bulld
mrnnk
D-fog
Rain
Snow
Methods
gain
Oracle
42.4
1
0.0
99.9
61.2
69.6
39.0
0.0
82.2
21.5
58
45.6
86
63.6
52.0
77.6
50
61.7
51.9
54.6
57.9
53.7
Baseline
333
0.2
25.5
1,6
34
15.3
9.2
168
0.0
219
39.5
7
44.0
83
0.8
15.2
16.0
16.8
12.8
155
+0.0
PoinDDR Nisovet al 2023]
41.1
2.8
34
18.1
0.2
31.3
2
1.3
34
10.2
19.7
1.0
52
220
4
21
58
192
S6
19.1
20.3
25.3
19.0
19.
+4.3
WADG Durta 2029)
33.8
299
17.0
26.8
4.3
53.9
5.0
2.2
64.3
53.8
27.0
57.0
286
86
2.66
23.4
27.2
21.4
21.9
+6.4
LIDA Reeeler
19.0
21.2
17.3
NTN
Park
24
29.1
8.9
43.5
20.5
0.0
49.8
20.0
$2.9
24.7
7.5
+3.6
A3Polxt (ours)
5.0
34.1
33.3
28.1
14.8
26.8
26.6
31,9 28.6
+11.7
and heavy snow. The extensive augmentation space and adaptive latent-space distillation enable A3Point handle wide spectrum of weather disturbances while preserving predictions in less corrupted regions, resulting in better performance than other methods in milder conditions.
Qualitative Results. Fig presents a visual comparison of A3Point with previous methods un- der challenging weather conditions like snow and dense fog. A3Point shows superior ability to accurately segment major scene components such as sidewalk, road, and terrain, which are often obscured distorted in adverse weather. Moreover, it exhibits better segmentation of complex in- stances like car and traffic sign, which are critical for safe navigation. The local pattern encoding helps capture the unique geometries of these objects, while the decoupling of semantic confusion and shift enhances the discriminative performance of these classes.
LIDARWeother
Snow
Mas
D-fog
Drop Cadt
.iter
Figure 4: Qualitative results on [A] - [C]. Signifi cant improvements are marked with boxes.
Figure 5: Mask ratio of SSR under differ- ent augmentation levels.
4.3 ABLATION STUDY
See Appendix F fooo more analyses, discussions, and visualizations.
Additional Results. To validate the effectiveness and generalizability of A3Point, we conduct ex- periments across different architectures and benchmarks (Tab. B). Results show consistent gains.
With SPVCNN, A3Point achieves + +12.0%, +8.5%, and +2.2% mloU on [A] + [C]. [B] + [C]. and [A] + [D], respectively. Using Minkowski, the improvements are +9.9%, +11.7%, and +5.9% mloU. Notably, improvements are larger on the real adverse weather benchmark [C] than on the synthetic corruption benchmark [D], indicating superior robustness to real-world disturbances. The consistent gains across architectures highlight the approach's 's architecture-fries frienndy nature, making it broadly applicable to various LiDAR segmentation networks.
Effectiveness of Components. We conduct an ablation study on the [A][B]]*[C] benchmark to evaluate the impact of A3Point's components (Tab. 4 For [A]-+C],, the baseline without aug- mentations achieves 31 31.4% mloU. Introducing the enhanced augmentation space (EAS) improves performance to 38.7%, highlighting the importance of diverse augmentations for domain robustness. Applying semantic shift region localization and masking to only optimize semantic consistency re- gions (SCR) with Zoe further improves mloU to 40.2%. Finally, incorporating latent variable-based
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Table 3: Different architectures & benchmarks.
Table 4: Ablation study of A3Point components.
Method
SPVCNN
Minkowski
None EAS (& SCR SSR (Ca [A]
[C] [B]
[c]
bwseline
w/ A3Piin
boseline
A3Pvmni
28.1
60.1 (+12.0)
31.4
41.3 (+9.9)
24.9
17.3
(+8.5)
15.5
(+1.7)
401
26.5
525
(4.7()
(45.9)
41.3
27.2
Table 5: Ablation study of augmentation level. We define different levels (light, moderate, and heavy), same as Fig [
Table 6: Performance comparison different strategies for modeling semantic confusion prior.
Method
None
light
moderate
heavy
random
[A] + [A]
62.7
61.2
59.7
55.5
60.4
w/ A3Point
62.6
62.4
61.0
58.7
62.5
Strategy
[A]
[C] [B]
[C]
[A] + C]
31.4
37.6
38.0
37.1
38.7
None
38.7
24.9
w/ A3Point
31.8
38.5
40.4
40.5
41.3
GT--based
39.1
25.0
[B] + [C]
15.5
19.3
23.6
24.1
24.9
Offline
40.7
26.4
w/ A3Point
15.6
20.7
25.1
26.4
27.2
Online (ours)
41.3
27.2
distillation loss Ldiststt for semantic shift regions (SSR) achieves the best result of 41.3% mloU. A similar trend is observed for [B]-{[], where the baseline starts at 15.5% mloU. EAS significantly boosts performance to 24.9%, bridging the large gap between synthetic and real adverse weather data. Adding SCR improves mloU 26.5%, and the full model with SSR reaches 27.2%. These results demonstrate the complementary nature of our components across domain generalization sce- narios. By leveraging learned semantic confusion priors to guide semantic shift optimization, the model adapts to novel disturbances while preserving semantic consistency.
We analyze the impact of augmentation levels on network perfor- mance using the [A] validation set ([A]+{[A]) and cross-domain benchmarks [A][[B]-[C] (Tab. [5) On [A], baseline performance drops from 62.7% to 55.5% mloU as augmentation increases from none to heavy, showing the adverse effect of severe augmentations. In contrast, A3Point main- tains higher performance, with only a slight decrease from 62.6% to 58.7%, demonstrating its ability to mitigate the negative impact of aggressive augmentations by handling semantic shift regions. For [A]-[C], augmentations improve baseline performance from 3 31.4% to 38.7% mIoU, confirming their role in enhancing robustness. However, slight drop from 38.0% 37.1% from moderate to heavy augmentations suggests emerging semantic shift issues. A3Point avoids this drop. achiev- ing 40.5% mloU with heavy augmentations and 41.3% mloU with random augmentations. Similar trends appear in [B]->[C], where A3Point achieves larger relative gains augmentation intensity increases. The performance gap over the baseline grows from 1.4% with light augmentations to 2.3% with random augmentations. These findings confirm that A3Point effectively handles seman- tic shift, allowing the use aggressive augmentations without harming performance. Its ability to balance source and target performance makes it well-suited for real-world applications.
Analysis of Augmentation Level.
Analysis of Semantic Confusion Prior. Tab. compares different strategies for modeling semantic confusion prior on [A][[B]+[C] benchmarks. For [A]-+[C], the GT-based method, using one-hot ground truth labels, models only class-wise shape distribution but lacks inter-class confusion knowl- edge, yielding marginal 0.4% mloU gain. The offline method, which uses pre-trained model's predictions on the source domain, improves mloU by 2.0% but struggles to capture evolving confu- sion patterns during training. Similar trends occur in [B]++[], where the GT-based method provides minimal gains (25.0% vs. 24.9%), and the offline method achieves better but limited improvement (26.4% vs. 24.9%). In contrast, our online modeling approach continuously updates the prior in- formation, effectively decoupling semantic confusion in augmented point clouds. This dynamic strategy achieves the best performance: 41.3% mloU on [A]-{C] and 27.2% mIoU on [B]+[C] outperforming the baseline by 2.6% and 2.3%, respectively. By continuously adapting to evolv- ing confusion patterns, A3point mitigates semantic shifts and consistently outperforms statie priors, demonstrating the importance of dynamic prior modeling in framework.
Analysis of Semantic Shift Region. 5ig shows how the SSR mask ratio varies with augmen- tation level. As the augmentation intensity increases, the mask ratio of the localized SSR grows accordingly. This observation aligns with our expectation that stronger perturbations lead to more significant semantic shift in the augmented point clouds, resulting in larger proportion of the input being identified as belonging to the SSR. The upward trend in the curve suggests that our proposed SSR localization module effectively captures the regions most affected by the augmentation-induues

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semantic shift, enabling the network apply appropriate optimization strategies in these to mitigate the impact the shift and enhance the model's 's robustness to adverse weather conditions.
5 CONCLUSION
This paper presents A3Point, an adaptive augmentation-awar latent learning framework for robust LiDAR semantic segmentation under adverse weather. A3Point effectively leverages large aug- mentation space while mitigating semantic shift through a two-step strategy: (1) semantic confusion prior latent learning, which encodes local confusion patterns through discrete latent representations, and (2) semantic shift region localization, which detects anomalies in augmented point clouds to separate semantic consistency from semantic shift regions, enabling targeted optimization. Experi- ments on domain generalization benchmarks demonstrate its effectiveness, particularly in general- izing from synthetic normal weather real adverse weather.
ACKNOWLEDGEMENTS
This work partially supported by the National Key R&D Program of China (Grant No. 2024Y)B3009002). National Natural Science Foundation of China (Grant No. U25A20536), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (CAS).
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A APPENDIX
This appendix provides additional details and analyses complement main paper. We include implementation details, ablation studies, and additional visualization results. Specifically, the ap- pendix is organized follows:
Section B Implementation: Semantic Confusion Prior (SCP) discrete latent learning (VQ- VAE design, per-class quantization, losses, and reconstruction).
Semantic Shift Region (SSR) localization and anomaly detec- tion strategy, mask generation, and dilation.
Section C
Implementation:
Discrete vs. continuous encodings-why discrete representations better match our prior and interpretability goals.
Section D
Discussion:
Section Algorithm: A3Point training pseudocode and end-to-end optimization pipeline.
Section Augmentation: Impact analysis of additional data augmentation techniques and conclusions.
Section G Hyperparameters: Sensitivity and trade-offss of key A3Point hyperparameters (k:, D,t,Ar X)
Section Visualization: SSR visual analysis and association with regions.
Section Visualization: More qualitative results.
Section Evaluation: Training-time SSR-region evaluation and inference-time high- distortion subregion evaluation.
Training overhead: Extra cost during training and zero-overhead inference state- ment.
Section K
Section L Comparison: Feasibility of replacing VQ-VAE with prototypes (K-prototype) and sources of performance gaps.
Section Schedule:: Comparison between 50-epoch and 15-epoch training schedules and their effects.
Section Nonffusinnnn Things-class confusion matrix and frequency- gain relationship anal- ysis.
Section O Universality: Commonality of semantic confusion across datasstss/rcchitecturss and the role online priors.
Section Rare classes: Oracle's 0.0 IoU phenomenon on extremely rare classes and A3Point's stabilizing effect.
EAS: "Safe training" capability under larger augmentation spaces and its upper bounds.
Section O
Section
R
Iteratie: Curriculum-style augmentation ceilings and iterative A3Point.
Section Fairness: Comparisons after equipping prior methods with stronger augmenta-
tions.
T Codebook: t-SNE visualization and interpretation of per-class VQ-VAE sub- codebooks.
Section
Section U Method comparison: Controlled comparisons with Mean Teacher, SCE, etc., under the same augmentation space.
Section V Design choice: Ablation verifying why "global nearest code" for distil- lation supervision within SSR.
Section W Class mapping across datasets: Unified 19-class protocol and exact mappings for [B/[C]
Section
X
Impact: LLM usage notes and societal impact discussion.
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B IMPLEMENTATION OF SCP LATENT LEARNING
In this section, we provide the implementation details of our semantic confusion prior (SCP) latent learning module, which is based on the vector quantized variational autoencoder (VQ- VAE) archi- tecture (Van Den Oord et al. 2017). The goal is to learn a discrete latent representation that captures semantic confusion patterns in the original point cloud predictions.
B.I VQ-VAE ARCHITECTURE
The VQ-VAE consists of an encoder, decoder, and vector quantization layer. The encoder net- work maps the input point cloud features concatenated with the predicted probabilities f(x) to latent representation že. We use sparse 3D convolutions (Tang et al. (020) in the encoder to process the sparse point cloud data efficiently. The architecture is as follows:
The encoder has 4 sparse conv3d downsampling blocks with channel dimensions [16, 32, 64, 128]. Each block consists of stride-2 sparse conv3d, batch norm, and LeakyReLU. This downsamples the point cloud by 16x.
An additional sparse conv3d, batch norm and LeakyReLU, followed by 2 residual blocks to further process the features.
A final sparse conv3d to map the latent dimension (64).
The decoder network reconstructs the predicted probabilities f(x) from the quantized latent repre- sentation 2q. It follows mirrored architecture to the encoder:
A sparse conv3d, batch norm and LeakyReLU to map from the latent dimension to the initial feature dimension (128).
2 residual blocks for further processing.
4 sparse transposed conv3d upsampling blocks, each consists of stride-2 transposed sparse conv3d, batch norm and LeakyReLU. This upsamples the features by 16x back the orig- inal resolution.
A final sparse conv3d and tanh activation to reconstruct the predicted probabilities.
B.2 PER-CLASS VECTOR QUANTIZATION
To avoid inter-class confusion, perform vector quantization independently for each semantic class. The VQ codebook contains learnable embeddings for each class, with dimensions C' D, where C' is the number of classes, & is the number of embeddings per class, and D is the latent dimension.
During the forward pass, split the encoder output 2e into class-specifis latents based on the class labels. For each class, the latents are quantized to the nearest embedding in its corresponding sub- codebook using 1.2 distance. The distances embeddings of other classes masked out to prevent selecting incorrect classes.
B.3 COMMITMENT LOSS AND EMBEDDING Loss
The learning objective combines a commitment loss and an embedding loss, following the original VQ-VAE:
The commitment loss encourages the encoder output 2e to commit to the selected embed- dings Zg. and is defined as:
Loommit = ||= sg(2,))l1
(12)
The embedding loss brings the selected embeddings 2y close the encoder output 2g, and is defined as:
Lembed [|sg(ze) ) 2/12
(13)
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where sg stands for the stop-grme hoop-gradeent operation. The total VQ loss is: Lvo Coommit + 8 emmeed
(14)
where 3 is weighing coefficient (set to 0.25).
B.4 RECONSTRUCTION Loss
To ensure the quantized representation can reconstruct the input predicted probabilities, we employ a mean squared error reconstruction loss:
Lreamn = llf(z) 2
(15)
The final loss is weighted combination of the reconstruction loss and the VQ losses:
C = Crecon LvQ
(16)
In summary, our SCP latent learning module utilizes a VQ-VAB architecture with sparse convolu- tions to learn a discrete latent representation of semantic confusion patterns in class-wise manner. The model is trained end-to-end using combination of reconstruction loss and VQ losses. This allows capturing informative priors on the semantic confusion from the original point cloud predic- tions.
C IMPLEMENTATION OF SSR LOCALIZATION
In this section, describe the implementation details of our semantic shift region (SSR) local- ization module. The goal is to identify regions in the augmented point cloud where the network's predictions deviate from the learned semantic confusion priors, indicating potential semantic shifts caused by the augmentations.
C.1 TRACKING LATENT REPRESENTATION DISTRIBUTIONS
During the training of the SCP latent learning module, we track the distribution of latent repre- sentations associated with each quantized latent variable (i.c., each embedding in the codebook). Specifically, for each latent variable 6,, we calculate the variance of of its corresponding latent representations before quantization. The variance is updated using an exponential moving average (EMA) with a momentum factor 7 (set to 0.9):
of - No + (1 7) Var(ze/zg es)
(17)
where Var(-) denotes the variance operation. This allows us to estimate the typical distribution of latent representations for each latent variable.
C.2 LOCATING SEMANTIC SHIFT REGIONS
To locate the semantic shift regions, we first pass the augmented point cloud through the trained encoder obtain its latent representations ze. We then compare each latent representation the distribution of its nearest latent variable in the corresponding sub-codebook.
Specifically, for each latent representation z find its nearest latent variable eNN() in the sub- codebook of the corresponding class. We consider 2f be an outlier (i.e., belonging semantic shift region) if its distance from ENNG) exceeds a threshold based on the tracked variance o NNG)
isOutlier 2!) = 1 (112 - -eNG))ll > t t (NG))
(18)
where 1(-) is the indicator function and is hyperparameter controlling the threshold (set to 3). The intuition is that if a latent representation deviates significantly from the typical distribution of its nearest latent variable, it likely corresponds semantic shift region.
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C.3 GENERATING SSR AND SCR MASKS
After identifying the outlier latent representations, we project them back to the point cloud space to generate masks for the semantic shift regions (SSR) and semantic consistency regions (SCR).
We first create binary mask Moutier in the latent representation space, where M M isOutlier(z)). We then use the transpose of the point cloud downsampling operation (used in the encoder) to upsample the mask back to the original point cloud resolution. This gives us the SSR mask MSSR.
The SCR mask is simply the complement of the SSR mask:
MsCR
MssR
(19)
C.4 HANDLING SPARSE OUTLIERS
In practice, the outlier latent representations may be sparse and scattered. To ensure the SSR mask covers semantically meaningful regions, we perform a dilation operation on the SSR mask. Specif- ically, for each point (ar, y. z, c) in the SSR mask, we set its neighboring points within certain radius r to also belong to the SSR. This helps to connect nearby outlier points and form contiguous semantic shift regions.
The dilation operation can be efficiently implemented by first upsampling the outlier mask to a dense 3D grid, performing dilation the grid, and then downsampling the dilated mask back to the point cloud using nearest-neighbor interpolation.
In summary, SSR localization module identifies semantic shift regions in the augmented point cloud by comparing the latent representations to the learned semantic confusion priors. It gener- ates SSR and SCR masks to guide the subsequent training with appropriate losses for each region. The module is computationally efficient and can be integrated into the training pipeline without significantly increasing the training time.
D DISCUSSION: DISCRETE VS. CONTINUOUS ENCODING
A key aspect of the proposed Semantic Confusion Prior (SCP) is explicitly obtaining the distri- bution form of the representation for subsequent localization and optimization. This section dis- cusses the differences between discrete and continuous encoding methods and explains why discrete encoding is more suitable for our framework.
D.1 COMPARISON OF ENCODING PARADIGMS
Discrete Encoding (e.g., VQ-VAE). VQ-VAE (Van Den Oord et al. (017)) learns discrete latent space by: 1) Mapping the input to a latent space via encoder. 2) Quantizing the latent space into a codebook, effectively clustering representative features.
Continuous Encoding Methods. Continuous representation methods can be categorized into two types: (1) Fixed Prior Distribution Paradigms (e.g., VAE (Kingma et al., 2013), Flow-based Models (Rezende & Mohamed, 2015; Kingma & Dhariwal, 2018)) These methods constrain the latent space to a fixed prior distribution (e.g.. Gaussian) to achieve implicit global distribution alignment. However, this alignment lacks interpretability and cannot naturally decouple different semantic classes, often requiring separate encoders and decoders per class. (2) Unconstrained Latent Representations (e.g., AE (Rumelhart et al., 1986), DAE (Vincent et al., 2008), SDAE Vincent et al., 2010) These methods learn continuous latent representations without predefined prior, making it difficult to directly extract structured distribution information. As a result, clustering techniques may still be needed to impose structure, similar to VQ-VAE's discrete encoding.
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Algorithm Pseudo Algorithm of A3Point
1: Inputs: Source domain Ds = {(x? yi )
2: Define: Network Je. Autoencoder IR, D. Codebook C, Augmentation function A, Learning rates
a, [3 6, Loss weight
3: Output: Trained model fe
4: for each batch (1: v in D3 do
5: Step I: Augmented Training
6: Apply augmentations: (..) D ) A(x?, ,
7: Compute supervised loss: Lie = tee (fe(z;)) ).
Eq. (1)
8:
Compute augmented loss: Loe = loe (fo( ).9)
Eq. (2)
9:
Step 2: Semantic Confusion Prior Learning
10:
Compute latent embedding: Ze B((fo(a;)) @ 2
11:
Quantize latent code: = quantize(Z)) using codebook
Eq. (3)
12:
Reconstruct prediction: x. = (2₂))
13:
Update VQ-VAE loss: Log = 1|=; 2 + + [|sg(z.) - 20112 ||=0 sg(z,)
Eq. (4)
14:
# Step 3: Semantic Shift Region Localization
15:
Track variance statistics for latent codes o² using EMA
16:
Map augmented predictions latent space: = وg49 () #:91) Identify semantic shift regions (SSR) via anomaly detection:
17:
Semantic consistency mask: MSCR = (2q r(N Nan (2 (20 Semantic shift mask: MSSR = 1(ze & CrNNNNNNN Neub(=2
Eq. (7)
19:
Eq. (8)
20:
Step 4: Optimization
Compute loss for SCR: -on= eon ( ④ Mscn, vi @ MSCR)
Eq. (9)
22:
Compute distillation loss for SSR: Ldiistill =uu sgl N global (= (
b Eq. (10)
Update model: t- VVoeCee Lee + XLdistatt
b Eq. (11)
24:
end for
D.2
WHY DISCRETE ENCODING IS NECESSARY?
While discrete encoding limits the number of latent patterns to k. this does not hinder our frame-
work's effectiveness. Instead, it provides several advantages:
Better Interpretability: Each latent code represents specific semantic confusion pattern, making easier analyze and interpret the learned representations.
Class-wise Separation: VQ-VAE allows natural clustering of latent representations per class, which crucial for our semantic shift localization.
Robustness and Generalization: A well-designed codebook ensures that only the most represen- tative and meaningful patterns are learned, improving generalization unseen data.
E PSEUDO ALGORITHM OF A3POINT.
We further provide detailed algorithmic description in Alg.
F IMPACT OF OTHER AUGMENTATION TECHNIQUES
To further analyze the role of various augmentation techniques, conduct experiments to evaluate their impact on both source and target domain performance.
F.1 EXPERIMENTAL SETUP
We follow the same training setup as in Section 3.3.2, varying the intensity of secondary augmen- tations such as scaling, flipping, and noise perturbation, while keeping point deformation and point loss augmentations unchanged. We define three augmentation levels: Light: Default augmentation settings used in main experiments. Moderate: Increasing the magnitude of scaling and flip-
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ping while maintaining the original distribution. Heavy: Aggressively increasing scaling factors and noise perturbation levels.
F.2 RESULTS AND ANALYSIS
Tab. presents the results on the [A]~>[C] and [B]-|[C] benchmarks.
Augmentation Level
[A]++[C]
[B]-[[C]
[A]+{A]]
Light
41.3
27.2
62.5
Moderate
40.1
26.0
60.3
Heavy
37.5
24.5
55.8
Table 7: Impact of different augmentation levels on domain generalization performance.
From the results, we observe that: Excessive secondary augmentations degrade both source and target performance. Increasing the augmentation magnitude from Mild to Heavy reduces mIoU by 3.8% on [A]-[C] and 2.7% on [B]-X[]. Moderate augmentations provide limited benefit. Compared to the Mild setting, Moderate augmentation slightly reduces performance, indi- cating that secondary augmentations do not contribute significantly to domain adaptation. 3. Source domain performance deteriorates with stronger augmentations. On [A]-[A], mloU drops sig- nificantly from 62.6% to 55.8%, suggesting that excessive scaling and noise perturbation disrupt the original data distribution and hinder learning.
These findings confirm that point deformation and loss (jittering, point drop) are the primary drivers of domain adaptation, while other augmentations are actually domain-agnostic factors and should be applied conservatively to avoid performance degradation.
G INFLUENCE OF PARAMETERS SETTING
In this section, we discuss the key hyperparameters of our A3Point framework and their impact on the performance and behavior of the model. All experiments conducted SemanticKITTII> semanticSTF.
G.1 LATENT SPACE DIMENSTONS
The dimensions of the latent space in the SCP latent learning module, determined by the number of embeddings per class (k) and the embedding dimension (D), affect the expressiveness and granular- ity of the learned semantic confusion patterns.
mloU (%)
16
64
39.9
41.3
64
64
41.4
32
32
40.8
32
64
41.3
32
128
41.2
Table 8: Ablation study on latent space dimensions.
In our implementation, we set 32 and 64 (Table 8). These values provide a good trade- off between the representational power of the latent space and the computational efficiency of the module. Increasing allows the model to capture more diverse semantic confusion patterns within each class, but it also increases the memory footprint and the risk of overfitting. Similarly, increasing D enhances the capacity of each embedding to encode more complex patterns but also increases the computational overhead.
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G.2 SSR LOCALIZATION THRESHOLD
The threshold used in the SSR localization module determines the sensitivity of detecting semantic shift regions.
t=
2
t=
mloU (%)
41.0
41.3
40.9
Table 9: Ablation study on SSR localization threshold.
In our experiments, we set which effectively identifies the regions with substantial semantic shifts while minimizing false positives. A higher value of results in a more conservative approach, where only the most significant deviations from the learned semantic confusion patterns are consid- ered as semantic shifts. Conversely, lower value of i makes the module more sensitive, potentially identifying more regions as semantic shifts.
G.3 Loss WEIGHTING COFFFICIENT
The loss weighting coefficient À balances the contributions of the cross-entropy loss and the distil- lation loss the overall training objective.
0.02
À = 0.1
X 0.5
mloU (%)
40.5
41.3
40.9
Table 10: Ablation study loss weighting coefficient.
In our experiments, we found that setting À = 0.1 achieves a good balance between the two loss terms. A higher value of İ gives more importance the distillation loss, encouraging the model to focus more on aligning the predictions in the semantic shift regions with the learned semantic con- fusion patterns. On the other hand, a lower value of emphasizes the cross-entropy loss, prioritizing the overall segmentation accuracy.
H VISUAL ANALYSIS OF SSR
To better understand how our method identifies semantic shift regions (SSR), we conduct a detailed visual analysis comparing the error maps with the detected SSR, as shown in Fig. 6 The error map highlights regions where the model's predictions differ from ground truth labels. while SSR indicates areas identified by our semantic shift detection mechanism.
From the visualization results, we observe a strong correlation between the SSR and regions prone to prediction errors. Specifically: (1) Our SSR detection effectively captures areas where adverse weather conditions cause significant semantic ambiguity, particularly at object boundaries and dis- tant regions where point cloud density decreases. (2) The SSR often corresponds to challenging scenarios such as intersections between different semantic categories (e.g., road-sidewalk bound- aries) and areas with complex geometric structures. (3) The semantic consistency regions (non-SSR areas) generally align well with regions where the model maintains accurate predictions, validat- ing our approach's 's ability to identify reliable predictions. This visual analysis demonstrates that our SSR detection mechanism provides meaningful guidance for applying different optimization strategies during training.
I MORE QUALITATIVE RESULTS
To further demonstrate the effectiveness of our proposed method, we present additional qualitative results comparing approach with baseline methods the challenging Se-
domain generalization task, as shown in Fig. 7 These qualitative results further validate the effectiveness of our semantic confusion prior learning and semantic shift region localization strategies in improving domain generalization performance for LiDAR semantic segmentation.
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Error map
Radio 11.6
7M
Ratio
SSR
Figure 6: Qualitative results on comparison between error map with semantic shift region.
J TARGETED EVALUATIONS IN SSR AND HIGH-DISTORTION REGIONS
Appendix visualizes the correlation between SSR and error maps, showing substantial overlap with error error-prone prone areas. To more directly quantify improvements, we add two complementary evalu- ations:
Training-time SSR-region evaluation (approximate mloU): We use training time sam- ples and their SSR masks to generate fixed evaluation masks and track mloU changes within SSR-region:
Generate a fixed evaluation mask MSSRLeval using the same rules (we use the prior encoder and variance statistics obtained from the EAS-only setting).
Because GT in SSR unreliable (by definition, labels are misaligned after strong aug- mentation), we follow semi-supervised practice and maintain an EMA (mean-teacher) model as a proxy evaluator. EMA preferable to GT here because the EMA provides stable, denoised target that tracks the model's long-term belief without inheriting the semantic misalignment introduced by strong augmentations. Better agreement with the EMA in SSR indicates improved consistency and reliability precisely where GT is noisy.
As shown in Fig compared to EAS-only, A3Point's predictions within MSsRLeval ex- hibit higher agreement with the EMA teacher. In particular, adding SSR distillation drives predictions to align more closely with the teacher within SSR regions.
Inference-time high-distortion subregions evaluation: As shown in Fig 9 we approxi- mate "high-distortion" subregions using density/curvature thresholds. In these subregions, A3Point improves the top-5 frequent classes by 1.9-15.1 points and raises average IoU by 7.6 over EAS. Construction details:
For each point i, find k--N (1) with 1: = 32.
Local (ensity(i) ~ inverse of the distance to the k-th nearest neighbor.
Local curvature via PCA on (i): compute the covariance matrix E,, obtain eigen- values A 2 A2 2 A 2 0, define curvature(i) = As/(A (A1 + X2 + Aa).
Aggregate point-wise density/curvature to voxels 1 via mean to obtain density(v) and curvature(v).
- Define the high-distortion indicator using quantile- based thresholds (selecting 20% hardest regions):
(ighDist(() = [(density(() S Ta V curvature(i) 2 Tc)
Within these high-distortion masks, A3Point outperforms EAS, as shown in Table
These analyses support that our method not only improves overall metrics but also meaningfully enhances performance within the high-risk regions detected during training.
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PoinDDR
LiDeRWeather
A3Point
GT
Snow
L-fog
D-fog
Figure 7: Qualitative results on [A] [C], where improvements marked with boxes.
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Mssa_ eval
EAS
90
A3Point
80
70
Value
60
50
40
30
Index
Figure 8: Agreement with EMA teacher within MSsR
Snow
Rain
L-fog
D-fog
Love densty
High STEVoERR
Figure 9: Example scenes showing "high-distortion" subregions.
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Table 11: Performance within high-distortion subregions.
Method
Car
Road
Sidewalk
Vegetation
Terrain
Overall mloU
EAS
61.6
50.7
21.4
47.5
35.7
27.1
A3Point
76.7
57.6
26.8
51.5
37.6
34.7
K TRAINING-TIME OVERHEAD
A3Point introduces lightweight, class-wise VQ-VAE module--inplemmntee with sparse convo- lutions, code dimension of D 64, and per-class codebook size of k 32 (see architecture details in Appendix B.I to enable SCP and SSR during training. Below we detail the practical implications and costs:
No inference-time overhead. Both SCP and SSR training-only components. They disabled at test time, so inference latency and memory footprint are identical to the baseline model. The exported checkpoint and forward path remain unchanged.
Low-cost statisties via EMA. We maintain per-codeword statisties (variance) using an exponential moving average (EMA). This update is ((1) (1) per iteration with negligible extra memory and does not grow with dataset size batch length.
Frozen prior for SSR localization. The encoder used to localize SSR regions kept frozen during localization and does not backpropagate. This design bounds the incremental compute to lightweight forward pass and avoids gradient overhead, keeping utilization stable.
To show the cost clearly, we report representative runtime snapshot under identical hardware and training settings in Table 12] Given the substantial mloU gains on SemantieSTF (+9.9/ / +.7. over the baseline in [C] and [B]- we regard the training-time increase as favorable trade: concentrate budget on the training side to obtain stronger adverse-weather weather robustness, while keeping the inference path clean and unchanged.
Method
GPU Memory (MB)
Iterations/s (it/s)
Total Time (h)
Baseline
8,496
2.72
24.4
PointDR
9,624
1.97
33.7
EAS
8,932
2.05
32.4
A3Point (ours)
17,144
1.61
41.3
Table 12: Training-time overhead comparison under identical hardware and settings.
L ON REPLACING VQ- VAE WITH A PROTOTYPE-BASED ALTERNATIVE
We evaluate whether simple prototype-based approach can replace the VQ- VAEVAE prior. Specifically, we implement a class-wise K-prototype variant (k 32) as a drop-in alternative to the VQ-VAE module. Under the same training budget and augmentation settings, VQ-VAE consistently performs better, as summarized Table
Setting
[A]-[C]
[B]+[C]
None
31.4
15.5
EAS
38.7
24.9
Prototype-based
40.0
25.4
VQ- VAE-based (ours)
41.3
27.2
Table 13: Comparison of class-wise K-prototype versus VQ- VAE prior under identical settings.
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We attribute the gap three advantages of the VQ-VAE prior:
Intra-class multimodality. Error/ambiguity patterns within class are inherently multi- modal (e.g.. lane markings near curbs, partial occlusions, far-range sparsity). Per-class codebooks learned by VQ-VAE capture multiple modes more faithfully than a single pro- totype centroid, yielding more accurate localization of risky subregions.
Well-formed anomaly modeling. VQ-VAE supports per-codeword variance tracking (via FMA), producing compact, approximately Gaussian local clusters and an interpretable "prior radius" " for anomaly detection. Prototype methods typically require additional metric-learning losses or finely tuned thresholds to behave well and can be brittle across conditions.
Online adaptivity and stability. Our prior is updated online in syne with the evolving backbone while the SSR-localization encoder remains frozen for backprop, providing sta- ble yet responsive statisties. Prototype updates can lag drift the feature space shifts, degrading SSR localization quality over training.
In short, VQ-VAE offers more robust, interpretable, and empirically stronger mechanism for mod- eling class- wise priors, delivering consistent gains at only modest additional training cost.
M TRAINING SCHEDULE: 50 EPOCHS vS. LIDARWEATHER' 15 EPOCHS
We follow a PointDR-like schedule of 50 epochs for all main results and ablations (Tables 3 to en- sure internal fairness. To disentangle schedule length from algorithmic gains, we conduct controlled comparisons under both short (15-epoch, LiDARWeather-stt and long (50-epoch) schedules.
Short schedule (I5 epochs) Under 15-epoch schedule (Table 14) A3Point still surpasses our LiDARWeather reproduction by +2.7-4.5 mloU with identical training budgets, indicating that our gains are not contingent on longer training.
Method
[A]-IC]
[B]-IC]
Baseline
31.4
15.5
LiDARWeather
37.8
20.4
A3Point (ours)
40.5
25.9
Table 14: 15-epoch (LiDARWeather-style)) training.
denotes the reproduced result.
Long schedule (50 epochs) Extending Baseline and LiDARWeather to 50 epochs (Table 15) yields only modest improvements and both remain below A3Point, suggesting that schedule length alone does not close the gap.
Method
[A]+{[C]
[B]-[C]
Baseline
32.3
16.1
LiDARWeather*
38.6
21.6
A3Point (ours)
41.3
27.2
Table 15: 50-epoch training. 'denotes the reproduced result.
N CONFUSION MATRIX FOR "THINGS" CLASSES
include confusion matrices and per-class point counts for both the Oracle and A3Point on Seman- ticSTF in Fig 10 to clarify how class frequency relates to performance gains. Our key observations are as follows:
Frequency is not a sufficient predictor of gains. Classes such as person and motor- cyclist, despite nontrivial aggregate frequency, frequently suffer from severe occlusions,
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(a) Oracle Confusion Matrix
(b) A3Point Confusion Matrix
Grounn ThuI
Ground
Prediction
Prediction
Figure 10: Confusion matrices SemanticSTF. Left: Oracle. Right: A3Point.
pose/shape variability, and far-range sparsity. Under adverse weather, these factors exacer- bate inter-class confusions (e.g.. person vs. polelsign, motorcyclist vs, bicycle/motorcyylee, effectively reducing the number of reliable points per scene and limiting the attainable im- provements.
Larger gains for classes with distinctive, stable geometry. Categories such as bicycle, motorcycle, traffic sign, and car preserve clearer geometric signatures under perturbations (e.g., edges, planar surfaces, and repeatable part configurations). Our SCP/SSR priors help maintain consistency in these regions, leading to stronger improvements, consistent with the trends reported in Tables
Overall, the confusion matrices reveal that robustness gains are better explained by the stability and distinctiveness of local geometric cues under weather shifts, rather than by raw class frequency alone.
O UNIVERSALITY OF SEMANTIC CONFUSION
This section clarifies how "semantic confusion" manifests across datasets and architectures with three complementary points:
1. Dataset-driven patterns vs, model-driven intensity. Across architectures, which classes tend to be confused is largely governed by dataset geometry, density, and sampling statis- tics (e.g., road-sidewalk, car-Iruck). By contrast, the magnitude of confusion (confidence spread, error rates) depends more the model's capacity, regularization, and training dy- namics. In our setup. SCP learns confusion priors online on the source domain for each backbone; for a fixed backbone, the confusion pattern remains stable while its intensity shrinks as the main network improves.
2. Architectural alignment via online priors. A3Point learns class-wise discrete priors from the current model's predictions on the original domain (via VQ-VAE) and freezes them for anomaly detection on augmented samples. This "online fit, frozen detect" design keeps the priors aligned with the model's evolving decision boundary and avoids cross-architccture mismatch.
3. Empirical evidence across backbones and settings. Table 3 shows consistent gains two voxel backbones (MinkowskiNet- ([A]-[[), [B]+ [A]-[D]] While absolute confusion varies with the backbone, A3Point consistently improves robustness.
18t and SPVCNN) and three DG tracks
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Furthermore, additionally evaluate multi- (offline) priors obtained by aggregating confu- sion statistics from an ensemble of three independently trained nource-domait models and freezing this prior during training. As summarized in Table 16 [A]+[C] mloU increases from 38.7 (no prior) to 40.7 with an offline prior and to 41.0 with an ensemble-based offline prior; on [B]->[C), mloU rises from 24.9 to 26.4 and 26.9, respectively. Nonetheless, both variants remain inferior to our online prior-which co-evolves with the current model's decision boundary-achievinn 41.3 on [A]-[C] and 27.2 on [B]-IC]. We attribute this gap to fundamental mismatch: offline priors are static and cannot track the evolving decision boundary, calibration, and feature geometry during training (especially under strong augmentations), whereas our online prior updates on the origi- nal domain and is only frozen for detection augmented data, preserving alignment throughout training. These findings are consistent with our analysis in the main text (lines 456-467; see also Table 6
Strategy
[A]~IC]
[B]
None
38.7
24.9
Offline
40.7
26.4
Offline (ensemble)
41.0
26.9
Online (ours)
41.3
27.2
Table 16: Comparison of offline and online confusion priors. Online (ours) consistently yields the best mloU by maintaining alignment with the evolving decision boundary.
P ORACLE 0.0 IOU ON RARE CLASSES VS. A3POINT IMPROVEMENTS
Revisiting the Oracle results on [C], the observed 0.0 IoU for motoryyde/moltoeeeeli arises from reproducibility instability under extreme class imbalance and cross-weather variation. Multiple in- dependent reruns yield non-zero IoUs with substantial variance, indicating that 0.0 is variance artifact rather than a systematic failure. Five independent runs are shown below.
hiclle
mi.ole
tuck
ath X
parka.
sidew.
otherg
build.
1ense
pole
mnf.
I
42.1
59.9
61.2
69.6
39.0
0.0
82.2
21.5
58.2
45.6
86.1
63.6
80.2
77.6
50.1
61.7
54.7
2
89.6
37.4
5644
64.3
67.3
81.8
27.9
54.6
50.1
86.5
66.2
78.3
56.3
75,4
45.8
58.6
55.3
3
89.7
41.8
4.0
58.6
62.8
66.
40.7
0.0
82.0
23.4
51.8
47.2
86.7
64.9
78.7
59.1
75.0
49.0
57.0
54.6
50.2
59.5
5
89.7
39.9
6.8
58.1
62.1
61.6
48.5
11.1
76.4
21.7
51.4
43.5
83.1
61.3
77.2
63.4
74.9
55.3
56.4
54.9
Table 17: Oracle on [C]: per-class IoU across five independent runs. Rare classes (motorcycle, motorcyclist) exhibit high variance; 0.0 IoU appears some runs but not others.
This instability also explains why other methods (including the baseline) show noticeable gains on mootoryclelmotorcyyiistt training on source domain [A] supplies stable, well-represented examples that mitigate scarcity on [C]. The effect is dataset-driven and broadly observable, not exclusive to single algorithm.
Beyond dataset coverage, A3Point contributes two components that particularly help long-tailed, smoll-footprint classes under strong perturbations:
Exposure via EAS. The augmentation space diversifies point density and pose, improving recall for fragile classes that otherwise suffer from sparsity and occlusion long range.
Safer supervision via SSR. When augmentations induce semantic shift, SSR replaces hard supervision with latent-space distillation toward the best-matching code. This prior- consistent guidance reduces label observation misalignment and stabilizes training for rare categories.
In practice, augmentation-heatt baselines already improve rare classes relative to a plain Oracle; SSR's latent guidance further reduces false positives and harmful updates. The net effect steadier
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gains motooeyccellmotoreyeliss and similar rare categories. The revised text clarifies the distinction between dataset-induced improvements and -3Point-specific stabilization.
Q EFFECTIVENESS FOR SAFELY ALLOWING A LARGER AUGMENTATION SPACE
We add experiments directly assess whether A3Point enables safe expansion of the augmentation space.
Scope of the default EAS. The default ranges already span common street-scene Li- DAR degradations. Pushing beyond these bounds (e.g., extreme point-drop ratios or jitter) quickly becomes physically implausible and risks label-geometry mismatch, which can corrupt supervision.
Stress test with arbitrarily large ranges. We expand jitter std to [0. 0.10) and point-drop ratio to [0, 0.99] (none-excessive), keep all other augmentations unchanged, and evaluate [A]-[C].
Method None light moderate heavy random (light-heavy) excessive random* (none-excessive)
Baseline 31.4 37.6
38.0
37.1
38.7
5.1
36.5
A3Point 31.8 38.5 40.4
40.5
41.3
7.8
41.2
Table 18: Stress test with enlarged augmentation ranges on [A]+[C]. Random: sampled within light-heavy; Random3: sampled within none-excessive.
As shown in Table 18 we have key observations:
Excessive augmentation without SSR is harmful. With very large ranges, the baseline suffers severe degradation, consistent with mis-supervision induced by semantic shift. SSR stabilizes training under heavy distortions. A3Point maintains strong perfor- mance when sampling randomly across none-vexcessive, because SSR localizes shifted regions and switches to latent-space distillation to avoid harmful updates.
There is hard upper bound set by physics. When drop ratio + 1 and jitter std + 0.1, inputs cease resemble plausible LiDAR scans; performance drops for all methods, indicating the practical limit of useful augmentation.
Evidence of adaptive behavior. As shown in Fig the SSR mask ratio increases with augmentation strength, indicating that the detector adaptively flags more regions as shift intensifies. This adaptivity enables safe use of stronger augmentations: regions consistent with the learned confusion prior receive standard supervision, while shifted regions receive guidance.
soft,
In summary, A3Point expands the usable augmentation envelope by mitigating mis-supervision where semantic shift occurs, while respecting a physically grounded ceiling beyond which aug- mentation is no longer beneficial.
R EXTENSION: ITERATIVE A3POINT WITH VARYING AUGMENTATION SPACE
Iteratively refining confusion prior (e.g., a confusion matrix or SCP) can further enhance robustness and generalization. The main limitation of purely offline priors is their static nature: similar to the multi-model ensemble in QI, they cannot track the evolving decision boundary, calibration, and capacity of the current network. In practice such static priors often misalign with the model's confidence distribution and error modes modes- especially early in training wradiiggleaddig to inferior results compared with online evolution.
In contrast, our approach updates the confusion prior online on the original domain and freezes only for anomaly detection augmented inputs. This design does not require perfect prior;
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instead, it maintains alignment with the model's current state throughout training. Empirically, the adaptive online prior yields better stability and higher final mloU than offline confusion matrices (see Table with particularly larger gains for long-tailed classes and long-range sparse regions.
To evaluate a curriculum-style extension, we implement an iterative variant of A3Point that expands the augmentation space in stages. The training proceeds in three phases: start with light augmenta- tion ceiling and train for several epochs; use the resulting SCP as initialization and raise the ceiling moderate; finally, proceed to heavy ceiling. Thus, the augmentation ceilings progress light moderate + heavy, and each stage initializes with the SCP learned in the previous one. Results are summarized below.
Strategy
[A]~>CC]
[B]-IC]
None
38.7
24.9
A3Point
41.3
27.2
A3Point+ (curriculum)
41.5
27.8
Table 19: Iterative, curriculum-style A3Point that expands the augmentation ceiling in stages (light
- moderate - heavy).
As shown in Table 19 the iterative scheme offers modest additional gains when expanding to larger augmentation space. is a promising direction-using SCP statistics to adapt augmentation distributions and SSR thresholds in closed-loop schedule-whil the principal improvements still stem from maintaining online alignment rather than from offline aggregation.
S STRONGER AUGMENTATION FOR PREVIOUS METHODS
To ensure fairness, prior sugmentation-baut methods are re-run with the same upper bounds as our Enhanced Augmentation Space (EAS): point drop ratio sampled in [0.2, 0.8) and jitter in [0.01, 0.05], with their original pipelines otherwise unchanged.
Method
[A]-[C]
[B]+[C]
Baseline
31.4
15.5
Baseline EAS PointDR* PointDR & EAS
38.7
24.9
33.9
19.8
39.1
25.1
LiDARWeather
37.8
20.4
LiDARWeather** EAS
39.6
25.9
A3Point (ours)
41.3
27.2
Table 20: Re-running prior methods under the same EAS upper bounds. result.
denotes the reproduced
Under stronger augmentations, prior methods do improve, but gains saturate and remain below A3Point. Expanding the augmentation space alone is insufficient unless semantic shift is explic- itly handled; SSR localization and latent distillation make stronger augmentation genuinely usable.
T VISUALIZING THE LEARNED CODEBOOK
We visualize the learned VQ-VAE codebook using t-SNE, as shown in Fig 11 Each per-class sub- codebook is rendered in distinct color; marker size encodes the per-code variance tracked via EMA, providing an at-a-glance sense of prototype stability and coverage.
Consistent patterns observed visualizations:
Intra-class multimodality. Within each class, prototypes form multiple clusters that align with distinct local geometry and context configurations (e-g., road-sidewalk bound-
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(a) t-SNE visualization of all classes
(b) t-SNE visualization of selected classes
150
Figure 11: t-SNE of the learned VQ-VAE codebook. Colors denote class-specific sub-codebooks; marker size encodes per-code variance.
aries, car-vegetation occlusion edges, terrain discontinuities). The codebook captures fine- grained, recurring confusion modes rather than collapsing to a single class template.
Inter-class neighborhoods. Prototypes from semantically similar or commonly confused categories (e-g., road vs. sidewalk, building vs. fence) lie proximity, reflecting typical confusion manifolds encountered by the segmentation network.
Progressive specialization. Over training, prototypes become more compact and better separated along confusion-specifie axes, indicating increasing specialization. This sharp- ening improves anomaly detection for semantic shift: embeddings that deviate from well- formed confusion clusters are more easily identified as outliers.
By encoding confusion manifolds explicitly in latent space, the codebook provides principled basis for distinguishing genuine semantic confusion (expected, class class-consistent variability) from augmentation-inddued induced semantic shift (label-geometry mismatch). Rather than memorizing class means, it organizes a discrete atlas of confusion patterns that SSR localization can leverage to decide when predictions consistent versus shifted.
U DIRECT COMPARISONS WITH OTHER DG METHODS.
We conduct controlled comparisons within the same augmentation space as A3Point. Specifically:
Mean Teacher on augmentations (MT): the student receives strongly augmented inputs, while the teacher sees the original or weakly augmented inputs; KL divergence consis- tency loss is applied.
Symmetric Cross Entropy (SCE): label-noise robust objective.
Method
[A]
[B]+[C]
Baseline
31.4
15.5
EAS EAS MT
38.7
24.9
39.0
25.5
EAS SCE
38.8
24.6
A3Point (ours)
41.3
27.2
Table 21: Comparisons with other DG methods under the same augmentation space.
As shown in Table 21 have observations:
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MT and SCE yield modest gains but do not explicitly identify handle augmentation- induced semantic shift under heavy perturbations, leading to overfitting to misaligned pseudo-targets necessitating weaker augmentations.
A3Point combines (i) class-wise semantic confusion priors learned from unaugmented pre- dictions, (ii) latent-space anomaly detection to localize semantic-shift regions (SSR), and (iii) region-adaptive adaptive supervision, yielding more stable improvements and higher mloU on heavy-weather targets.
V WHY GLOBAL NEAREST CODE FOR SSR?
Motivation. Heavy augmentations can introduce label-semantics misalignment during SSR. Us- ing class-conditional nearest code anchors supervision to the original class codebook, potentially reinforcing bias from incorrect labels. contrast, a global nearest code provides a class-agnostic, cabilltty-oriented prior that aligns shifted regions to the most compatible confusion mode in latent space, without being dragged by the (possibly wrong) original class.
Variant
[]-[C]
[B]-[C]
Baseline
31.4
15.5
EAS
38.7
24.9
Class-conditional nearest code (SSR)
38.8
24.5
Soft CE on logits with temperature (SSR)
39.7
25.8
Consistency with pre-augmentation prediction
37.2
24.4
A3Point (global nearest code in latent space)
41.3
27.2
Table 22: Controlled comparisons of SSR supervision choices under the same augmentation space. Global nearest code (class-agnostic latent alignment) performs best.
Interpretation. We perform controlled comparisons in Table 22
Class-conditional nearest code. Keeps supervision tied to the original class-specific code- book. It does not resolve lebal-semantics mismatch under strong augmentations and re- mains susceptible to original-class bias, yielding negligible improvement over EAS.
Soft CE with temperature logits. Temperatuur--calee soft cross-entropy reduces over- confidence and partially alleviates semantic shift, giving slight gains. However, remains label-conditioned and cannot fully correct mismatches.
Pre-augmentation consistency. Using the pre-augmented prediction teacher is vul- nerable when augmentations induce semantic shift; enforcing such consistency reinforces unreliable alignment target, degrading performance relative EAS.
Conclusion. Our class-agnostic Ahjective-distilllan toward the global nearest code in latent space for SSR-explicilly decouples supervision from potentially incorrect labels and aligns shifted regions with the most compatible semantic confusion prior, consistently achieving the best perfor- mance.
W CLASS MAPPING ACROSS DATASETS
We follow the standard 19-class evaluation protocol shared by SemanticKITTI (source [A]). Syn- LiDAR (source [B]), and SemantieSTF (target [C]). All datasets are mapped to a unified 19-class taxonomy with IDs 08:8; ID 255 denotes "ignore" and is excluded from loss and metrics.
W.1 UNITEED 19-CLASS TAXONOMY
ID Class:
0 car, bicycle, motorcycle, 3 truck, other-vehicle,
person, 6 bicyclist, motorcyclist 8 road, 9 parking,
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10 sidewalk, 11 other-ground, 12 building, 13 fence,
14 vegetation, 15 trunk, 16 terrain, pole, 18 traffic-sign
W.2 [A] SEMANTICKITTI: NATIVE LABELS AND KEPT SET
Label-to-name (native).
saael__nae__mapeee samppnnaapingggggg {
0: unlabeled',
1: 'outlier',
10: ' Ycar',
11: 'bicycle',
13: 'bus',
15: mmotorcycle',
16: cn-raiis',
18: 'truck',
20: otherr-ehicce',
30: person',
31: bicyclist',
32: motorcyclist',
40: road',
44: paarinn''
48: sidewalk',
49: 'other-ground',
50: 'bilddin',
51: 'ence',
52: oothersttructure'
60: lane-marking',
70: vegetation',
71: 'tuuk',
72: 'terrain',
80: 'pole',
81: traffic-sign',
99: oothe-object',
252: moving-car',
253: 'moving-bieyclist",
254: 'moving-persoon',
255: ' moviingrmatorcyellsst,,,
256: 'moving-on-rails",
257: moving-bus',
258: 'moving-truck',
259: mevingg-tther-ehhilee
Kept labels for the 19-class protocol.
Lept_labels
"road', dewwalkk'' ''pariin', 'other-ground', 'bui 'builddng'' 'car', "truck', "bicycle', 'motorcycle', oother-vehicle! /eveetttinn 'trunk', terrain', ppersnn', 'iiccclist' /motorcyclist! 'fece' pole', /traffic-sign'
Notes.
Moving classes (e.g., 252-259) are mapped to their static counterparts (e.g., moving-car + car) before applying the 19-class filter, following the official protocol. Categories outside the 19-class set are assigned to 255 (ignore).
W.3 [B] SYNLIDAR + UNIFIED 19-CLASS IDs
dearning_mapi
0: 255 unlabeled ignore
1: $ car car
2: & pick-up truck
3: $ truck truck
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4: # bus other-vehicle
5: 1 bicycle bicycle
6: 4 motorcycle motorcycle
7: $ other-vehicle * other-wchicle
8: + road + road
9: sidewalk sidewalk
10: parking + parking
11: 11 other-ground other-ground
12: 5 & female person
13: male person
14: kid person
15: 5 $ group person
16: $ bicyclist bicyclist
17: 7 & metorcyclist + motorcyclist
18: 12 building building
19: 255 # other-structure ignore
20: 14 vegetation * vegetation
21: 15
trunk trunk
22: 16
terrain terrain
23: 18
traffic-sign traffic-sign
24: 17
pole pole
25: 255
traffic-cone ignore (net in 19-class)
26: 13
fence fence
21: 255
garbage-can ignore
28: 255
electric-box ignore
29: 255
table ignore
30: 255
chair + ignore
31: 255
bench + ignore
32: 255
other-object ignore
W.4 [C] SEMANTICSTF + UNIFIED 19-CLASS IDs
learning_map
0: 255, # unlabeled + ignore
1: 0, $ car
2: 1, bicycle
3: 2, motorcycle
4: 3, & truck
5: 4, i other-vehicle
6: 5, person
7: 6, bicyclist
8: 7, & motorcyclist
9: 8, $ read
10: 9, parking
11: 10, sidewalk
12: 11, other-ground
13: 12, building
14: 13, fence
15: 14, vegetation
16: 15, trunk
11: 16, terrain
18: 17, $ pole
19: 18, # traffic-sign
20: 255
invalid ignore
X LLMS AND SOCIETY IMPACT
Use of Large Language Models (LLMs). In preparing this manuscript, we used LLMs solely for language polishing and writing assistance (e.g.. clarity, grammar, and style). LLMs were not used to generate research ideas, experimental results, code, analyses, and proprietary data sensitive information were provided to LLMs beyond the manuscript text. All technical content, experiments, and conclusions were produced and verified by the authors.
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Within this paper, present approach for domain- LiDAR semantic segmen- tation under adverse weather. Our contributions focus on robustness to distribution shifts via augmentation-awwe aware latent learning and semantic shift localization. At present, not aware of direct negative societal implications arising specifically from the proposed methodology.
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Reviews:
[{"review_id": "6i1cHutGui", "reviewer": "Reviewer_wTLP", "summary": "This paper focuses on augmentation-based methods among existing approaches for domain generalization under adverse weather conditions. This paper assert that previous augmentation-based methods suffered from the issue that strong augmentations caused semantic shifts, which interfered with training. In this study, the authors propose a framework using VQ-VAE to distinguish between semantic confusion and semantic shift, and they claim to address the problem by introducing a distillation-based loss that prevents semantic shift from disrupting the training dynamics.", "questions": "See the above.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tNew idea\n2.\tClearly identifies the weaknesses of existing weather-augmentation approaches\n3.\tThe method that separates SSR from SCR is highly sophisticated\n4.\tThrough performance gains and ablations, the authors show strong effectiveness for LiDAR semantic segmentation under adverse weather. Notably, there are large improvements on SynLiDAR → SemanticSTF, where prior methods achieved limited gains.", "weaknesses": "1.\tThe method appears applicable to domain generalization beyond adverse weather; there is no compelling reason it must be evaluated specifically on SemanticSTF.\n2.\tBy the same logic, even if strong augmentation produces SSR, wouldn’t training those regions with the original ground truth still be effective? Why deliberately block cross-entropy learning there? Is there any convincing reason that training with GT in SSR is not good? In real weather conditions, distortions can be just as strong.\n3.\tIt is unclear whether the method actually performs well within SSR regions (i.e., the detected regions during training, or the highly distorted areas within a single sample at inference).\n4.\tTraining time appears to be very long.\n5.\tCouldn’t a simple prototype-based method separate SCR and SSR without a VQ-VAE? If not, why not? If feasible, that alternative would likely incur much lower training cost.\n6.\tOn LiDARWeather the training schedule uses 15 epochs, but you train for 50. Why? The gains might simply come from longer training.\n7.\tIn Table 1, among the “things” classes, person and motorcyclist presumably have more samples and should be easier to improve, yet they are not; in my knowledge, this is unexpected. Please provide the confusion matrix.\n\n(Minor)\n1.\tIn “Weather-level Comparison” of experiment, please indicate which table the reader should consult.\n2.\tIn Table 1, the experimental results for LiDARWeather and NTN appear to be swapped.\n3.\tXiao et al., 2023 primarily proposed SemanticSTF; it is not the work that established point dropping and jittering as the main disturbances in LiDAR data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "m4kcgYg3Gh", "reviewer": "Reviewer_79Cq", "summary": "This paper tries to answer an important question in LiDAR augmentation for adverse weather: how to utilize a larger augmentation space while mitigating semantic shift. In this paper, A3Point is introduced, an adaptive augmentation-aware latent learning framework for point cloud semantic segmentation, based on disentanglement of semantic confusion and semantic shift. Experiments on domain generalization benchmarks demonstrate the effectiveness of A3Point, particularly in transferring from normal weather to a wide range of adverse weather conditions.", "questions": "(Effectiveness on safely allowing a larger augmentation space) I’m curious to see if the proposed semantic shift detection pipeline is effective in enabling the utilization of “a large augmentation space”. A trivial approach for enlarging the augmentation space might be to train models with multiple augmentation spaces (hyperparameter search). But considering the augmentation space is multi-dimensional with many variables, it’s not a scalable way. In this regard, the proposed method seems to have a capability to safely allow a larger augmentation space by detecting and handling semantic shifts from augmentation, so that the performance drop caused by the use of too excessive augmentation space can be mitigated. If so, it would be great to provide an experiment on training with arbitrary large augmentation spaces lead similar performance.\n\n(Extension of A3Point) As the semantic confusion matrices from baseline and A3Point must be different, the A3Point method can be treated as a training framework to gradually improve confusion matrix of a learned model. It leads an idea of iterative applying A3Point framework with varying augmentation space. Adding a discussion on this could suggest a future research direction to readers.\n\n(Lack of experiment setting) The class mapping between the source datasets [A, B] and the target dataset [C] is not clearly explained.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(Problem definition) This paper defines two factors that affect to the prediction performance: semantic confusion and semantic shift. The proposed framework is based on a clear observation that that semantic confusion is consistent across domains (raw and augmented data) while semantic shift occurs only in augmented data.\n\n(Effectiveness)  Experimental results verify the effectiveness of the proposed framework under various conditions, consistently outperforming previous approaches.", "weaknesses": "(Universality of semantic confusion) As the semantic confusion is identified using a learned model, the semantic confusion might have a sort of dependency with the learned model. For example, I guess the semantic confusion identified based on the consistency in predictions (the key idea of this paper) is not consist across models used for prediction with different discriminative power. (i.e., the confusion matrix shown in Figure 2-(a) could be different when a smaller/larger model is used for prediction.) In-depth discussion on this would be recommended. In addition, can we take a benefit from identifying universal semantic confusion using multiple learned models, if possible?\n\n(Dependency of semantic shift to model) In Figure 2(b), how to encode each point to draw t-SNE visualization? If a learnable encoder was used, then the distribution shift in t-SNE may be due to the semantic confusion (or lack of discriminative power) of the encoder, too. I agree that there could be “semantic shift” due to an augmentation (e.g., if an augmentation removes too many points on a “bus” object so that only a single large plane is remained, then there is no way to distinguish the augmented points between bus and wall; i.e., an inherent information that makes the set of points (bus) different from the other classes is damaged.), but the t-SNE visualization doesn’t seem to confirm the existence of semantic shift in a strict sense.\n\n(Efficiency) The discrimination of SCR and SSR is performed from inferences on augmented data, which requires additional computation for learning encoder/decoder in SCP latent learning and computing SSR localization. According to Table 6, online training of SCP learning leads the best performance, which means the additional network (for SCP latent learning) has to be trained simultaneously and severe inefficiency during training is caused. \n\n(Lack of analysis) In Table 1 and 2, Oracle (training on target domain) shows 0.0 IoU on certain classes: motorcycle, motorcyclist, but this is improved a lot in A3Point (e.g., 0.0 → 57.5 IoU, 0.0 → 46.4 IoU). What makes this drastic improvement?\n\n(Concern about overclaiming) The paper claims that the proposed module is architecture-agnostic; however, experiments were only conducted on SPVCNN and MinkowskiNet, both voxel-based models. It would be interesting to see if the semantic confusion and shift happen in a similar way on projection-/point-based approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ey9VyRYCfc", "reviewer": "Reviewer_GuJ4", "summary": "LiDAR semantic segmentation networks struggle in adverse weather (fog, snow, rain) due to distribution shifts. Existing augmentation-based methods face a dilemma: (1) Mild augmentations fail to simulate severe weather. (2) Aggressive augmentations cause semantic shift (labels no longer match distorted regions).\n\nThis paper introduces two distinct error sources: (1) Semantic Confusion: Network's inherent difficulty distinguishing similar classes (e.g., road vs. sidewalk) - exists in both original and augmented data. (2) Semantic Shift: Augmentation-induced mismatch between labels and distorted regions - only exists in augmented data. The proposed method A3Point contains two main parts: (a) Semantic Confusion Prior (SCP) Latent Learning, which use VQ-VAE to learn discrete latent representations of confusion patterns. (b) Semantic Shift Region (SSR) Localization, which treats semantic shift detection as anomaly detection in the discrete latent codebook space and use the frozen encoder to map augmented predictions to latent space for Semantic Shift Regions (SSR) detection. With the detected semantic shift region, the paper propose a distillation loss.", "questions": "1. Could the authors provide a quantitative evaluation of  A3Point's computational and memory consumption overhead during training?\n\n2. Could the authors provide a more in-depth analysis on the proposed distillation loss? Or maybe conduct additional experiments on simply eliminate the detected region in the loss computation.\n\n3. It could be better if the authors could provide experiment results using previous methods with stronger augmentation.\n\n4. Could the authors provide some visualization of the learned VQ-VAE codebook? For example, using different colors to indicate different entry in the codebook.\n\nThe reviewer is willing to raise the score if the questions are well-resolved.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. A3Point is well-motivated and the proposed semantic shift detection method makes a lot of sense.\n\n2. The experiments are thorough and extensive.\n\n3. The results show the effectiveness of the proposed method.", "weaknesses": "1. The authors do not discuss the computational efficiency during training. To the reviewer's understanding, the VQ-VAE part and distillation loss part only appear during training and are discarded during testing. Thus the computational efficiency during testing remain the same with other methods. However, a quantitative evaluation of the computational and memory consumption overhead during training is not provided.\n\n2. Although demonstrated by the experiment results, the reviewer is concerned about the design of the distillation loss. The reviewer understand the detection of semantic shift region part and agree that the proposed method makes sense. However, after detecting the semantic shift region, it might be more reasonable to directly eliminate those points in the loss instead of the proposed distillation loss.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eJWfp3M9gt", "reviewer": "Reviewer_My2N", "summary": "The authors proposes an adaptive augmentation-aware latent learning framework to enhance single-source domain generalization for LiDAR semantic segmentation in poor weather conditions. The main idea is to separate semantic confusion that is built into the model from semantic shift that is caused by augmentation. Per-class VQ-VAE learns discrete \"semantic confusion priors\" from predictions that haven't been augmented, and then a frozen encoder uses latent-space anomaly detection (diagonal Gaussian around each code, threshold t) to find semantic shift on augmented scans. Training takes into account the regions: cross-entropy on semantic-consistent regions (SCR) and latent-space distillation toward the global nearest code on semantic-shift regions (SSR). Coupled with a broadened augmentation space (wide point-drop ratios and jitter std), A3Point yields consistent, sizable gains on SemanticKITTI→SemanticSTF and SynLiDAR→SemanticSTF, improving mIoU over a Minkowski baseline by +9.9 and +11.7, respectively, with similar improvements on SPVCNN and on SemanticKITTI-C.", "questions": "Please make sure that no SemanticSTF target frames, even unlabeled ones, were used in any way during training. Is there any hyperparameters were chosen using [C]-val.\n\nWhy pick the global nearest code as the SSR goal? Please give us a controlled comparison: global vs. class-conditional nearest code; latent-space distillation vs. soft CE with temperature on logits; and consistency with the original (pre-augmentation) prediction.\n\nCould you compare the parameter size and, if possible, the FLOPs of the baseline and the A3Point modules to prove that the performance gains are not mostly due to the increased model capacity and not the proposed learning scheme.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Originality: Decoupling model-inherent confusion from augmentation-induced shift through a per-class discrete latent prior, followed by regional supervision, transcends traditional augmentation and consistency frameworks.\n\nQuality: Consistent, sizable mIoU gains on SemanticKITTI→SemanticSTF and SynLiDAR→SemanticSTF (+9.9/+11.7 over Minkowski baseline), plus improvements on SemanticKITTI‑C, with thorough ablations of components and hyperparameters.\n\nClarity: The pipeline, losses, and masks (SCR/SSR) are clearly defined, and figures 1–3 and the appendix algorithm help make it possible to reproduce the results.\n\nSignificance: It is very important for safety that things are strong enough to handle bad weather. The method works with any architecture (SPVCNN, Minkowski) and uses latent modeling during training without changing the architectures used for inference.", "weaknesses": "Direct comparisons to consistency-regularization or teacher–student DG variants (e.g., mean-teacher on augmentations), label-noise robust losses, or distributionally robust objectives are absent, making relative advantage boundaries less clear.\n\nThe dataset set [A][B][C][D] are defined after the performance tables, which can be confusing for readers when reading the performance tables. \"SCP\" should be where \"SCR latent learning module\" is. There is an extra \"s\" in the caption for Table 1. Appendix I/J has two copies of \"More Qualitative Results.\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)