Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation

Wangkai Li; Zhaoyang Li; Yuwen Pan; Rui Sun; Yujia Chen; Tianzhu Zhang

cs.CVMar 1, 2026

Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation

Authors: Wangkai Li, Zhaoyang Li, Yuwen Pan, Rui Sun, Yujia Chen, Tianzhu Zhang

Organizations: University of Science and Technology of China · National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory

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.

Figures & tables

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 augmentation levels, showing that aggressive distortions lead to significant performance degradation.

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 $\rightarrow$ veg. ).

Figure 3: Pipeline of A3Point. We explore an abundant augmentation space (Sec. 3.4 ) and propose two key components: SCP latent learning to capture inherent semantic confusion (Sec. 3.5 ) and SSR localization to decouple semantic shift (Sec. 3.6 ).

Methods	car	bi.cle	mt.cle	truck	oth-v.	pers.	bi.clst	mt.clst	road	parki.	sidew.	other-g.	build.	fence	veget.	trunk	terr.	pole	traf.	D-fog	L-fog	Rain	Snow	mIoU	gain
Oracle	89.4	42.1	0.0	59.9	61.2	69.6	39.0	0.0	82.2	21.5	58.2	45.6	86.1	63.6	80.2	52.0	77.6	50.1	61.7	51.9	54.6	57.9	53.7	54.7	-
Baseline	67.1	5.0	28.1	38.5	14.6	45.8	8.3	13.8	40.1	16.1	26.1	3.3	71.6	52.7	53.8	33.9	39.2	25.3	12.7	30.7	30.1	29.7	25.3	31.4	+0.0
PointDR ∗ ( Xiao et al., 2023 )	69.2	1.0	8.9	41.9	7.6	48.9	17.0	36.2	57.8	15.9	32.3	4.0	75.7	46.4	54.0	36.2	43.9	23.7	24.2	37.3	33.5	35.5	26.9	33.9	+2.5
DGUIL ( Kim et al., 2023 )	78.2	2.5	33.0	29.7	6.1	49.8	0.8	40.9	67.3	7.2	38.0	2.2	79.8	54.4	64.1	36.8	52.3	31.0	40.0	36.3	34.5	35.5	33.3	37.6	+6.2
WADG ( Du et al., 2024 )	72.0	0.0	32.9	37.0	1.9	37.7	6.8	52.9	59.9	10.7	31.8	2.2	76.0	48.8	62.7	34.0	49.3	23.6	20.4	39.5	32.5	31.7	29.4	34.8	+3.4
DGLSS ( He et al., 2024 )	69.6	0.8	42.8	34.4	8.9	41.9	12.8	44.5	52.0	14.5	30.8	6.0	77.8	51.1	57.6	38.9	43.2	29.7	30.6	34.2	34.8	36.2	32.1	36.2	+4.8

Table 1: Comparison results of [A]

\rightarrow

[C].

*

denotes the reproduced result with the same backbone.

Methods	car	bi.cle	mt.cle	truck	oth-v.	pers.	bi.clst	mt.clst	road	parki.	sidew.	other-g.	build.	fence	veget.	trunk	terr.	pole	traf.	D-fog	L-fog	Rain	Snow	mIoU	gain
Oracle	89.4	42.1	0.0	59.9	61.2	69.6	39.0	0.0	82.2	21.5	58.2	45.6	86.1	63.6	80.2	52.0	77.6	50.1	61.7	51.9	54.6	57.9	53.7	54.7	-
Baseline	33.8	1.7	3.3	15.5	0.2	25.5	1.6	3.4	15.3	9.2	16.8	0.1	33.4	21.9	39.5	18.7	44.0	8.8	0.8	15.2	16.0	16.8	12.8	15.5	+0.0
PointDR ∗ ( Xiao et al., 2023 )	41.1	2.8	3.4	18.1	0.2	31.3	2.8	3.3	34.4	10.2	19.7	1.0	52.7	22.0	48.5	21.3	38.3	19.2	5.6	19.1	20.3	25.3	19.0	19.8	+4.3
WADG ( Du et al., 2024 )	33.8	1.1	2.9	17.0	0.2	26.8	1.0	4.3	53.9	5.0	20.6	2.2	64.3	27.1	53.8	27.0	37.0	28.6	8.6	21.6	23.4	27.2	21.4	21.9	+6.4
LiDARWeather ( Park et al., 2024 )	39.3	2.9	0.9	19.4	0.8	27.7	2.2	3.8	42.5	9.4	21.6	0.3	51.9	33.5	47.4	23.1	33.3	23.2	6.8	19.0	21.2	23.1	17.3	20.5	+5.0
NTN ( Park et al., 2025 )	48.4	1.5	2.4	19.4	0.2	29.1	3.2	8.9	43.5	6.7	20.5	0.0	52.2	30.1	49.8	20.0	32.9	24.7	7.5	-	-	-	-	21.1	+5.6

Table 2: Comparison results of [B]

\rightarrow

[C].

*

denotes the reproduced result with the same backbone.

Table 7

Table 8

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Augmentation Level	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]	[A] $\rightarrow$ [A]
Light	41.3	27.2	62.5
Moderate	40.1	26.0	60.3
Heavy	37.5	24.5	55.8

Appendix

Table 7: Impact of different augmentation levels on domain generalization performance.

$k$	$D$	mIoU (%)
16	64	39.9
32	64	41.3
64	64	41.4
32	32	40.8
32	64	41.3
32	128	41.2

Appendix

Table 8: Ablation study on latent space dimensions.

	$t=2$	$t=3$	$t=4$
mIoU (%)	41.0	41.3	40.9

Appendix

Table 9: Ablation study on SSR localization threshold.

	$\lambda=0.02$	$\lambda=0.1$	$\lambda=0.5$
mIoU (%)	40.5	41.3	40.9

Appendix

Table 10: Ablation study on loss weighting coefficient.

Figure 6: Qualitative results on comparison between error map with semantic shift region.

Figure 7: Qualitative results on [A] $\rightarrow$ [C], where improvements are marked with boxes.

Figure 8: Agreement with EMA teacher within $\mathcal{M}_{\mathrm{SSR}\_\mathrm{eval}}$ .

Figure 9: Example scenes showing “high-distortion” subregions.

Method	Car	Road	Sidewalk	Vegetation	Terrain	Overall mIoU
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

Appendix

Table 11: Performance within high-distortion subregions.

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

Appendix

Table 12: Training-time overhead comparison under identical hardware and settings.

Setting	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [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

Appendix

Table 13: Comparison of class-wise K-prototype versus VQ-VAE prior under identical settings.

Method	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]
Baseline	31.4	15.5
LiDARWeather*	37.8	20.4
A3Point (ours)	40.5	25.9

Appendix

Table 14: 15-epoch (LiDARWeather-style) training. * denotes the reproduced result.

Method	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]
Baseline*	32.3	16.1
LiDARWeather*	38.6	21.6
A3Point (ours)	41.3	27.2

Appendix

Table 15: 50-epoch training. * denotes the reproduced result.

Figure 10: Confusion matrices on SemanticSTF. Left: Oracle. Right: A3Point.

Strategy	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]
None	38.7	24.9
Offline	40.7	26.4
Offline (ensemble)	41.0	26.9
Online (ours)	41.3	27.2

Appendix

Table 16: Comparison of offline and online confusion priors. Online (ours) consistently yields the best mIoU by maintaining alignment with the evolving decision boundary.

Run	car	bi.cle	mt.cle	truck	oth-v.	pers.	bi.clst	mt.clst	road	parki.	sidew.	other-g.	build.	fence	veget.	trunk	terr.	pole	traf.	mIoU
1	89.4	42.1	0.0	59.9	61.2	69.6	39.0	0.0	82.2	21.5	58.2	45.6	86.1	63.6	80.2	52.0	77.6	50.1	61.7	54.7
2	89.6	37.4	9.6	56.4	64.3	67.3	44.8	0.8	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	66.1	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
4	89.6	35.3	0.2	60.8	64.3	67.4	44.4	5.4	81.3	27.4	54.6	50.2	87.2	66.3	77.8	61.1	74.0	51.8	59.5	55.7
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

Appendix

Table 17: Oracle on [C]: per-class IoU across five independent runs. Rare classes (motorcycle, motorcyclist) exhibit high variance; 0.0 IoU appears in some runs but not others.

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

Appendix

Table 18: Stress test with enlarged augmentation ranges on [A]

\rightarrow

[C]. Random: sampled within light–heavy; Random*: sampled within none–excessive.

Strategy	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]
None	38.7	24.9
A3Point	41.3	27.2
A3Point+ (curriculum)	41.5	27.8

Appendix

Table 19: Iterative, curriculum-style A3Point that expands the augmentation ceiling in stages (light

\rightarrow

moderate

\rightarrow

heavy).

Method	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]
Baseline	31.4	15.5
Baseline + EAS	38.7	24.9
PointDR*	33.9	19.8
PointDR* + EAS	39.1	25.1
LiDARWeather*	37.8	20.4
LiDARWeather* + EAS	39.6	25.9

Appendix

Table 20: Re-running prior methods under the same EAS upper bounds. * denotes the reproduced result.

Figure 11: t-SNE of the learned VQ-VAE codebook. Colors denote class-specific sub-codebooks; marker size encodes per-code variance.

Method	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [C]
Baseline	31.4	15.5
EAS	38.7	24.9
EAS + MT	39.0	25.5
EAS + SCE	38.8	24.6
A3Point (ours)	41.3	27.2

Appendix

Table 21: Comparisons with other DG methods under the same augmentation space.

Variant	[A] $\rightarrow$ [C]	[B] $\rightarrow$ [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

Appendix

Table 22: Controlled comparisons of SSR supervision choices under the same augmentation space. Global nearest code (class-agnostic latent alignment) performs best.

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Annotating large-scale LiDAR point clouds for 3D semantic segmentation is costly and time-consuming, which motivates the use of semi-supervised learning (SemiSL). Standard LiDAR SemiSL methods typically adopt a two-step training paradigm, where pseudo-labels are separately generated from a single distillation source, either from the same or another LiDAR representation. Such supervision relies on a unique source of pseudo-labels, which can reinforce confirmation bias and propagate errors during training, ultimately limiting performance. To address this challenge, we introduce CoLLiS, a novel framework that leverages Collaborative Learning for LiDAR Semi-supervised segmentation. Unlike prior paradigms with decoupled pseudo-labeling and training phases, CoLLiS trains multiple representations collaboratively in a single step by treating them as coequal students. Each student is adaptively distilled from multiple representations, while inter-student disparities are monitored online to resolve contradictory supervision and effectively mitigate confirmation bias. Extensive experiments on three datasets demonstrate that CoLLiS consistently outperforms state-of-the-art LiDAR SemiSL methods, with particularly strong gains in low-label regimes.

Abstract

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