Rethinking Range-View LiDAR Segmentation in Adverse Weather (2506.08979v1)

Abstract: LiDAR segmentation has emerged as an important task to enrich multimedia experiences and analysis. Range-view-based methods have gained popularity due to their high computational efficiency and compatibility with real-time deployment. However, their generalized performance under adverse weather conditions remains underexplored, limiting their reliability in real-world environments. In this work, we identify and analyze the unique challenges that affect the generalization of range-view LiDAR segmentation in severe weather. To address these challenges, we propose a modular and lightweight framework that enhances robustness without altering the core architecture of existing models. Our method reformulates the initial stem block of standard range-view networks into two branches to process geometric attributes and reflectance intensity separately. Specifically, a Geometric Abnormality Suppression (GAS) module reduces the influence of weather-induced spatial noise, and a Reflectance Distortion Calibration (RDC) module corrects reflectance distortions through memory-guided adaptive instance normalization. The processed features are then fused and passed to the original segmentation pipeline. Extensive experiments on different benchmarks and baseline models demonstrate that our approach significantly improves generalization to adverse weather with minimal inference overhead, offering a practical and effective solution for real-world LiDAR segmentation.

• The paper introduces a modular framework that suppresses geometric noise and corrects reflectance distortions in LiDAR segmentation.
• It significantly improves mIoU performance in adverse weather, demonstrated by a jump from 14.6 to 60.6 on baseline models.
• The approach retains real-time efficiency while enhancing segmentation robustness for autonomous vehicle LiDAR systems.

Rethinking Range-View LiDAR Segmentation in Adverse Weather

The paper "Rethinking Range-View LiDAR Segmentation in Adverse Weather" explores the significant challenges faced by LiDAR segmentation systems when deployed under adverse weather conditions. The research underscores the practical necessity of robust LiDAR segmentation, especially concerning range-view methods known for their computational efficiency and real-time applicability.

Summary of Core Contributions

1. Challenge Identification: The paper identifies and elaborates on the limitations of existing range-view-based LiDAR segmentation models when confronted with adverse weather phenomena such as rain, snow, and fog. These conditions degrade the spatial fidelity of LiDAR point clouds and introduce reflectance distortions that traditional methods struggle to manage.
2. Proposed Framework: To address these challenges, the authors introduce a modular framework designed to enhance existing range-view models' robustness without altering their core architectural design. This framework consists of two key modules:
   • Geometric Abnormality Suppression (GAS): Aims to mitigate the impact of spatial noise induced by adverse weather. It dynamically identifies and suppresses points likely distorted, thus preventing them from degrading overall segmentation accuracy.
   • Reflectance Distortion Calibration (RDC): Corrects reflectance distortion through memory-guided adaptive instance normalization. It relies on learned reflectance patterns to recalibrate distorted intensities, thereby preserving meaningful feature extraction.
3. Experimental Validation: Extensive experiments demonstrate that integrating the proposed framework into baseline models leads to significant improvements in generalization capabilities under various weather conditions compared to existing methods. Models equipped with this framework maintain almost the same efficiency levels concerning inference time while achieving higher accuracy across diverse weather scenarios.

Numerical Performance Insights

The experiments clearly present numerical performance enhancements across multiple benchmarks. The proposed approach offers substantial improvements in mean Intersection over Union (mIoU) across several categories, notably bridges in adverse weather against baseline architectures such as SalsaNext, RangeNet++, and newer transformer-based methods like RangeViT. For instance, integrating the framework into SalsaNext produced improvements from 14.6 mIoU to 60.6 mIoU in specific conditions, illustrating its efficacy without sacrificing computational efficiency.

Theoretical and Practical Implications

The research carried out in this paper provides notable theoretical advancements by demonstrating effective techniques to suppress noise and calibrate reflectance distortions systematically. Practically, these findings can be integrated into current LiDAR systems used in autonomous vehicles, wherein reliable semantic segmentation is crucial for safety and operational efficiency.

Future Directions and Developments

Speculating on future developments, the modular nature of the proposed framework allows it to be easily adapted to other LiDAR segmentation paradigms beyond range-view methods, potentially applying to voxel-based approaches where computational cost becomes prohibitive in real-time scenarios. Moreover, future research could explore finer granularity in anomaly detection and normalization techniques, potentially employing deep learning systems to predict environmental adjustment needs dynamically.

In summary, the paper presents a comprehensive strategy that significantly enhances LiDAR segmentation, paving the way for more resilient and efficient deployment of these systems in real-world autonomous navigation applications, even in challenging weather conditions.