Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather (2407.02286v4)

Abstract: Existing LiDAR semantic segmentation methods often struggle with performance declines in adverse weather conditions. Previous work has addressed this issue by simulating adverse weather or employing universal data augmentation during training. However, these methods lack a detailed analysis and understanding of how adverse weather negatively affects LiDAR semantic segmentation performance. Motivated by this issue, we identified key factors of adverse weather and conducted a toy experiment to pinpoint the main causes of performance degradation: (1) Geometric perturbation due to refraction caused by fog or droplets in the air and (2) Point drop due to energy absorption and occlusions. Based on these findings, we propose new strategic data augmentation techniques. First, we introduced a Selective Jittering (SJ) that jitters points in the random range of depth (or angle) to mimic geometric perturbation. Additionally, we developed a Learnable Point Drop (LPD) to learn vulnerable erase patterns with a Deep Q-Learning Network to approximate the point drop phenomenon from adverse weather conditions. Without precise weather simulation, these techniques strengthen the LiDAR semantic segmentation model by exposing it to vulnerable conditions identified by our data-centric analysis. Experimental results confirmed the suitability of the proposed data augmentation methods for enhancing robustness against adverse weather conditions. Our method achieves a notable 39.5 mIoU on the SemanticKITTI-to-SemanticSTF benchmark, improving the baseline by 8.1\%p and establishing a new state-of-the-art. Our code will be released at \url{https://github.com/engineerJPark/LiDARWeather}.

• The paper introduces targeted data augmentation methods to simulate weather-induced distortions in LiDAR data.
• It implements Selective Jittering and a Learnable Point Drop approach to counteract geometric perturbations and point drop effects.
• Experimental results report a significant mean IoU improvement of 39.5 and enhanced detection of critical objects like cars and pedestrians.

An Insightful Analysis of "Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather"

This scholarly discourse examines the paper "Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather" by Junsung Park, Kyungmin Kim, and Hyunjung Shim. The paper addresses the critical challenge of enhancing LiDAR-based semantic segmentation under adverse weather conditions, a domain of significant relevance in applications such as autonomous driving.

LiDAR semantic segmentation's robustness is notably compromised in conditions such as fog, rain, and snow, fundamentally impacting safety-critical tasks. Traditional approaches, including weather simulations for training datasets or universal data augmentations, fail to adequately address the specific intricacies and complexities introduced by adverse weather disturbances on LiDAR data.

The paper hypothesizes that key factors behind performance degradation in such conditions are (1) geometric perturbations due to environmental interferences like air humidity and rain droplets, and (2) point drop from energy absorption and occlusions. These two factors lead to inaccuracies in scene interpretation, which the authors thoroughly explore through a carefully designed toy experiment. This experimental setup confirmed the detrimental influence of aforementioned weather-induced distortions, providing a solid foundation for their counteractive strategies.

Subsequently, the authors introduce targeted data augmentation techniques aimed at imitating these specific perturbations. They propose "Selective Jittering" (SJ) to simulate geometric disturbances within a controlled depth or angular range, and a "Learnable Point Drop" (LPD) approach using Deep Q-learning to dynamically model and counteract the point drop phenomenon. This innovative data-centric method enables robust model training across varying adverse weather conditions without relying on precise physical simulations, which can be computationally onerous and imprecise.

Experimental validation reported a substantial increase in robustness, with a remarkable mean IoU improvement of 39.5 on the SemanticKITTI-to-SemanticSTF benchmark, a significant 5.4 percentage points above the previous best. The results are compelling, showing enhanced performance not only across overall metrics but also within critical object classes such as cars and pedestrians, highlighting the practical utility of these augmentation techniques.

Future work could focus on integrating these data augmentation strategies with adaptive neural network architectures or unsupervised domain adaptation techniques, furthering advancement in this space. Additionally, investigating the combination of these augmentations with novel sensor fusion methodologies could offer an avenue for algorithmic improvement to accommodate real-world ecological complexities more comprehensively.

In conclusion, this paper makes a noteworthy contribution to the field of LiDAR semantic segmentation, particularly in its methodical dissection of environmental challenges and its pragmatic solutions to enhance system resilience. The augmentation strategies proposed stand as essential tools for building more reliable autonomous systems capable of navigating under adverse weather conditions.