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.
Abstract (translated)
恶劣的天气条件会显著降低激光雷达点云语义分割网络的表现，通过引入大的分布偏移。现有的基于数据增强的方法试图通过在训练过程中模拟天气干扰来提高模型的鲁棒性。然而，由于轻微和激进的数据增强之间的权衡，它们难以充分利用增强技术的潜力。为了解决这个问题，我们提出了A3Point，这是一种自适应增广感知隐式学习框架，能够有效利用多样化的增强方法的同时减少由这些增强引起的语义偏移（即数据增强造成的语义含义变化）。A3Point主要包含两个关键组成部分：语义混淆先验（SCP）隐式学习，用于捕捉模型内在的语义模糊信息；以及语义偏移区域（SSR）定位，它将语义混淆与语义偏移解耦，从而能够根据不同干扰程度实施自适应优化策略。在多个标准广义激光雷达分割基准测试中的恶劣天气条件下进行的广泛实验展示了我们方法的有效性，并且我们的方法为这一任务设定了新的最先进水平（SOTA）结果。
URL
https://arxiv.org/abs/2603.01074
