针对恶劣天气下激光雷达分割性能下降问题，提出自适应增强感知的鲁棒分割方法。
Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation
- 根据增强强度自适应调整学习策略，分离语义混淆与语义偏移
- 在多个基准测试中达到新最优，显著提升恶劣天气下的分割精度
- 适合需要应对复杂天气的自动驾驶与机器人环境感知场景
恶劣天气会显著降低激光雷达点云语义分割网络的性能，引发较大的分布偏移。现有基于增强的方法虽在训练中模拟天气干扰以提升鲁棒性，但受限于轻微与剧烈增强之间的权衡，难以充分发挥增强潜力。为此，我们提出A3Point——一种自适应增强感知的潜在学习框架，有效利用多样增强的同时缓解语义偏移（即增强引起的语义含义变化）。A3Point包含两个关键组件：语义混淆先验（SCP）潜在学习，用于捕捉模型固有的语义混淆信息；语义偏移区域（SSR）定位，将语义混淆与语义偏移解耦，实现对不同干扰程度的自适应优化。在多个标准广义激光雷达分割基准上进行的大量实验表明，该方法在恶劣天气条件下表现优异，刷新了当前最优结果。
原文摘要 · Abstract (English)
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.
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