提升单传感器训练模型在多激光雷达环境下的泛化能力
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets
- 提出特征级不变性度量，无需标注数据即可评估跨域泛化
- 设计两种特定数据增强方法，显著提升模型在多雷达场景的适应性
- 适用于自动驾驶中从单雷达到多雷达系统的平滑迁移
近年来，基于深度神经网络的激光雷达分割方法在nuScenes和SemanticKITTI等经典基准上性能迅速提升。然而，在将单传感器训练的模型部署到配备多个高分辨率激光雷达的现代车辆时，仍存在显著性能差距。本文提出一种新的特征级不变性度量，可作为无标签数据下跨域泛化的代理指标。同时，我们设计了两种面向应用场景的数据增强方法，当在单传感器数据集上训练时，能有效提升模型向多传感器激光雷达配置的迁移能力。我们在模拟和真实数据上提供了实验证据，表明所提增强方法能增强不同激光雷达设置间的特征不变性，从而实现更好的泛化性能。
原文摘要 · Abstract (English)
Recently, LiDAR segmentation methods for autonomous vehicles, powered by deep neural networks, have experienced steep growth in performance on classic benchmarks, such as nuScenes and SemanticKITTI. However, there are still large gaps in performance when deploying models trained on such single-sensor setups to modern vehicles with multiple high-resolution LiDAR sensors. In this work, we introduce a new metric for feature-level invariance which can serve as a proxy to measure cross-domain generalization without requiring labeled data. Additionally, we propose two application-specific data augmentations, which facilitate better transfer to multi-sensor LiDAR setups, when trained on single-sensor datasets. We provide experimental evidence on both simulated and real data, that our proposed augmentations improve invariance across LiDAR setups, leading to improved generalization.
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