In the realm of LiDAR-based perception, significant strides have been made, yet domain generalization remains a substantial challenge. The performance often deteriorates when models are applied to unfamiliar datasets with different LiDAR sensors or deployed in new environments, primarily due to variations in point cloud density distributions. To tackle this challenge, we propose a Density Discriminative Feature Embedding (DDFE) module, capitalizing on the observation that a single source LiDAR point cloud encompasses a spectrum of densities. The DDFE module is meticulously designed to extract density-specific features within a single source domain, facilitating the recognition of objects sharing similar density characteristics across different LiDAR sensors. In addition, we introduce a simple yet effective density augmentation technique aimed at expanding the spectrum of density in source data, thereby enhancing the capabilities of the DDFE. Our DDFE stands out as a versatile and lightweight domain generalization module. It can be seamlessly integrated into various 3D backbone networks, where it has demonstrated superior performance over current state-of-the-art domain generalization methods. Code is available at https://github.com/dgist-cvlab/MultiDensityDG.

翻译：在基于激光雷达的感知领域，尽管已取得显著进展，但领域泛化仍是一个重大挑战。当模型应用于具有不同激光雷达传感器的不熟悉数据集或部署在新环境中时，其性能通常会下降，这主要归因于点云密度分布的变化。为应对这一挑战，我们提出了一个密度判别特征嵌入模块，该模块基于以下观察：单一源激光雷达点云包含一系列密度变化。DDFE模块经过精心设计，可在单一源域内提取密度特异性特征，从而促进识别不同激光雷达传感器之间具有相似密度特性的物体。此外，我们引入了一种简单而有效的密度增强技术，旨在扩展源数据中的密度谱，从而增强DDFE的能力。我们的DDFE作为一个多功能且轻量级的领域泛化模块脱颖而出。它可以无缝集成到各种3D骨干网络中，在这些网络中，它已展现出优于当前最先进领域泛化方法的性能。代码可在 https://github.com/dgist-cvlab/MultiDensityDG 获取。