Domain Generalization in LiDAR Semantic Segmentation Leveraged by
  Density Discriminative Feature Embedding
Jaeyeul Kim Jungwan Woo Jeonghoon Kim Sunghoon Im
While significant progress has been achieved in LiDAR-based perception,
domain generalization continues to present challenges, often resulting in
reduced performance when encountering unfamiliar datasets due to domain
discrepancies. One of the primary hurdles stems from the variability of LiDAR
sensors, leading to inconsistencies in point cloud density distribution. Such
inconsistencies can undermine the effectiveness of perception models. We
address this challenge by introducing a new approach that acknowledges a
fundamental characteristic of LiDAR: the variation in point density due to the
distance from the LiDAR to the scene, and the number of beams relative to the
field of view. Understanding this, we view each LiDAR's point cloud at various
distances as having distinct density distributions, which can be consistent
across different LiDAR models. With this insight, we propose the Density
Discriminative Feature Embedding (DDFE) module, crafted to specifically extract
features related to density while ensuring domain invariance across different
LiDAR sensors. In addition, we introduce a straightforward but effective
density augmentation technique, designed to broaden the density spectrum and
enhance the capabilities of the DDFE. The proposed DDFE stands out as a
versatile and lightweight domain generalization module. It can be seamlessly
integrated into various 3D backbone networks, consistently outperforming
existing state-of-the-art domain generalization approaches. We commit to
releasing the source code publicly to foster community collaboration and
advancement.