  Vehicle-to-Everything (V2X) collaborative perception has recently gained
significant attention due to its capability to enhance scene understanding by
integrating information from various agents, e.g., vehicles, and
infrastructure. However, current works often treat the information from each
agent equally, ignoring the inherent domain gap caused by the utilization of
different LiDAR sensors of each agent, thus leading to suboptimal performance.
In this paper, we propose DI-V2X, that aims to learn Domain-Invariant
representations through a new distillation framework to mitigate the domain
discrepancy in the context of V2X 3D object detection. DI-V2X comprises three
essential components: a domain-mixing instance augmentation (DMA) module, a
progressive domain-invariant distillation (PDD) module, and a domain-adaptive
fusion (DAF) module. Specifically, DMA builds a domain-mixing 3D instance bank
for the teacher and student models during training, resulting in aligned data
representation. Next, PDD encourages the student models from different domains
to gradually learn a domain-invariant feature representation towards the
teacher, where the overlapping regions between agents are employed as guidance
to facilitate the distillation process. Furthermore, DAF closes the domain gap
between the students by incorporating calibration-aware domain-adaptive
attention. Extensive experiments on the challenging DAIR-V2X and V2XSet
benchmark datasets demonstrate DI-V2X achieves remarkable performance,
outperforming all the previous V2X models. Code is available at
https://github.com/Serenos/DI-V2X
