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

翻译：车辆与万物（V2X）协同感知技术因其能够整合车辆、路侧设施等多智能体信息以增强场景理解能力而受到广泛关注。然而现有研究通常平等对待各智能体信息，忽视了因不同智能体采用不同LiDAR传感器所导致的固有域差异，从而限制了系统性能。本文提出DI-V2X框架，通过新型蒸馏架构学习域不变表征以缓解V2X三维目标检测中的域差异问题。DI-V2X包含三个核心模块：域混合实例增强模块（DMA）、渐进式域不变蒸馏模块（PDD）和域自适应融合模块（DAF）。具体而言，DMA在训练过程中为教师模型与学生模型构建域混合三维实例库，实现数据表征对齐；PDD通过将智能体重叠区域作为蒸馏引导，促使不同域的学生模型逐步向教师模型学习域不变特征表征；DAF通过引入标定感知的域自适应注意力机制弥合学生模型间的域差异。在DAIR-V2X与V2XSet两大基准数据集上的大量实验表明，DI-V2X取得了卓越性能，全面超越现有V2X模型。代码已开源：https://github.com/Serenos/DI-V2X