TransIFF: An Instance-Level Feature Fusion Framework for Vehicle-Infrastructure Cooperative 3D Detection with Transformers

Ziming Chen, Yifeng Shi, Jinrang Jia; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 18205-18214

Cooperation between vehicles and infrastructure is vital to enhancing the safety of autonomous driving. Two significant and contradictory challenges now stand in the collaborative perception: fusion accuracy and communication bandwidth. Previous intermediate fusion methods that transmit features balance the accuracy and bandwidth compared with early fusion and late fusion, but usually have problems with feature alignment and domain gaps, and the bandwidth usage still falls short of the industrial application standard to our best knowledge. In this paper, we propose TransIFF, an instance-level feature fusion framework with transformers that can effectively reduce bandwidth usage. Furthermore, it can align the domain gaps between vehicle and infrastructure features, and improve the robustness of feature fusion, leading to a high cooperative perception accuracy. TransIFF is composed of three components: a vehicle-side network, an infrastructure-side network, and a vehicle-infrastructure fusion network. Initially, the vehicle-side and infrastructure-side networks independently generate instance-level features. Subsequently, the infrastructure-side instance-level features are transmitted to the vehicles, significantly reducing the communication bandwidth usage. Finally, in the vehicle-infrastructure fusion network, Cross-Domain Adaptation (CDA) module is designed to align the feature domains, followed by Feature Magnet (FM) module which can adaptively fuse the instance features and achieve a robust feature fusion. TransIFF yields state-of-the-art performance on the widely used real-world vehicle-infrastructure cooperative benchmark DAIR-V2X, achieving 59.62% AP with only 2^12 bytes bandwidth consumption.