Multiview Bird’s-Eye View Fusion for Enhanced Three-Dimensional Object Detection in Vehicle–Infrastructure Cooperative Systems

Engineering notes

Extensive experiments on the dataset for infrastructure–vehicle cooperative perception via vehicle-to-everything communication (DAIR-V2X) benchmark demonstrate that V2IFormer achieves state-of-the-art performance, with a mean average precision of 55.64 for 3D detection and 59.63 for BEV detection, significantly outperforming existing fusion methods.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Three-dimensional (3D) object detection plays a critical role in connected and autonomous vehicles (CAVs) because it enables precise localization and classification of surrounding obstacles by estimating their spatial properties from two-dimensional (2D) images. This capability effectively addresses the limitation of traditional 2D detectors, which lack depth information. However, image-based 3D detection remains challenging in complex scenarios, particularly under occlusion or limited fields of view, such as at intersections or on curved roads. To address these issues, vehicle-to-infrastructure (V2I) cooperative perception frameworks leverage vehicle-to-everything (V2X) communication to transmit sensor data from roadside units to CAVs, enabling multiview feature fusion from both vehicle-mounted and infrastructure-side cameras. In this paper, a V2IFormer, a novel V2I cooperative 3D object detection framework based on bird’s-eye view (BEV) representations is proposed. Instead of transmitting raw images or high-level detection outputs, the V2IFormer transmits BEV features to reduce bandwidth while preserving essential spatial information. On the infrastructure side, a HeightNet module with a linearly-increasing discretization strategy is introduced to predict adaptive height distributions, improving long-range depth perception. On the vehicle side, multiview image features are lifted into the BEV space using depth distribution prediction, enhancing depth accuracy in close-range regions. A deformable mutual-attention module is further used to adaptively fuse BEV features from both sides, selectively focusing on informative regions while suppressing irrelevant content. Extensive experiments on the dataset for infrastructure–vehicle cooperative perception via vehicle-to-everything communication (DAIR-V2X) benchmark demonstrate that V2IFormer achieves state-of-the-art performance, with a mean average precision of 55.64 for 3D detection and 59.63 for BEV detection, significantly outperforming existing fusion methods.

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