  3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a
prevalent approach in the field of autonomous driving. Despite the demonstrated
improvements in accuracy and velocity estimation compared to perspective view
methods, the deployment of BEV-based techniques in real-world autonomous
vehicles remains challenging. This is primarily due to their reliance on
vision-transformer (ViT) based architectures, which introduce quadratic
complexity with respect to the input resolution. To address this issue, we
propose an efficient BEV-based 3D detection framework called BEVENet, which
leverages a convolutional-only architectural design to circumvent the
limitations of ViT models while maintaining the effectiveness of BEV-based
methods. Our experiments show that BEVENet is 3$\times$ faster than
contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge,
achieving a mean average precision (mAP) of 0.456 and a nuScenes detection
score (NDS) of 0.555 on the NuScenes validation dataset, with an inference
speed of 47.6 frames per second. To the best of our knowledge, this study
stands as the first to achieve such significant efficiency improvements for
BEV-based methods, highlighting their enhanced feasibility for real-world
autonomous driving applications.
