  Detecting objects in 3D space using multiple cameras, known as Multi-Camera
3D Object Detection (MC3D-Det), has gained prominence with the advent of
bird's-eye view (BEV) approaches. However, these methods often struggle when
faced with unfamiliar testing environments due to the lack of diverse training
data encompassing various viewpoints and environments. To address this, we
propose a novel method that aligns 3D detection with 2D camera plane results,
ensuring consistent and accurate detections. Our framework, anchored in
perspective debiasing, helps the learning of features resilient to domain
shifts. In our approach, we render diverse view maps from BEV features and
rectify the perspective bias of these maps, leveraging implicit foreground
volumes to bridge the camera and BEV planes. This two-step process promotes the
learning of perspective- and context-independent features, crucial for accurate
object detection across varying viewpoints, camera parameters, and
environmental conditions. Notably, our model-agnostic approach preserves the
original network structure without incurring additional inference costs,
facilitating seamless integration across various models and simplifying
deployment. Furthermore, we also show our approach achieves satisfactory
results in real data when trained only with virtual datasets, eliminating the
need for real scene annotations. Experimental results on both Domain
Generalization (DG) and Unsupervised Domain Adaptation (UDA) clearly
demonstrate its effectiveness. The codes are available at
https://github.com/EnVision-Research/Generalizable-BEV.
